Is DNS Ready for Ubiquitous Internet of Things?

The vision of the Internet of Things (IoT) covers not only the well-regulated processes of specific applications in different areas but also includes ubiquitous connectivity of more generic objects (or things and devices) in the physical world and the related information in the virtual world. For example, a typical IoT application, such as a smart city, includes smarter urban transport networks, upgraded water supply, and waste-disposal facilities, along with more efficient ways to light and heat buildings. For smart city applications and others, we require unique naming of every object and a secure, scalable, and efficient name resolution which can provide access to any object\u27s inherent attributes with its name. Based on different motivations, many naming principles and name resolution schemes have been proposed. Some of them are based on the well-known domain name system (DNS), which is the most important infrastructure in the current Internet, while others are based on novel designing principles to evolve the Internet. Although the DNS is evolving in its functionality and performance, it was not originally designed for the IoT applications. Then, a fundamental question that arises is: can current DNS adequately provide the name service support for IoT in the future? To address this question, we analyze the strengths and challenges of DNS when it is used to support ubiquitous IoT. First, we analyze the requirements of the IoT name service by using five characteristics, namely security, mobility, infrastructure independence, localization, and efficiency, which we collectively refer to as SMILE. Then, we discuss the pros and cons of the DNS in satisfying SMILE in the context of the future evolution of the IoT environment

Research on insulation joint damage of the station track circuit in high speed and heavy load condition

In high-speed and heavy-load operation environment, the insulation joints of the station track circuit are damaged in many stations, which caused the carrier information of the adjacent sections to interfere with each other, endangered the safety of the train and reduced the reliability of the track circuit. In order to solve the incorrect code problem caused by the insulation joint breakage, we analysed the insulation damage situation for the station track circuit and the structural principle of BES choke transformer. Then the different structures of the insulation damage protection circuit are given and the relevant parameters are obtained, and verifying the anti-interference ability of the new BES choke transformer. Finally, a complete four-terminal network model of the new BES choke transformer is established. And its four-terminal network parameters are calculated by matlab simulation, which provides the theoretical basis for establishing the track circuit complete system

Research on insulation joint damage of the station track circuit in high speed and heavy load condition

In high-speed and heavy-load operation environment, the insulation joints of the station track circuit are damaged in many stations, which caused the carrier information of the adjacent sections to interfere with each other, endangered the safety of the train and reduced the reliability of the track circuit. In order to solve the incorrect code problem caused by the insulation joint breakage, we analysed the insulation damage situation for the station track circuit and the structural principle of BES choke transformer. Then the different structures of the insulation damage protection circuit are given and the relevant parameters are obtained, and verifying the anti-interference ability of the new BES choke transformer. Finally, a complete four-terminal network model of the new BES choke transformer is established. And its four-terminal network parameters are calculated by matlab simulation, which provides the theoretical basis for establishing the track circuit complete system

Single-state distributed k-winners-take-all neural network model

Distributed k-winners-takes-all (k-WTA) neural network (k-WTANN) models have better scalability than centralized ones. In this work, a distributed k-WTANN model with a simple structure is designed for the efficient selection of k winners among a group of more than k agents via competition based on their inputs. Unlike an existing distributed k-WTANN model, the proposed model does not rely on consensus filters, and only has one state variable. We prove that under mild conditions, the proposed distributed k-WTANN model has global asymptotic convergence. The theoretical conclusions are validated via numerical examples, which also show that our model is of better convergence speed than the existing distributed k-WTANN model.</p

