The diagnosis and prevention of lumpy skin disease, a viral ailment affecting cattle and buffalo, present significant financial implications for the livestock industry. Traditional methods for identifying lumpy skin d...
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The diagnosis and prevention of lumpy skin disease, a viral ailment affecting cattle and buffalo, present significant financial implications for the livestock industry. Traditional methods for identifying lumpy skin disease rely on manual visual inspection by veterinarians, which can be labor-intensive, subjective, and prone to errors. To address these challenges, this study proposes a novel deep convolutional neural network (DCNN) model for the automatic recognition and grading of lumpy skin disease from bovine images. The primary contributions of this research include the development of a DCNN architecture specifically tailored for this task, comprising five convolutional layers, five max pooling layers, two fully connected layers with ReLU activation, and a final fully connected layer with softmax activation. The model’s detection accuracy is further enhanced by applying image cropping and patching techniques, which divide each input image into 12 patches to improve local feature extraction. The proposed model was trained and tested using a publicly available dataset from Kaggle. Comparative analysis was conducted against several state-of-the-art models, including InceptionV3, ResNet50, MobileNetV3, VGG19, and Xception. The DCNN model demonstrated superior performance, achieving the highest validation accuracy of 0.96875, outperforming the compared models in terms of accuracy, precision, recall, and F1 score. Additionally, the study explores the potential of transitioning from binary to multiclass classification, which would allow for the assessment of the severity of lumpy skin disease. This future direction aims to provide more nuanced and actionable information for veterinary diagnostics. The significance of this research lies in its potential to offer an objective, efficient, and scalable solution for early disease detection and prevention in livestock, thereby presenting considerable economic benefits for farmers and the livestock industry as a whole. The me
The emerging field of topological acoustics provides exciting possibilities for controlling sound propagation with extraordinary robustness. Conventional topological acoustic waveguides built from topological edge sta...
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The emerging field of topological acoustics provides exciting possibilities for controlling sound propagation with extraordinary robustness. Conventional topological acoustic waveguides built from topological edge states, which arise solely from the nontrivial topology in the momentum space, usually have restricted shapes due to their crystalline structures. Here, we show that, using an acoustic topological system with both nontrivial topologies in the real and momentum spaces, free-form acoustic topological waveguides can be constructed. These topological waveguides support disclination-localized states, caused by the interplay between topological lattice defects and the valley-Hall topology. As demonstrated in our simulations and experiments, such disclination waveguides can take arbitrary shapes and form open arcs within the lattice, which are not possible for previous crystalline acoustic topological waveguides. Furthermore, by increasing the number of topological lattice defects, we can realize more than one topological waveguide in one lattice. They join at the topological lattice defect and form a topological waveguide network, enabling more complicated functionalities such as beam splitting. Our results point to a promising direction for free-form acoustic topological devices with the advantages of embedded forms, easy cascading, and high robustness.
Histone deacetylases (HDACs) are epigenetic enzymes that are crucial in tumor formation and are potential therapeutic targets for breast cancer treatment. HDACs catalyze the deacetylation of histone and nonhistone pro...
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Finite-temperature spin transport in integrable isotropic spin chains (i.e., spin chains with continuous non-Abelian symmetries) is known to be superdiffusive, with anomalous transport properties displaying remarkable...
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Finite-temperature spin transport in integrable isotropic spin chains (i.e., spin chains with continuous non-Abelian symmetries) is known to be superdiffusive, with anomalous transport properties displaying remarkable robustness to isotropic integrability-breaking perturbations. Using a discrete-time classical model, we numerically study the crossover to conventional diffusion resulting from both noisy and Floquet isotropic perturbations of strength λ. We identify an anomalously-long crossover timescale t★∼λ−α with α≈6 in both cases. We discuss our results in terms of a kinetic theory of transport that characterizes the lifetimes of large solitons responsible for superdiffusion.
Wireless signals are vulnerable to various security threats, like eavesdropping and jamming, due to the inherent broadcast nature of the wireless channel. Encryption may ensure the confidentiality of the data but does...
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Wireless signals are vulnerable to various security threats, like eavesdropping and jamming, due to the inherent broadcast nature of the wireless channel. Encryption may ensure the confidentiality of the data but does not guarantee successful communication among legitimate users in the presence of strong adversaries, like wideband jammers. In this scenario, hiding a secret signal in presence of another mundane ongoing communication is one of the ways to minimize its chances of getting intercepted. Wireless Steganography is a process of embedding a secret signal inside another signal that acts as a cover to hide the signal of interest. In this paper, we propose to encode secret bits into covert signals that are statistically indistinguishable from a hardware noise generated by a low-cost transmitter. As the covert signal resembles hardware noise, it can be transmitted over any waveform, making it adaptable and portable to any communication link. Each generated complex signal sample is merged with a cover signal sample, yielding a 50% embedding rate. We create the encoding and decoding models by creating a pair of complex-valued neural networks (NNs), which is trained in presence of another NN model, critic. The critic model differentiates between true hardware noise and encoder-generated covert signal, thus providing essential feedback to the NN pair to improve the encoding technique. The decoder undergoes a transfer learning process to adapt to the residual channel effects in over-the-air experiments. In an indoor testbed, we successfully decoded the covert communication that mimics a range of hardware noises and is transmitted using different modulation orders of cover OFDM waveform. Our steganalysis indicates that the covert signal can be generated to mimic specific hardware, which remains indistinguishable in different statistical tests. Our method performs an order of magnitude better in statistical steganalysis compared to the state-of-the-art method in this fi
This article presents a frequency-domain analysis that models the behavior of Constant-on-Time (COT)-controlled buck converters in wide-input-output-range scenarios. Based on this analysis, a design strategy is propos...
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Due to the increase in demand for electricity, the lack of fossil fuels, and the use of renewable energy sources, the use of energy storage systems becomes necessary. The use of storage systems in different parts of m...
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This work aims to develop a planar microwave sensor fabricated on a flexible polyimide substrate to monitor the water content of fruits nondestructively. The sensor is based on a planar loop resonator tuned with a con...
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Artificial lattices have been used as a platform to extend the application of topological band theory beyond electronic systems. Here, using the two-dimensional Lieb lattice as a prototypical example, we show that an ...
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Artificial lattices have been used as a platform to extend the application of topological band theory beyond electronic systems. Here, using the two-dimensional Lieb lattice as a prototypical example, we show that an array of disks which each support localized plasmon modes gives rise to an analog of the quantum spin-Hall state enforced by a synthetic time-reversal symmetry. We find that the plasmonic modes naturally possess a synthetic spin degree of freedom which leads to a spin-dependent second-neighbor coupling mechanism mediated by interorbital coupling. This interaction introduces a nontrivial Z2 topological order and gaps out the Bloch spectrum. A faithful mapping of the plasmonic system onto a tight-binding model is developed and shown to capture its essential topological signatures. Full wave numerical simulations of graphene disks arranged in a Lieb lattice confirm the existence of propagating helical boundary modes in the nontrivial band gap.
Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long *** such,several Battery Management Systems(BMS)ha...
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Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long *** such,several Battery Management Systems(BMS)have been developed to maintain system reliability and extend the battery’s operative *** estimation of the battery’s State of Charge(SOC)is a key challenge in the BMS due to its non-linear *** paper presents a comprehensive review on the most recent classifications and mathematical models for SOC *** trends for SOC estimation methods are also presented.
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