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In this paper, network energy assesses the capacity of a node to convey messages to others. In most cases, network energy is created when two nodes interact with one another. If a node is part of the same network as the node it is connecting with, then it will be able to make an inter-network energy connection with the node it is meeting. In the case that this does not take place, there will be an accumulation of energy within the part of the network that is facing the node. A node with higher inter-network energy is considered suitable for forwarding. The energy optimisation is achieved using efficient identification of source and destination pairs. This work has considered two scenarios, i.e. lossless transmission and lossy transmission, for our experiments and evaluated the detection probability. The performance of the proposed PPM is evaluated in terms of delivery ratio, overhead and hops count performance measures. When the buffer size is set to 100 MB, PPM delivers 59% of messages with message overhead of 750 and it has a hop count of 2, which is comparable to the state-of-the-art methods. With lengthy lifetimes of IOT networks, PPM is capable of giving higher performance while maintaining the privacy of network. The detection probability for the lossy observations model is applied to a 10-node, 20-node, 30-node and 40-node IoT network. [ABSTRACT FROM AUTHOR]

To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving, we proposed an improved U-Net network. Firstly, to improve the model representation capability, our improved U-Net network structure was designed as three parts, shallow layer, intermediate layer and deep layer. Different attention mechanisms were used according to their feature extraction characteristics. Specifically, a spatial attention module was used in the shallow network, a dual attention module was used in the intermediate layer network and a channel attention module was used in the deep network. At the same time, the traditional convolution was replaced by depthwise separable convolution in above three parts, which can largely reduce the number of network parameters, and improve the network operation speed greatly. The experimental results on three datasets show that our improved U-Net semantic segmentation model for street images can get better results in both segmentation accuracy and speed. The average mean intersection over union (MIoU) is 68.8%, which is increased by 9.2% and the computation speed is about 38 ms/frame. We can process 27 frames images for segmentation per second, which meets the real-time process and accuracy requirements for semantic segmentation of urban street images. [ABSTRACT FROM AUTHOR]

The article titled "Privacy-preserving routing using jointly established protocol in IoT network environment" has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and being out of scope for the journal or guest-edited issue. The investigation conducted by the publisher led to a loss of confidence in the results and conclusions of the article. The authors have not responded to correspondence regarding this retraction. Springer Nature, the publisher, remains neutral in terms of jurisdictional claims and institutional affiliations. [Extracted from the article]

The toxicity of soluble oil to the aquatic environment has started to attract wide attention in recent years. In the present work, we prepare graphene according to oxidation and thermal reduction methods for the removal of soluble oil from the solution. Characterization of the as-prepared graphene are performed by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Raman spectra, Brunauer-Emmett-Teller, X-ray photoelectron spectroscopy, and contact angle analysis. The adsorption behavior of soluble oil on graphene is examined, and the obtained adsorption data are modeled using conventional theoretical models. Adsorption experiments reveal that the adsorption rate of soluble oil on graphene is notably fast, especially for the soluble diesel oil, which could reach equilibrium within 30 min, and the kinetics of adsorption is perfectly consistent with a pseudo-second-order model. Furthermore, it is determined that the adsorption isotherm of soluble diesel oil with graphene fit the Freundlich model best, and graphene has a very strong adsorption capacity for soluble diesel oil in the solution. These results demonstrate that graphene is the material that provided both good adsorptive capacity and good kinetics, implying that it could be used as a promising sorbent for soluble oil removal from wastewater. [ABSTRACT FROM AUTHOR]