Hidden Properties Research Articles

In search of hidden symmetries

Abstract This paper exemplifies the importance of finding hidden symmetries of differential equations that are models of physical phenomena. The hidden symmetries (Lie symmetries) may be determined by either linking together different equations for certain values of their parameters or transforming the original model into another equivalent system of equations that may have more symmetries. Therefore, hidden symmetries may help to solve the original model or yield its hidden properties, e.g. linearity and conservation laws. Moreover Noether symmetries are shown to be preserved by going from classical to quantum mechanics, namely from Lagrangian systems to the corresponding time-dependent Schrödinger equation.

A novel 5D memristor conservative chaotic system with multiple forms of hidden flows

Memristor is one of the basic circuit elements commonly used in circuit model analysis. More complex dynamic characteristics can be observed by coupling memristor into nonlinear circuit. However, there is relatively little attention paid to high-dimensional conservative chaos based on memristors up to now. In this paper, a five-dimensional memristor conservative chaotic system is built after the introduction of the memristor into conservative chaotic system. There is no equilibrium point in this system and the phase trajectory produced by it has hidden properties. Its conservatism is analyzed by bifurcation diagram, Lyapunov exponent spectrum and divergence. The phase trajectory will change with the change of parameters, which Poincaré mapping also verified these dynamic behaviors. In addition, hidden extreme multistability and initial value offset boosting behavior are also found in this system. It is to be noted that this behavior is less in memristor conservative chaotic system without equilibrium points. At the same time, a new transient transition behavior is observed. By introducing spectral entropy algorithm, the complexity of sequences is analyzed and compared with the existing literature. The results show that the system has higher complexity. Finally, the systematic analogous circuit is designed and built whose results are consistent with the MATLAB numerical simulation results, which has laid a solid foundation for the practical application of the system in engineering.

Enhancing healthcare security in the digital era: Safeguarding medical images with lightweight cryptographic techniques in IoT healthcare applications

The Internet of Things has allowed for the connectivity of medical imaging devices to the healthcare sector's data backbone. This development, made possible by the IoT, will speed up the diagnosis and treatment phases of medical care. The increasing reliance on interconnected devices and cloud-based systems creates potential entry points for cyber-attacks and unauthorized access to sensitive medical data which not only jeopardize patient privacy but also have serious implications for patient safety and trust in healthcare systems. Medical images are critical assets in healthcare, and their confidentiality, integrity, and availability are paramount for accurate diagnoses, treatment planning, and patient care. This instigated research related to medical image security in IoT healthcare. For this, we investigated the usage of a cryptography-based network for image encryption and decryption, and its potential application to the safe transmission of medical images through deep learning. In the proposed work, we use a key learning network based on the ResNet-50 architecture to do the mapping between various image representations. Due to the incorporation of these “hidden properties” into the learning model, the encryption technique can be fine-tuned for each specific domain. As a first step in decryption, we use reconstructive networks to convert the encrypted image back into its “plaintext” form. Once the hidden entities have been uncovered, a Return on Investment (ROI) framework can be made available, and data mining can be simplified by drawing directly from the user's local information environment. Imaging tools for gauging therapy is a highly reliable by using the proposed system. We used two different kinds of publicly available datasets, and that helped us succeed in our mission. The comprehensive empirical setup and the findings of the security analysis suggest that the suggested method may give an unprecedented level of security and an unprecedentedly powerful outcome.

Rediscovering orbital mechanics with machine learning

We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.

Learning more from the inter-rater reliability of interstitial fibrosis assessment beyond just a statistic

Interstitial fibrosis assessment by renal pathologists lacks good agreement, and we aimed to investigate its hidden properties and infer possible clinical impact. Fifty kidney biopsies were assessed by 9 renal pathologists and evaluated by intraclass correlation coefficients (ICCs) and kappa statistics. Probabilities of pathologists’ assessments that would deviate far from true values were derived from quadratic regression and multilayer perceptron nonlinear regression. Likely causes of variation in interstitial fibrosis assessment were investigated. Possible misclassification rates were inferred on reported large cohorts. We found inter-rater reliabilities ranged from poor to good (ICCs 0.48 to 0.90), and pathologists’ assessments had the worst agreements when the extent of interstitial fibrosis was moderate. 33.5% of pathologists’ assessments were expected to deviate far from the true values. Variation in interstitial fibrosis assessment was found to be correlated with variation in interstitial inflammation assessment (r2 = 32.1%). Taking IgA nephropathy as an example, the Oxford T scores for interstitial fibrosis were expected to be misclassified in 21.9% of patients. This study demonstrated the complexity of the inter-rater reliability of interstitial fibrosis assessment, and our proposed approaches discovered previously unknown properties in pathologists’ practice and inferred a possible clinical impact on patients.

Information-driven optimal experimental design with deep neural network surrogate model for composite materials

The optimal experimental design (OED) provides informative experimental resources and reduces the inherent uncertainty of the experiment. The OED is a framework to maximize performed experiments by controlling design variables. This paper proposes the OED framework combined with deep neural network (DNN) surrogate model to find the optimal design over large and complex design space. The OED has the optimization process to maximize the conditional mutual information (CMI) between hidden properties and composite structural performances. Approximate coordinate exchange (ACE) algorithm was applied to find the optimal design over a high and complicated design space. For fast optimization, surrogate DNN model was used instead of finite element analysis. Two examples were executed for the demonstration of the OED frameworks. From the results of each demonstration, OED provided the results that are consistent with heuristic knowledge.

Transformation of Hexagonal Lu to Cubic LuH2+x Single-Crystalline Films

With the recent report of near ambient superconductivity at room temperature in the N-doped lutetium hydride (Lu–H–N) system, the understanding of cubic Lu–H compounds has attracted worldwide attention. Generally, compared to polycrystals with non-negligible impurities, the single-crystalline form of materials with high purity can provide an opportunity to show their hidden properties. However, the experimental synthesis of single-crystalline cubic Lu–H compounds has not been reported so far. Here, we develop an easy way to synthesize highly pure LuH2+x single-crystalline films by the post-annealing of Lu single-crystalline films (purity of 99.99%) in H2 atmosphere. The crystal and electronic structures of films were characterized by x-ray diffraction, Raman spectroscopy, and electrical transport. Interestingly, Lu films are silver-white and metallic, whereas their transformed LuH2+x films become purple-red and insulating, indicating the possible formation of an unreported electronic state of Lu–H compounds. Our work provides a novel route to synthesize and explore more single-crystalline Lu–H compounds.

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Researched and developed mechanisms for optimizing the identification of random time series based on mechanisms for searching for unknown knowledge, hidden properties, patterns, relationships, features of non-stationary objects under the condition of limited a priori information, uncertainty, non-stationary processes. A generalized model for optimizing the identification of RTS based on the use of neural networks, neuro-fuzzy networks of dynamic models, as well as fuzzy logic algorithms is implemented. Instruments for data reliability control are obtained based on statistical, dynamic, intelligent approaches suitable for the conditions of transmission of incomplete, heterogeneous, partially given information with large parametric uncertainty. The principles and methods of data reliability control in a fuzzy environment are developed based on the generalization and use of the properties of neural networks, fuzzy logic and statistical modeling methods. Algorithms for searching for correlations, trends, interrelations and regularities in data, forming training, control and test sets for solving problems of recognition, classifying images of micro-objects and predicting power supply indicators are synthesized. The results of the research are implemented in the form of a software package for identifying non-stationary objects, which ensures the adaptability of model variables, high data processing performance and accuracy of results.

BIO EFFICACY OF EPIPREMNUM AUREUM: A REVIEW

Epipremnum aureum belonging to the family Araceae are most commonly known as Money plant. Epipremnum aureum species is one of the most popular houseplant and generally used for a ornamental purposes. It is a common indoor plant and has air purifying properties which removes air pollutants such as xylene, formaldehyde and benzne.. In this article, we review origin, distribution, morphological characters, Qualitative screening, ethnomedicinal uses and medicinal properties of Epipremnum aureum. Every part of the E.aureum possess antioxidant and antibacterial properties. It can also contain anti-malarial, anti-cancerous and wound healing etc properties. There are many more hidden properties in Epipremnum species that need to be exposed through using the scientific investigation to make it useful for the human health and environment.

Community detection using Local Group Assimilation

Clustering of vertices in complex networks to detect communities is an open challenge due to its unknown and hidden properties and broad areas. Complex networks occur in various areas and interdisciplinary fields be it biological, social, chemical, electrical or any other. Numerous methods have been proposed with significant contributions to detect communities but still its nature of properties, throw us a challenge in detecting meaningful communities in real networks. Communities are built through nodes and each node has its own significance. Similarity among nodes through their neighbors suggests common interest of those nodes. The idea is to identify small groups of similar nodes locally and gradually built communities using global information. Moreover, we are not taking any seed nodes and consider each node an important player. The identification of local structures and demarcating their boundaries possess another challenge. Here, we propose Local Group Assimilation (LGA) algorithm that identifies clusters or communities in a network graph using both local and global structure information. The algorithm compares two adjacent nodes by neighborhood similarity measure and picks the highest value pair. The highest value pairs of nodes are grouped together in such a manner that it generates initial clusters of various sizes. The local groups are merged further in iterative manner that maximizes inter cluster edge density between them. Our algorithm detects small significant and relevant communities on real networks that assimilate into larger ones with promising results. We evaluated our algorithm in both real world networks and synthetic benchmark networks and compared it with most popular state-of-the-art community detection algorithms. Our experimental results show that the LGA algorithm detects significant communities in complex networks comparable to most popular algorithms and can be used in real networks to detect communities.

Diagnostics of counterfeit food products

In Russia, the concept of counterfeiting is defined as “Counterfeit food products are food products that are deliberately modified (fake) and (or) have hidden properties and quality, information about which is deliberately incomplete or unreliable” [5]. According to this definition, the main sign of counterfeiting is the willfulness of counterfeiting or willful concealment of information about the properties of products that are not obvious to the buyer. For laboratory testing activities, counterfeiting is basically one of two possible identification results, i.e. compliance or non-compliance of products with the requirements established by the technical regulations of the Customs Union, legislative, regulatory legal acts of the Russian Federation. The most commonly identified nonconformity, in addition to food safety requirements, is an unreliable composition.

Oscillations and Pattern Formation in a Slow-Fast Prey-Predator System.

We consider the properties of a slow-fast prey-predator system in time and space. We first argue that the simplicity of the prey-predator system is apparent rather than real and there are still many of its hidden properties that have been poorly studied or overlooked altogether. We further focus on the case where, in the slow-fast system, the prey growth is affected by a weak Allee effect. We first consider this system in the non-spatial case and make its comprehensive study using a variety of mathematical techniques. In particular, we show that the interplay between the Allee effect and the existence of multiple timescales may lead to a regime shift where small-amplitude oscillations in the population abundances abruptly change to large-amplitude oscillations. We then consider the spatially explicit slow-fast prey-predator system and reveal the effect of different timescales on the pattern formation. We show that a decrease in the timescale ratio may lead to another regime shift where the spatiotemporal pattern becomes spatially correlated, leading to large-amplitude oscillations in spatially average population densities and potential species extinction.

Molecular Confinement Effects by Self-Assembled Coordination Cages

Abstract When substrates are confined in an isolated cavity, they experience circumstances that are distinctly different from those in a bulk solution. Molecular self-assembly has widened the potential of molecular confinement by offering synthetic cavities on the nanometer-scale and allowing chemists to treat molecular aggregates and larger molecules in the cavities. In this account, we introduce the molecular confinement effects of self-assembled cages as a strategy to discover new or hidden properties and reactivities from the confined substrates in the cages. By confining molecules, the cavity can gather, arrange, fold, compress, and twist the molecules. The molecular confinement thus becomes a powerful strategy to draw new aspects of molecules.

