Unsupervised category modeling, recognition, and segmentation in images

Suppose a set of arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category; (2) learning a region-based structural model of the category in terms of these properties; and (3) detection, recognition and segmentation of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to extract the maximally matching subtrees across the set, which are taken as instances of the target category. The extracted subtrees are then fused into a tree-union that represents the canonical category model. Detection, recognition, and segmentation of objects from the learned category are achieved simultaneously by finding matches of the category model with the segmentation tree of a new image. Experimental validation on benchmark datasets demonstrates the robustness and high accuracy of the learned category models, when only a few training examples are used for learning without any human supervision.     

A new IoT‐based smart energy meter for smart grids

The significant increase in energy consumption by the growth of the population or by the use of new equipment has brought big challenges to the energy security as well as the environment. There is a need that consumers can track their daily use and understand consumption standards for better organizing themselves to obtain financial and energetic efficiency. With the improvement of smart networks technology for better energy supply, a smart meter is not just a simple measurement gadget anymore, but it has additional functions including smart equipment control, bidirectional communication that allows integration of users and networks, and other functionalities. Smart meters are the most fundamental components in smart power grids. Besides, the meters used with a management system can be utilized for monitoring and controlling home appliances and other gadgets according to the users' need. A solution of an integrated and single system should be more efficient and economical. Smart measurement systems allow monitoring the energy consumption of the final consumers while providing useful information about the energy quality. The information provided by these systems is used by the operators to enhance the energy supply, and different techniques can be also applied for this end, such as charge scheduling, management from the demand side, and non‐intrusive load monitoring. The Internet of Things (IoT) is becoming a great ally in the management of smart distribution and energy consumption in smart systems scenarios. To address these issues, this paper proposes and demonstrates a new smart energy meter following an IoT approach and its associated costs and benefits. The developed device incorporates several communication interfaces. In order to easily integrate with any monitoring software solution, the meter has a multi‐protocol connection. Finally, the provided solution is validated and demonstrated in real‐life environments and it is also under use.     

Economic Policy Uncertainty Index Meets Ensemble Learning

Computational Economics - We utilize a battery of ensemble learning techniques [ensemble linear regression (LM), random forest], as well as two gradient boosting techniques [Gradient Boosting...     

Impact of short-term heat pre-treatment at 50 °C on the toxicity of contact insecticides to adults of three Sitophilus granarius (L.) populations

The effect of short-term heat pre-treatment on the toxicity of contact insecticides was tested on Sitophilus granarius adults from three populations in order to examine possible interactions. In the laboratory: (a) weevil paralysis time (PT) after start of direct exposure to 50 °C; (b) the impact of three short-term heat pre-treatments at 50 °C (PT₂₀, PT₅₀ and PT₈₀) on the 24 and 72 h recovery rates of laboratory (Lab) strain adults exposed to one of five insecticides on filter papers; and (c) deltamethrin and dichlorvos toxicity to weevils of two populations with altered insecticide susceptibility after exposure to 50 °C over the PT₈₀ interval, were determined.The PT₉₉ values for weevils after exposure to 50 °C showed that the population selected with pirimiphos-methyl had the longest paralysis time. Previous heat treatments of the Lab strain for the PT₂₀ and PT₅₀ intervals had little impact on the toxicity of the insecticides, chlorpyrifos-methyl being the most toxic with the first interval, deltamethrin with the second, while malathion was the least toxic. However, after heat treatment of the Lab strain over the PT₈₀ and after a 72-h recovery period, deltamethrin and dichlorvos showed reduced toxicity, while chlorpyrifos-methyl was the most toxic insecticide and malathion again the least toxic.For field and selected weevils, heat pre-treatment at 50 °C over the PT₈₀ increased deltamethrin and dichlorvos toxicity to selected populations, but not to field populations when compared to their toxicity without pre-heating. Deltamethrin and dichlorvos toxicity after a 72-h recovery period from heat treatment over the PT₈₀ was higher to the selected population than to the resistant field population. Compared to the Lab strain, deltamethrin demonstrated lower toxicity to selected weevils and especially to a field population, while dichlorvos was considerably more toxic to both populations.     

On the control of dynamic systems with unknown operating point

For dynamic systems where the position of the operating point is unkown a certainty equivalence adaptive control scheme is proposed which extends and clarifies the wash-out filter practice. Stability properties of the adaptive controller are derived. For slow adaptation, a singular perturbation analysis is carried out to derive additional stability properties and design criteria. Conditions are given under which the proposed scheme achieves exponential stability of the unkown operating point.     

CCVD Synthesis of Carbon‐Encapsulated Cobalt Nanoparticles for Biomedical Applications

Carbon‐encapsulated ferromagnetic Cobalt nanoparticles (Co@C) have been synthesized by catalytic chemical vapour deposition (CCVD). The nanoparticles, mainly ranging between 10 and 15 nm, are tightly encapsulated by 2–3 concentric graphitic carbon shells and protected from oxidation. Because of their magnetic properties (saturation magnetization of 106 emu/g and a coercivity HC of 250 Oe), Co@C nanoparticles have been investigated for hyperthermia application. Although the observed values of the specific absorption rate (28.7 W/gCo@C at 30 kA/m and 215.4 W/gCo@C at 70 kA/m) are slightly lower than required in actual hyperthermia therapies, the observed strong heating effect provides a very promising starting point for future clinical application. It is also demonstrated that these nanoparticles can at the same time be used for magnetic resonance imaging (MRI) with an efficiency comparable to commercially available T2 contrast agents.