Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species.

Roadside trees alter biotic and abiotic factors of plants diversity in an ecosystem. Rows of plants grow along the roadside due to the interplay between the arrival of propagule and seedling establishment, which depends on the road's specifications, land pattern, and road administration and protection practices. A field study was conducted to measure the roadside tree diversity in the city of Karachi (Pakistan). A total of 180 plots, divided into three primary road groups, were surveyed. The highest quantity of tree biomass per unit area was found on wide roads, followed by medium roads. On narrow roads, the least biomass was detected. A single species or a limited number of species dominated the tree community. Conocarpus erectus was the most dominant non-native species on all types of sidewalks or roadsides, followed by Guaiacum officinale. A total of 76 species (32 non-natives and 44 natives) that were selectively spread along the roadsides of the city were studied. There was a significant difference in phylogenetic diversity (PD), phylogenetic mean pairwise distance (MPD), and phylogenetic mean nearest taxon distance (MNTD) among wide, medium, and narrow roads. Management practices have a significant positive correlation with diversity indices. Our study identified patterns of diversity in roadside trees in Karachi. It provides the basis for future planning for plant protection, such as the protection of plant species, the maintenance of plant habitats, and the coordination of plant management in Karachi.

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications.

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

A hybrid approach to forecast the COVID-19 epidemic trend.

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model's inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.

Advance hybrid medical watermarking algorithm using speeded up robust features and discrete cosine transform.

In the continuous development of computer network technology, multimedia technology and information technology, digitization has become the main means of displaying information, thus facilitating the storage, copying and dissemination of digital multimedia information. In this context, there are no restrictions on arbitrary editing, copying, modification, and dissemination of digital images, music, etc., which leads to various social problems such as information security, copyright disputes, and piracy. With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Experiments show that the algorithm has good invisibility and strong robustness against conventional and geometric attacks and can effectively protect the security of images with NC value more than 90%.