Optimal experimental design with fast neural network surrogate models

Designing optimal experiments minimizes the uncertainty of results and maximizes the efficient use of resources. Herein, machine learning surrogate models and the approximate coordinate exchange (ACE) algorithm are used to determine optimal experimental designs (OEDs) over large or arbitrarily restrictive design spaces. OED is particularly salient in materials science, where experiments are expensive and material properties must often be inferred indirectly. The proposed framework is demonstrated by finding optimal experiments with which the hidden constituent properties of composite materials can be most efficiently inferred from observable experimental outcomes. The OED is given by an information-theoretic criterion that maximizes the conditional mutual information between the hidden properties and the expected experimental outcomes. To perform tractable optimization, a neural network is trained as a surrogate model to mimic a physics-based simulation, which can calculate the expected experimental outcome based on a candidate experimental design and sampled constituent properties. The ACE algorithm is used to optimize over large design spaces with many tests and controlled parameters where an exhaustive search would be intractable even with the surrogate model. Using this approach, OEDs that are consistent with those produced by heuristic knowledge and established best practices are found; then optimal designs in larger design spaces where heuristic knowledge is unavailable are examined. All code, data, and trained models to reproduce the work in this paper is available at https://github.com/nasa/OED-with-NN-surrogates.

A review on three-dimensional graphene: Synthesis, electronic and biotechnology applications-The Unknown Riddles.

In the last decade, carbon‐based nanostructures such as buckyball (C60), carbon nanotube (CNT), graphene and three‐dimensional (3D) graphene have been identified as promising materials for electronic, electrochemical energy storage (batteries and supercapacitors), optical and sensing applications. Since the discovery of graphene in 2004, scientists have devised mass production techniques and explored graphene as a promising material for a wide range of applications. Most of the electronic and solar cell applications require materials with good electronic conductivity, mobility and finite bandgap. Graphene is a zero bandgap material which prevents it from the mainstream applications. On the other hand, 3D graphene has good electronic conductivity, mobility, bandgap and electrochemical properties. This review article will focus on the synthesis of the 3D graphene, its structure‐property relationships, biotechnology and electronic applications and the hidden properties that are yet to be explored fully.

What’s behind the curtain? Visual priming by draped objects

Perhaps the most foundational assumption in perception research is that our experience of the world goes beyond the light reaching our eyes. This principle is familiar from visual illusions, perceptual constancies, and amodal completion — as when we perceive the continuity of an object behind an occluding surface. What is the limit to such phenomena? In particular, could we ever experience objects that are occluded completely? In fact, we frequently do: When a cloth or other fabric is draped over an object, we can often appreciate which properties the draped object might have, even when no part of the object is directly visible (Yildirim et al., 2016) — e.g., when a car is placed under a weather-protective covering, or when a child wears a bedsheet to dress up as a ghost. What is the nature of this experience? Do we only infer such hidden properties through thought and reflection? Or might we see such properties as part of automatic visual processing? Here, we investigate this latter possibility using visual priming. On each trial, one of two volumetric shapes (e.g., a cube or sphere) appeared suddenly, and subjects indicated which shape was shown. Crucially, each shape was preceded by a brief ‘cue’: A rendering of one of the shapes with a cloth draped over it. There were thus two types of trials: congruent-cued and incongruent-cued. Even though this cue was completely invalid and task-irrelevant, it facilitated subjects’ responses: They were faster to report the identity of the displayed shape when its draped cue was congruent vs. when it was incongruent. We suggest that the mind automatically represents objects even when they reflect no light whatsoever onto our eyes, such that visual processing computes the properties of completely occluded objects.