The studies on gas adsorption properties of MIL-53 series MOFs materials

Molecular dynamics (MD), grand canonical Monte Carlo (GCMC) and ideal adsorbed solution theory (IAST) were used to study the structures and gas adsorption properties of MIL-53(M)[M=Cr, Fe, Sc, Al] metal organic framework (MOF) materials. The results show that the volumes of those MOF materials increase significantly at high temperature. By analyzing the adsorption isotherms, we found that the temperature had a paramount effect on the gas adsorption behaviors of these MOF materials. For MIL-53(Cr), the orders of the quantities of adsorbed gases were CH4>N2>CO2>H2S, CH4>H2S>CO2>N2 and CH4>CO2>H2S>N2 at 100K, 293K and 623K, respectively. We also calculated the adsorption of several combinations of two gases by MIL-53(Cr) at 293K, the results indicate that the material had selective adsorption of CH4 over CO2, H2S and N2. Our calculations provide microscopic insights into the gas adsorption performances of these MOFs and may further guide the practice of gas separation

Tomato Leaf Disease Recognition via Optimizing Deep Learning Methods Considering Global Pixel Value Distribution

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, a self-attention mechanism called GD-Attention is proposed, which considers global pixel value distribution information and guide the deep learning model to give more concern on the leaf disease area. Based on data augmentation, the proposed method inputs both the image and its pixel value distribution information to the model. The GD-Attention mechanism guides the model to extract features related to pixel value distribution information, thereby increasing attention towards the disease area. The model is trained and tested on the Plant Village (PV) dataset, and by analyzing the generated attention heatmaps, it is observed that the disease area obtains greater weight. The results achieve an accuracy of 99.97% and 27 MB parameters only. Compared to classical and state-of-the-art models, our model showcases competitive performance. As a next step, we are committed to further research and application, aiming to address real-world, complex scenarios

Gastric Ulceration and Immune Suppression in Weaned Piglets Associated with Feed-Borne Bacillus cereus and Aspergillus fumigatus

As a multifactorial cause, gastric ulceration-mediated diarrhea is widely prevalent in the weaned piglets, impairing pig health and economic benefits. With full implementation of antibiotic stewardship programs in China, Bacillus cereus (B. cereus) and Aspergillus fumigatus (A. fumigatus) were identified frequently in porcine feedstuffs and feeds of the animal industry. Association between feed-borne B. cereus and frequent diarrhea remains unclear. In the present study, we conducted a survey of B. cereus and A. fumigatus from feeds and feedstuffs in pig farms during hot season. Interestingly, B. cereus, B. subtilis, B. licheniformis and B. thuringinesis were isolated and identified from piglets&rsquo; starter meals to sow feeds, accounting for 56.1%, 23.7%, 13.7% and 6.5%, respectively. Obviously, both B. cereus and B. subtili were dominant contaminants in the survey. In an in vitro study, Deoxynivalenol (DON) contents were determined in a dose-dependent manner post fermentation with B. cereus (405 and DawuC). Subsequently, 36 weaned piglets were randomly assigned to four groups and the piglets simultaneously received the combination of virulent B. cereus (Dawu C) and A. fumigatus while animals were inoculated with B. cereus (Dawu C), A. fumigatus or PBS as the control group. Clinically, piglets developed yellow diarrhea on day 5 and significant reductions of relative body weight were observed in the B. cereus group, and co-infection group. More importantly, IgG titers against Classical swine fever virus (CSFV) and Porcine epidemic diarrhea (PED) were reduced dramatically during 14-day observation in co-infection group, the B. cereus (Dawu C) group or the A. fumigatus group. However, lower Foot and mouth disease (FMD) -specific antibodies were reduced on day 7 compared to those of the control group. Additionally, lower lymphocyte proliferations were found in the B. cereus group and the co-infection group compared to the control group. Postmortem, higher lesions of gastric ulceration were observed in the B. cereus group and the co-infection group from day 7 to day 14 compared with those of the A. fumigatus group and the control group. Compared to the A. fumigatus group, higher DON contents were detected in the stomach inoculated with B. cereus and the co-infection with A. fumigatus. In conclusion, our data support the hypothesis that B. cereus might be associated with severe diarrhea by inducing gastric ulcerations and A. fumigatus might aggravate immune suppression, threating a sustainable swine industry. It is urgently needed to control feed-borne B. cereus contamination