Accurate estimation of essential enzyme kinetic parameters, such asKmandVmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1) extracted fromSolanum tuberosum,Agaricus bisporus, andPleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values ofKmandVmax.

Search and rescue operations usually require significant resources, personnel, equipment, and time. In order to optimize the resources and expenses and to increase the efficiency of operations, the use of unmanned aerial vehicles (UAVs) and aerial photography is considered for fast reconnaissance of large and unreachable terrains. The images are then transmitted to control center for automatic processing and pattern recognition. Furthermore, due to the limited transmission capacities and significant battery consumption for recording high resolution images, in this paper we consider the use of smart acquisition strategy with decreased amount of image pixels following the compressive sensing paradigm. The images are completely reconstructed in the control center prior to the application of image processing for suspicious objects detection. The efficiency of this combined approach depends on the amount of acquired data and also on the complexity of the scenery observed. The proposed approach is tested on various high resolution aerial images, while the achieved results are analyzed using different quality metrics and validation tests. Additionally, a user study is performed on the original images to provide the baseline object detection performance.

The need for search and rescue is not one that will go away. Extending beyond the concerns of hikers, natural disasters have brought the necessity for search and rescue in the heart of civilization. The advent of new technologies, such as unmanned aerial vehicles, can help decrease the cost of search and rescue operations and increase survival rates by finding the lost individuals in a faster manner. Visual object recognition is key to successful drone application for assisting search and rescue activities, and it is critical to develop a fully autonomous system. In recent years, deep convolutional neural networks have proven themselves as a powerful class of models and have become de facto a standard in a computer vision community. Moreover, a number of studies have shown that intermediate activations extracted from a deep convolutional neural network, pre-trained on large image dataset, can be adopted as universal image representation and transferred to other image classification tasks, leading to striking performances. In this paper, we investigate the effectiveness of different layer activations on the performance of convolutional features for binary person classification from UAV imagery, using various deep network architectures. Experiments have demonstrated that convolutional network generated features can deliver extremely competitive results, comparable to human-level performance. Furthermore, they reveal that in said context fully connected layer activations generalize well and are more expressive than the mid- or higher-level layers activations. Our work provides guidance for transferring pre-trained deep convolutional neural networks to address remote person classification tasks.

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images—combining a Siamese network model with a triplet-based loss function—to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework’s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets.