Biological samples are a complex and heterogeneous matrix where different macromolecules with different physicochemical parameters cohabit in reduced spaces. The introduction of fluorophores into these samples, such as in the interior of cells, can produce changes in the fluorescence emission properties of these dyes caused by the specific physicochemical properties of cells. This effect can be especially intense with solvatofluorochromic dyes, where changes in the polarity environment surrounding the dye can drastically change the fluorescence emission. In this article, we studied the photophysical behavior of a new dye and confirmed the aggregation-induced emission (AIE) phenomenon with different approaches, such as by using different solvent proportions, increasing the viscosity, forming micelles and adding bovine serum albumin (BSA), through analysis of the absorption and steady-state and time-resolved fluorescence. Our results show the preferences of the dye for nonpolar media, exhibiting AIE under specific conditions through immobilization. Additionally, this approach offers the possibility of easily determining the critical micelle concentration (CMC). Finally, we studied the rate of spontaneous incorporation of the dye into cells by fluorescence lifetime imaging and observed the intracellular pattern produced by AIE. Interestingly, different intracellular compartments present strong differences in fluorescence intensity and fluorescence lifetime. We used this difference to isolate different intracellular regions to selectively study these regions. Interestingly, the fluorescence lifetime shows a strong difference in different intracellular compartments, facilitating selective isolation for a detailed study of specific organelles.

This paper presents additional results of the generalized bathtub model for urban networks, including a simpler derivation and exact solutions for uniformly distributed trip lengths. It is shown that in steady state this trip-based model is equivalent to the more parsimonious accumulation-based model, and that the trip-length distribution has merely a transient effect on traffic dynamics, which converge to the same point in the macroscopic fundamental diagram (MFD). To understand the statistical properties of the system, a queueing approximation method is proposed to compute the network accumulation variance. It is found that (i) the accumulation variance is much larger than predicted by traditional queueing models, due to the nonlinear dynamics imposed by the MFD, (ii) the trip-length distribution has no effect on the accumulation variance, indicating that the proposed formula for the variance might be universal, (iii) the system exhibits critical behavior near the capacity state where the variance diverges to infinity. This indicates that the tools from critical phenomena and phase transitions might be useful to understand congestion in cities.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.