A Lesson in Scaling 6LoWPAN -- Minimal Fragment Forwarding in Lossy Networks

This paper evaluates two forwarding strategies for fragmented datagrams in the IoT: hop-wise reassembly and a minimal approach to directly forward fragments. Minimal fragment forwarding is challenged by the lack of forwarding information at subsequent fragments in 6LoWPAN and thus requires additional data at nodes. We compared the two approaches in extensive experiments evaluating reliability, end-to-end latency, and memory consumption. In contrast to previous work and due to our alternate setup, we obtained different results and conclusions. Our findings indicate that direct fragment forwarding should be deployed only with care, since higher packet transmission rates on the link-layer can significantly reduce its reliability, which in turn can even further reduce end-to-end latency because of highly increased link-layer retransmissions.Comment: If you cite this paper, please use the LCN reference: M. S. Lenders, T. C. Schmidt, M. W\"ahlisch. "A Lesson in Scaling 6LoWPAN - Minimal Fragment Forwarding in Lossy Networks." in Proc. of IEEE LCN, 201

The origin ofhigh hydraulic resistance for filter cakes ofdef ormable particles: cell-bed deformation or surface-layer effect?

This study reports a numerical approach for modeling the hydraulic resistance ofa filter cake ofdef ormable cells. First, a mechanical and osmotic model that describes the volume fraction ofsolids in a bed ofyeast cells as a function ofthe compressive pressure it experiences is presented. The effects ofpressure on the compressibility ofyeast cells beds were further investigated both by filtration experiments and by centrifugal experiments based on the multiple speed equilibrium sediment height technique. When comparing the latter measurements with compression model calculations, we observed that the method based on centrifugal experiments suffers from rapid relaxation of the compressed bed. Concerning the filtration experiments, specific resistance ofwell-defined bed ofcells were calculated by a combination of the compression model with a formulation for hydraulic resistivity developed using the Lattice Boltzmann method. We further explain the experimental values observed for the hydraulic resistance ofcell beds, assuming that the first layer ofcells in contact with the membrane partially blocks the membrane area open to flow. In such a case, the blocked area seems to be a constant fraction of the normal cell–cell contact area

Role of Macrophage Migration Inhibitory Factor in Obesity, Insulin Resistance, Type 2 Diabetes, and Associated Hepatic Co-Morbidities: A Comprehensive Review of Human and Rodent Studies

<p>Obesity is associated with a chronic low-grade inflammatory state that drives the -development of obesity-related co-morbidities such as insulin resistance/type 2 diabetes, non-alcoholic fatty liver disease (NAFLD), and cardiovascular disease. This metabolic inflammation is thought to originate in the adipose tissue, which becomes inflamed and insulin resistant when it is no longer able to expand in response to excess caloric and nutrient intake. The production of inflammatory mediators by dysfunctional adipose tissue is thought to drive the development of more complex forms of disease such as type 2 diabetes and NAFLD. An important factor that may contribute to metabolic inflammation is the cytokine macrophage migration inhibitory factor (MIF). Increasing evidence suggests that MIF is released by adipose tissue in obesity and that it is also involved in metabolic and inflammatory processes that underlie the development of obesity-related pathologies. This review provides a comprehensive summary of our current knowledge on the role of MIF in obesity, its production by adipose tissue, and its involvement in the development of insulin resistance, type 2 diabetes, and NAFLD. We discuss the main findings from recent clinical studies in obese subjects and weight-loss intervention studies as well as results from clinical studies in patients with insulin resistance and type 2 diabetes. Furthermore, we summarize findings from experimental disease models studying the contribution of MIF in obesity and insulin resistance, type 2 diabetes, and hepatic lipid accumulation and fibrosis. Although many of the findings support a pro-inflammatory role of MIF in disease development, recent reports also provide indications that MIF may exert protective effects under certain conditions.</p

Contact sensors and methods for making same

The present invention is directed to novel contact sensors. The contact sensors of the invention include a conductive composite material formed of a polymer and a conductive filler. In one particular embodiment, the composite materials can include less than about 10 wt % conductive filler. Thus, the composite material of the contact sensors can have physical characteristics essentially identical to the polymer, while being electrically conductive with the electrical resistance proportional to the load on the sensor. If desired, the sensors can be formed of the same polymeric material as the bearing that is being examined. The sensors can provide real time dynamic contact information for joint members under conditions expected during use. In one particular embodiment, the sensors can be used to examine dynamic wear characteristics of artificial joint bearings such as artificial knee, hip, or shoulder bearings

Schistosoma haematobium infection and Buruli ulcer

No abstract available

Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features