New Aspects of Cytochrome c: 3D Domain Swapping, Membrane Interaction, Peroxidase Activity, and Met80 Sulfoxide Modification

Abstract Cytochrome (cyt) c is a multifunctional water-soluble heme protein. It transfers electrons from the cyt bc1 complex (Complex III) to cyt c oxidase (Complex IV) in the respiratory chain of mitochondria, and can trigger apoptosis as well. Although cyt c has been studied for more than a century, its new aspects are still being elucidated. For example, we found that cyt c molecules can form oligomers and polymers by 3D domain swapping (3D-DS), where the C-terminal α-helix is exchanged between molecules. 3D-DS is observed in other c-type cyts—although the swapping regions may differ—indicating that 3D-DS is a common feature for c-type cyts. 3D-DS of c-type cyt can occur during protein folding and expression in cells. The electron transfer ability of cyt c decreases by 3D-DS, due to the dissociation of Met80 from the heme iron, whereas the peroxidase activity increases. The cyt c electron transfer partners, Complex III and Complex IV, are embedded in the inner mitochondria membrane, whereas positively charged cyt c interacts with negatively charged cardiolipin (CL) molecules at the inner mitochondrial membrane. We have recently elucidated the CL-interaction site of cyt c at atomic level by NMR spectroscopy using CL-containing bicelles. The membrane interaction site of cyt c is relatively wide and similar to the interaction site for Complex III and Complex IV, indicating that cyt c interacts with lipid membranes and partner proteins in a similar way. When cyt c interacts strongly with CL, Met80 dissociates from the heme iron and the peroxidase activity of cyt c increases. We have shown that the proton concentration at the CL-containing membrane is higher than that in the bulk solution, which may enhance the peroxidase activity of cyt c. The Met80-dissociated cyt c has been shown to oxidize CL, increasing the permeability of cyt c through the membrane. We found that when Met80 is dissociated from the heme iron in cyt c, Met80 can be oxidized to methionine sulfoxide by the peroxidase reaction of the heme of cyt c or its reaction with molecular oxygen under reduced conditions. Met80-oxidized cyt c depicts a higher peroxidase activity compared to that of unmodified cyt c; thus Met80 oxidation may enhance lipid oxidation and eventually apoptosis. These new findings not only help in understanding the structure-function relationships of multifunctional cyt c but also show that there are still hidden properties in well-studied proteins.

Hidden Properties of Mathematical Physics Equations. Double Solutions. The Realization of Integrable Structures. Emergence of Physical Structures and Observable Formations

With the help of skew-symmetric differential forms the hidden properties of the mathematical physics equations are revealed. It is shown that the equations of mathematical physics can describe the emergence of various structures and formations such as waves, vortices, turbulent pulsations and others. Such properties of the mathematical physics equations, which are hidden (they appear only in the process of solving these equations), depend on the consistency of derivatives in partial differential equations and on the consistency of equations, if the equations of mathematical physics are a set of equations. This is due to the integrability of mathematical physics equations. It is shown that the equations of mathematical physics can have double solutions, namely, the solutions on the original coordinate space and the solutions on integrable structures that are realized discretely (due to any degrees of freedom). The transition from the solutions of the first type to one of the second type describes discrete transitions and the processes of origin of various structures and observable formations. Only mathematical physics equations, on what no additional conditions such as the integrability conditions are imposed, can possess such properties. The results of the present paper were obtained with the help of skew-symmetric differential forms.

Envelope of Family of Angled Projectiles and Its Universal Geometric Characteristics

Geometric properties of trajectories of angled projectiles under gravity pull are a popular common traditional theme discussed in introductory physics and engineering college courses. What is overlooked is the universal collective properties of the overarching specificities of families of such parabolas, the envelope. For instance [1] and references within explored the existence of one such envelope, however, even the most recent article [2] overlooked its global hidden properties. Here, we investigate exposing this hidden information. Having the equation of the envelope on hand we introduce its universal characteristics such as its: arc length, enclosed 2D surface area, surface area of the surface-of-revolution about the symmetry axis, and, the volume of the enclosure. Numeric values of these quantities are global as is e.g. the 45&deg projectile angle that maximizes the range of a projectile in vacuum irrespective, its initial speed. In our exploratory investigation, we utilize the popular Computer Algebra System (CAS) MathematicaTM [3] [4] [5].

The interpolation of Young\u2019s inequality using dyadics

In this article we interpolate Young’s inequality using a delicate treatment of dyadics. Although there are other simple methods to prove these results, we present this new approach hoping to reveal more of the hidden properties of such inequalities.