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71.
Melanopsin is an essential element in the human eye, sensing optical light, indeed beyond the function of rods and cones, and contributing largely to non-image-forming functions like learning behavior, emotions, and maintaining sun cycle. Mimicking melanopsin behavior using an optoelectronic device could be an essential step toward the advancement of future technologies, including data storage applications and artificial vision. This research proposes and illustrates the mimicking of the primitive functions of melanopsin in a manner very similar to the human eye retina by using an integrated Schottky photodetector and an HfO2-based memory device. Particularly, current–voltage curves show nominal hysteresis loop opening under dark conditions, whereas stable analog hysteresis loops form with light illumination, which is regulated progressively by increasing the intensity. Based on photoconductive atomic force microscopy and charge transport measurements, the charge trapping/detrapping is estimated to be associated with the light-regulated hysteresis loop opening. Specifically, versatile synaptic functions are stimulated artificially with an optical pulse, which is sustained over a long period of time even after removing the light, indeed very similar to melanopsin behavior. The optically regulated properties described herein pave the way for the development of artificial optoelectronic melanopsin, artificial biological electronics, soft robotics, and data storage applications. 相似文献
72.
The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects. 相似文献
73.
74.
Naveed Ahmad Khan Fahad Sameer Alshammari Carlos Andrs Tavera Romero Muhammad Sulaiman Ghaylen Laouini 《Molecules (Basel, Switzerland)》2021,26(23)
In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction and substrate within the biosensor. The model is based on a reaction–diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg–Marquardt training (LMT) algorithm. NNs–LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as “pdex4”. Furthermore, the data set is validated by the processing of the NNs–LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique. 相似文献
75.
76.
Yury Rodimkov Evgeny Efimenko Valentin Volokitin Elena Panova Alexey Polovinkin Iosif Meyerov Arkady Gonoskov 《Entropy (Basel, Switzerland)》2021,23(1)
When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area. 相似文献
77.
Stanis&#x;aw Kozdrowski Pawe&#x; Cichosz Piotr Paziewski S&#x;awomir Sujecki 《Entropy (Basel, Switzerland)》2021,23(1)
Increasing demand in the backbone Dense Wavelength Division (DWDM) Multiplexing network traffic prompts an introduction of new solutions that allow increasing the transmission speed without significant increase of the service cost. In order to achieve this objective simpler and faster, DWDM network reconfiguration procedures are needed. A key problem that is intrinsically related to network reconfiguration is that of the quality of transmission assessment. Thus, in this contribution a Machine Learning (ML) based method for an assessment of the quality of transmission is proposed. The proposed ML methods use a database, which was created only on the basis of information that is available to a DWDM network operator via the DWDM network control plane. Several types of ML classifiers are proposed and their performance is tested and compared for two real DWDM network topologies. The results obtained are promising and motivate further research. 相似文献
78.
Stephen Fox 《Entropy (Basel, Switzerland)》2021,23(2)
Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence. 相似文献
79.
Dr. Maria A. Komkova Ksenia D. Andreeva Alexander A. Zarochintsev Prof. Arkady A. Karyakin 《ChemElectroChem》2021,8(6):1117-1122
We report the single-step fabrication of advanced electrochemical biosensors upon drop-casting of “artificial peroxidase”, that is, oxidase-containing mixtures. Compared to bienzyme biosensors, we substitute peroxidase with more active and stable nanozymes based on catalytically synthesized Prussian Blue. The electroactivity of nanozymes in the sensing membrane was facilitated with carbon black nanoparticles, which resulted in record sensitivity of the H2O2 transducer exceeding 1.1 A M−1 ⋅ cm−2. Both glucose and lactate oxidases were co-immobilized with nanozymes in alkoxysilane or perfluorosulfonated ionomer membranes. The proposed drop-casting approach results in biosensors that are advantageous over those conventionally produced upon layer-by-layer immobilization in terms of i) almost an order of magnitude increased sensitivity, ii) unsurpassed biosensor-to-transducer sensitivity ratio reaching the value of 0.3, and iii) three times extended operational lifetime. 相似文献
80.
The antioxidant activity of a novel artificial glutathione peroxidase-like enzyme, selenium-containing glutathione 5-transferase
from Lucilia cuprina (seleno-LuGST1-1), was studied by using a ferrous sulfate/ascorbate-induced mitochondrial damage model system. Swelling of
mitochondria, lipid peroxidation, and cytochrome-c oxidase activity were selected to evaluate the preservation of mitochondrial integrity in this system. Seleno-LuGST1-1 could
effectively protect the mitochondria against oxidative damage in a dose-dependent manner and exhibited both higher catalytic
activity and greater antioxidant ability than the classic mimic, 2-phenyl-1,2-benziososelenazol-3(2H)-one (Ebselen). This
novel artificial biocatalyst therefore may have great protential for pharmacologic application in the treatment of reactive
oxygen species-related diseases. 相似文献