Purpose – Monitoring the real-time temperature, humidity, and physical position status of goods is vital in the cold chain. Diverse logistics technologies and systems have been adopted in the cold chain for monitoring perishable goods. However, these technologies and systems are independent from each other. Data and information in them are not integrated so that information control is not effective. The paper aims to discuss these issues. Design/methodology/approach – By integrating Internet of Things and tracking technologies, this paper proposes an intelligent tracking system, which is designed to achieve effective and fast live monitoring of goods in the cold chain at the lowest cost and with the largest network capacity and simplest protocols. Findings – Structure and information platform design mechanism are introduced. The key part of this system is a wireless sensor network built on Zigbee. Wireless sensors located in cold storages or refrigerated trucks are able to collect and transmit live data quickly and efficiently. Originality/value – Users of the proposed system can easily monitor goods transported in cold chains. In addition, the system assigns specific servers to save historical data for inquiries.

Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

By dragging a phospholipid solution on microstructured silicon surfaces, phospholipid molecules are selectively deposited inside the microstructures to get regular phospholipid multilayer patterns of controlled thickness over a large scale ( approximately cm(2)). By varying the dragging speed, the thickness of the patterns varies between 28 and 100 nm on average (7 to 25 bilayers). Electroswelling of phospholipid multilayer patterns leads to the formation of giant liposomes of controlled size and narrow size distributions.

No abstract