The ability to detect and size individual nanoparticles with high resolution is crucial to understanding the behaviour of single particles and effectively using their strong size-dependent properties to develop innovative products. We report realtime, in situ detection and sizing of single nanoparticles, down to 30 nm in radius, using mode splitting in a monolithic ultrahigh-quality-factor (Q) whispering-gallery-mode microresonator. Particle binding splits a whispering-gallery mode into two spectrally shifted resonance modes, forming a self-referenced detection scheme. This technique provides superior noise suppression and enables the extraction of accurate particle size information with a single-shot measurement in a microscale device. Our method requires neither labelling of the particles nor a priori information on their presence in the medium, providing an effective platform to study nanoparticles at single-particle resolution. With the rapid progress in nanotechnology, nanoparticles of different materials and sizes have been synthesized and engineered as key components in various applications ranging from solar cell
Coenzyme Q (CoQ) is present in all cells and membranes and in addition to be a member of the mitochondrial respiratory chain it has also several other functions of great importance for the cellular metabolism. This review summarizes the findings available to day concerning CoQ distribution, biosynthesis, regulatory modifications and its participation in cellular metabolism. There are a number of indications that this lipid is not always functioning by its direct presence at the site of action but also using e.g. receptor expression modifications, signal transduction mechanisms and action through its metabolites. The biosynthesis of CoQ is studied in great detail in bacteria and yeast but only to a limited extent in animal tissues and therefore the informations available is restricted. However, it is known that the CoQ is compartmentalized in the cell with multiple sites of biosynthesis, breakdown and regulation which is the basis of functional specialization. Some regulatory mechanisms concerning amount and biosynthesis are established and nuclear transcription factors are partly identified in this process. Using appropriate ligands of nuclear receptors the biosynthetic rate can be increased in experimental system which raises the possibility of drug-induced upregulation of the lipid in deficiency. During aging and pathophysiological conditions the tissue concentration of CoQ is modified which influences cellular functions. In this case the extent of disturbances is dependent on the localization and the modified distribution of the lipid at cellular and membrane levels.
High quality factor resonances are extremely promising for designing ultra-sensitive refractive index label-free sensors, since it allows intense interaction between electromagnetic waves and the analyte material. Metamaterial and plasmonic sensing have recently attracted a lot of attention due to subwavelength confinement of electromagnetic fields in the resonant structures. However, the excitation of high quality factor resonances in these systems has been a challenge. We excite an order of magnitude higher quality factor resonances in planar terahertz metamaterials that we exploit for ultrasensitive sensing. The low-loss quadrupole and Fano resonances with extremely narrow linewidths enable us to measure the minute spectral shift caused due to the smallest change in the refractive index of the surrounding media. We achieve sensitivity levels of 7.75 × 103 nm/refractive index unit (RIU) with quadrupole and 5.7 × 104 nm/RIU with the Fano resonances which could be further enhanced by using thinner substrates. These findings would facilitate the design of ultrasensitive real time chemical and biomolecular sensors in the fingerprint region of the terahertz regime.
Abstract Task scheduling, which plays a vital role in cloud computing, is a critical factor that determines the performance of cloud computing. From the booming economy of information processing to the increasing need of quality of service (QoS) in the business of networking, the dynamic task-scheduling problem has attracted worldwide attention. Due to its complexity, task scheduling has been defined and classified as an NP-hard problem. Additionally, most dynamic online task scheduling often manages tasks in a complex environment, which makes it even more challenging to balance and satisfy the benefits of each aspect of cloud computing. In this paper, we propose a novel artificial intelligence algorithm, called deep Q-learning task scheduling (DQTS), that combines the advantages of the Q-learning algorithm and a deep neural network. This new approach is aimed at solving the problem of handling directed acyclic graph (DAG) tasks in a cloud computing environment. The essential idea of our approach uses the popular deep Q-learning (DQL) method in task scheduling, where fundamental model learning is primarily inspired by DQL. Based on developments in WorkflowSim, experiments are conducted that comparatively consider the variance of makespan and load balance in task scheduling. Both simulation and real-life experiments are conducted to verify the efficiency of optimization and learning abilities in DQTS. The result shows that when compared with several standard algorithms precoded in WorkflowSim, DQTS has advantages regarding learning ability, containment, and scalability. In this paper, we have successfully developed a new method for task scheduling in cloud computing.
The q‐rung orthopair fuzzy set (q‐ROFS), originally developed by Yager, is more capable than that of Pythagorean fuzzy set to deal uncertainty in real life. The main goal of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for q‐ROFSs. The primary purpose of the study is to develop the systematic transformation of information measures (distance measure, similarity measure, entropy, and inclusion measure) for q‐ROFSs. For obtaining this goal, some new formulae for information measures of q‐ROFSs are presented. To show the validity of the explored similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings, and also demonstrate their practicality and availability of similarity measure between q‐ROFSs.
An adult human body is made up of some 30 to 40 trillion cells, all of which stem from a single fertilized egg cell. The process by which the right cells appear to arrive in their right numbers at the right time at the right place -- development -- is only understood in the roughest of outlines. This process does not happen in isolation: the egg, the embryo, the developing foetus, and the adult organism all interact intricately with their changing environments. Conceptual and, increasingly, mathematical approaches to modelling development have centred around Waddington's concept of an epigenetic landscape. This perspective enables us to talk about the molecular and cellular factors that contribute to cells reaching their terminally differentiated state: their fate. The landscape metaphor is however only a simplification of the complex process of development; it for instance does not consider environmental influences, a context which we argue needs to be explicitly taken into account and from the outset. When delving into the literature, it also quickly becomes clear that there is a lack of consistency and agreement on even fundamental concepts; for example, the precise meaning of what we refer to when talking about a `cell type' or `cell state.' Here we engage with previous theoretical and mathematical approaches to modelling cell fate -- focused on trees, networks, and landscape descriptions -- and argue that they require a level of simplification that can be problematic. We introduce random dynamical systems as one natural alternative. These provide a flexible conceptual and mathematical framework that is free of extraneous assumptions. We develop some of the basic concepts and discuss them in relation to now `classical' depictions of cell fate dynamics, in particular Waddington's landscape.
Lindsey Davis, Elizabeth French, Matias J. Aguerre
et al.
The widespread adoption of automatic milking systems (AMS) in the United States has afforded dairy cows the flexibility to establish personalized milking, feeding, and resting schedules. Our study focused on investigating the short-term effects of transitioning milking permissions from every 4 (MP4) to 6 (MP6) hours on the 100th day of lactation on milking frequency, milk yields, and cow behavior. Twenty-four Holstein dairy cows were divided into control (maintaining a 4 h milking interval) and test groups (transitioning to a 6 h milking interval) and observed for 6 days. The analysis revealed that parity and treatment had no significant impact on milking frequency, milk/visit, or daily milk yield. However, multiparous cows spent more time inside the commitment pen, while test group cows exhibited more tail-swishing and displacement behavior, approached the AMS more frequently, and spent longer idle times. The interaction between parity and treatment influenced heart rate variability parameters, indicating increased stress in the test group cows. Additionally, the test group cows had greater total and daytime lying frequencies, suggesting short-term behavioral modifications. Despite no immediate impact on milk production, further research is recommended to assess the potential long-term effects on milk yield in AMS farms, considering the identified stress indicators short-term.