The Existence of Positive Solutions for Boundary Value Problem of Nonlinear Fractional Differential Equations

We consider the existence of positive solutions for the nonlinear fractional differential equations boundary value problem -D0+αu(t)=f(t,u(t)),   0<t<1,  u(0)=u'(0)=u'(1)=0, where 2<α≤3 is a real number, D0+α is the Riemann-Liouville fractional derivative of order α, and f is a given continuous function. Our analysis relies on the fixed point index theory in cones

A Scale-Free Topology Construction Model for Wireless Sensor Networks

A local-area and energy-efficient (LAEE) evolution model for wireless sensor networks is proposed. The process of topology evolution is divided into two phases. In the first phase, nodes are distributed randomly in a fixed region. In the second phase, according to the spatial structure of wireless sensor networks, topology evolution starts from the sink, grows with an energy-efficient preferential attachment rule in the new node's local-area, and stops until all nodes are connected into network. Both analysis and simulation results show that the degree distribution of LAEE follows the power law. This topology construction model has better tolerance against energy depletion or random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure

Review of Cross-border Electronic Retail Logistics Research in the Last Five Years

Nearly five years, retail in cross-border e-commerce (CBER) has gradually attracted more attention with the expansion of domestic e-commerce in several retail major economies. Scholars both here and abroad have become increasingly interested in CBER. As the important supporting services, logistics services of CBER have also be deeply concerned. However, compared with the rich practice, the theoretical outputs of logistics services in CBER are still less. Previous studies have focused on the relationship between cross-border e-commerce and logistics services , and international distribution network. So far, there is little research on content-based logistics services concerning CBER. Based on the literature review, this paper has determined a series of possible research directions, including strategic significance of cooperation in forming CBER distribution structure and how to implement customer-driven logistics service improvement. Finally, some future research directions are proposed

Hysteresis and Delta Modulation Control of Converters Using Sensorless Current Mode

Sensorless current mode (SCM) is a control formulation for dc-dc converters that results in voltage-source characteristics, excellent open-loop tracking, and near-ideal source rejection. Hysteresis and delta modulation are well-known, easy-to-construct large-signal methods for switched systems. Combining either large-signal method with SCM creates a controller that is simpler and more robust than a pulse-width modulation (PWM) based controller. The small-signal advantages of PWM-based SCM are retained and expanded to include converter response to large-signal disturbances. These approaches can be used with any converter topology over a broad range of operating conditions. In the present work, hysteresis and delta modulation SCM controllers are derived and simulated. Extensive experimental results demonstrate the large-signal behavior of both control schemes

Non-Unity Active PFC Methods for Filter Size Optimization

Active power factor correction seeks to obtain unity power factor and sinusoidal line currents. Optimized nonsinusoidal line currents reduce filter capacitor requirements with a nonunity target power factor. Implementation methods are presented that permit reduced power factor to be traded off against filter size in a nearly optimum manner. A simple waveform shape can reduce filter component size by about 40% in active PFC converters at the same level of complexity as in conventional PFC designs while yielding power factor as high as 0.9. Two approximate methods to generate appropriate shapes are presented. They offer direct practical implementation of nonunity power factor solutions and have been verified experimentally. Such solutions meet power quality standards and deliver acceptable power factor with reduced converter cost

Model Debiasing via Gradient-based Explanation on Representation

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches