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
The CMS trigger system must reduce an input data rate from the LHC bunch-crossing frequency of 40 MHz to a rate which will be written to permanent storage. A detailed study has recently been made of the performance of this system. This paper presents key elements of the results obtained and gives details of a draft “trigger table” for the Level-1 Trigger and the High-Level Trigger selection at a “start-up” luminosity of 2× 1033 cm – 2s – 1. High efficiencies for most physics objects are attainable with a selection that remains inclusive and avoids detailed topological or other requirements on the event.
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.
Subwavelength confinement of light with plasmonics is promising for nanophotonics and optoelectronics. However, it is nontrivial to obtain narrow plasmonic resonances due to the intrinsically high optical losses and radiative damping in metallic structures. In this review, a thorough summary of the recent research progress on achieving high‐quality (high‐Q) factor plasmonic resonances is provided, emphasizing the fundamentals and six resonant mode types, including surface lattice resonances, multipolar resonances, plasmonic Fano resonances, plasmon‐induced transparency, guided‐mode resonances, and Tamm plasmon resonances. The applications of high‐Q plasmonic resonances in spectrally selective thermal emission, sensing, single‐photon emission, filtering, and band‐edge lasing are also discussed.
q‐Rung orthopair fuzzy set (q‐ROFS) is a powerful tool that attracts the attention of many scholars in dealing with uncertainty and vagueness. The aim of paper is to present a new score function of q‐rung orthopair fuzzy number (q‐ROFN) for solving the failure problems when comparing two q‐ROFNs. Then a new exponential operational law about q‐ROFNs is defined, in which the bases are positive real numbers and the exponents are q‐ROFNs. Meanwhile, some properties of the operational law are investigated. Later, we apply them to derive the q‐rung orthopair fuzzy weighted exponential aggregation operator. Additionally, an approach for multicriteria decision‐making problems under the q‐rung orthopair fuzzy data is explored by applying proposed aggregation operator. Finally, an example is investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q‐rung orthopair fuzzy decision‐making methods, are (1) it can obtain the optimal alternative without counterintuitive phenomena; (2) it has a great power in distinguishing the optimal alternative.
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto
et al.
Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient than non-ensemble counterparts such as Soft Actor-Critic (SAC) (Haarnoja et al., 2018a). To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions. Our dropout Q-functions are simple Q-functions equipped with dropout connection and layer normalization. Despite its simplicity of implementation, our experimental results indicate that DroQ is doubly (sample and computationally) efficient. It achieved comparable sample efficiency with REDQ, much better computational efficiency than REDQ, and comparable computational efficiency with that of SAC.
Javad Noorbakhsh, Ali Foroughi pour, Jeffrey Chuang
Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.
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.
Resumen El bullying es un fenomeno de agresion injustificada que actualmente sucede en dos formatos: cara a cara y como una conducta que se realiza a traves de dispositivos digitales ( cyberbullying ). Ampliamente estudiado tanto el primero como el segundo, hay sin embargo escaso conocimiento sobre la homogeneidad de ambos problemas y no disponemos de instrumentos de medida que permitan valorar las dos dimensiones del fenomeno: la agresion y la ciberagresion, la victimizacion y la cibervictimizacion. Este trabajo presenta la validacion del European Bullying Intervention Project Questionnaire y del European Cyberbullying Intervention Project Questionnaire, que evaluan la implicacion en bullying y en cyberbullying , respectivamente. Ambos se han administrado a 792 estudiantes de secundaria y se han obtenido unos buenos resultados de ajuste y propiedades psicometricas. La realizacion de un modelo de ecuaciones estructurales ha evaluado la concurrencia y relaciones entre ambos fenomenos, encontrando la influencia del bullying sobre el cyberbullying , pero no al contrario. Estos resultados muestran la idoneidad de ambos instrumentos para evaluar de forma conjunta bullying y cyberbullying , dada su importante relacion y similitud, lo que los convierten en buenas herramientas para la intervencion psicoeducativa destinada a prevenir y reducir ambos fenomenos.
Florian Kittelmann, Pavel Sulimov, Kurt Stockinger
Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel deep learning methods stimulated not only the rise of LQOs but also contributed to the appearance of learned cardinality estimators (LCEs). However, the majority of them are based on classical neural networks, ignoring that multivariate correlations between attributes across different tables could be naturally represented via entanglements in quantum circuits. In this paper, we introduce QardEst - Quantum Cardinality Estimator - a novel quantum neural network approach to estimate the cardinality of join queries. Our experiments conducted with a similar number of trainable parameters suggest that quantum neural networks executed on a quantum simulator outperform classical neural networks in terms of mean squared error as well as the q-error.
Microorganisms could play a significant role in shaping the properties of ice. This overview examines the existing knowledge on the influence of microorganisms on ice, focusing on glacial, polar, and marine ice systems. The interaction between microorganisms and ice mechanics is complex and multifaceted. Studies have shown that microbial activity, including biofilm formation, enzymatic processes, and ice nucleation, can impact ice strength and int interaction with structures. Microbial colonization has been found to weaken ice structures, alter ice permeability, and influence ice deformation behavior. Additionally, microbial communities associated with ice surfaces have been shown to enhance ice adhesion and frictional properties. Understanding the role of microorganisms in ice mechanics is crucial for comprehending the dynamics of frozen environments and their response to environmental changes. Further research in this field will contribute to improved predictions of ice behavior and its implications for climate, engineering, and environmental processes.