Die Attraktivität des Logistikstandortes Shanghai in China im internationalen Vergleich

Das Ziel der vorliegenden Arbeit besteht in der Betrachtung der Attraktivität der Industriemetropole Shanghai als globaler Logistikstandort im internationalen Vergleich. Dazu wird über eine einführende Klärung relevanter Begrifflichkeiten der Bogen geschlagen bis zur Analyse von Ressourcen und Defiziten einschließlich der Erwähnung von geplanten oder begonnenen Maßnahmen zu deren Behebung, bis hin zu Empfehlungen für die bessere Etablierung der Stadt als Logistikstandort für aus- und inländische Investoren

An adaptive ensemble approach to ambient intelligence assisted people search

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks

MCP-induced protein 1 deubiquitinates TRAF proteins and negatively regulates JNK and NF-kappa B signaling

The intensity and duration of macrophage-mediated inflammatory responses are controlled by proteins that modulate inflammatory signaling pathways. MCPIP1 (monocyte chemotactic protein-induced protein 1), a recently identified CCCH Zn finger-containing protein, plays an essential role in controlling macrophage-mediated inflammatory responses. However, its mechanism of action is poorly understood. In this study, we show that MCPIP1 negatively regulates c-Jun N-terminal kinase (JNK) and NF-kappa B activity by removing ubiquitin moieties from proteins, including TRAF2, TRAF3, and TRAF6. MCPIP1-deficient mice spontaneously developed fatal inflammatory syndrome. Macrophages and splenocytes from MCPIP1(-/-) mice showed elevated expression of inflammatory gene expression, increased JNK and I. B kinase activation, and increased polyubiquitination of TNF receptor-associated factors. In vitro assays directly demonstrated the deubiquitinating activity of purified MCPIP1. Sequence analysis together with serial mutagenesis defined a deubiquitinating enzyme domain and a ubiquitin association domain in MCPIP1. Our results indicate that MCPIP1 is a critical modulator of inflammatory signaling

Evidence for Majorana bound state in an iron-based superconductor

The search for Majorana bound state (MBS) has recently emerged as one of the most active research areas in condensed matter physics, fueled by the prospect of using its non-Abelian statistics for robust quantum computation. A highly sought-after platform for MBS is two-dimensional topological superconductors, where MBS is predicted to exist as a zero-energy mode in the core of a vortex. A clear observation of MBS, however, is often hindered by the presence of additional low-lying bound states inside the vortex core. By using scanning tunneling microscope on the newly discovered superconducting Dirac surface state of iron-based superconductor FeTe1-xSex (x = 0.45, superconducting transition temperature Tc = 14.5 K), we clearly observe a sharp and non-split zero-bias peak inside a vortex core. Systematic studies of its evolution under different magnetic fields, temperatures, and tunneling barriers strongly suggest that this is the case of tunneling to a nearly pure MBS, separated from non-topological bound states which is moved away from the zero energy due to the high ratio between the superconducting gap and the Fermi energy in this material. This observation offers a new, robust platform for realizing and manipulating MBSs at a relatively high temperature.Comment: 27 pages, 11 figures, supplementary information include

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature

Superfast and controllable microfluidic inking of anti-inflammatory melanin-like nanoparticles inspired by cephalopods

Here, a microfluidic approach for superfast melanin-like nanoparticle preparation with tunable size and monodispersity is reported. The particles formed have similar chemical composition to those prepared by the bulk method, and show reactive oxygen species scavenging behaviour and inflammatory macrophage phenotype switching capability, suggesting their potential for anti-inflammatory applications.Peer reviewe

Surface Adsorption-Mediated Ultrahigh Efficient Peptide Encapsulation with a Precise Ratiometric Control for Type 1 and 2 Diabetic Therapy

A surface adsorption strategy is developed to enable the engineering of microcomposites featured with ultrahigh loading capacity and precise ratiometric control of co-encapsulated peptides. In this strategy, peptide molecules (insulin, exenatide, and bivalirudin) are formulated into nanoparticles and their surface is decorated with carrier polymers. This polymer layer blocks the phase transfer of peptide nanoparticles from oil to water and, consequently, realizes ultrahigh peptide loading degree (up to 78.9%). After surface decoration, all three nanoparticles are expected to exhibit the properties of adsorbed polymer materials, which enables the co-encapsulation of insulin, exenatide, and bivalirudin with a precise ratiometric control. After solidification of this adsorbed polymer layer, the release of peptides is synchronously prolonged. With the help of encapsulation, insulin achieves 8 days of glycemic control in type 1 diabetic rats with one single injection. The co-delivery of insulin and exenatide (1:1) efficiently controls the glycemic level in type 2 diabetic rats for 8 days. Weekly administration of insulin and exenatide co-encapsulated microcomposite effectively reduces the weight gain and glycosylated hemoglobin level in type 2 diabetic rats. The surface adsorption strategy sets a new paradigm to improve the pharmacokinetic and pharmacological performance of peptides, especially for the combination of peptides.Peer reviewe