Simple Properties Research Articles

One-Dimensional Dyadic Wavelets

One-Dimensional Dyadic Wavelets

Advancing descriptor search in materials science: feature engineering and selection strategies

A main goal of data-driven materials research is to find optimal low-dimensional descriptors, allowing us to predict a physical property, and to interpret them in a human-understandable way. In this work, we advance methods to identify descriptors out of a large pool of candidate features by means of compressed sensing. To this extent, we develop schemes for engineering appropriate candidate features that are based on simple basic properties of building blocks that constitute the materials and that are able to represent a multi-component system by scalar numbers. Cross-validation based feature-selection methods are developed for identifying the most relevant features, thereby focusing on high generalizability. We apply our approaches to an ab initio dataset of ternary group-IV compounds to obtain a set of descriptors for predicting lattice constants and energies of mixing. In particular, we introduce simple complexity measures in terms of involved algebraic operations as well as the amount of utilized basic properties.

Successful prednisolone or calcimimetic treatment of acquired hypocalciuric hypercalcemia caused by biased allosteric CaSR autoantibodies.

Biased agonism is a frontier field in GPCR research. Acquired hypocalciuric hypercalcemia (AHH) is a rare disease caused by calcium-sensing receptor (CaSR) autoantibodies, to date, showing either simple blocking or biased properties (i.e., stimulatory or blocking effects on different downstream signaling pathways). This emphasizes the importance of the Gi/o (pertussis toxin-sensitive G proteins, whose βγ subunits activate multiple signals, including ERK1/2) in regulating parathyroid hormone secretion. We here describe 3 patients with symptomatic AHH who shared characteristics with the 2 cases we previously reported as follows: (a) elderly (74-87 years at diagnosis), (b) male, (c) unexpectedly showed no other autoimmune diseases, (d) showed spontaneously fluctuating Ca levels from approximately normal to near fatally high ranges, (e) acute exacerbations could be successfully treated with prednisolone and/or calcimimetics, (f) the presence of CaSR autoantibodies that operated as biased allosteric modulators of CaSR, and (g) were likely to be conformational (i.e., recognizing and, thereby, stabilizing a unique active conformation of CaSR that activates Gq/11, activating phosphatidylinositol turnover, but not Gi/o). Our observations with these prominent commonalities may provide new insights into the phenotype and characteristics of AHH and the mechanisms by which the biased agonism of GPCRs operate.

The effect of perceptual history on the interpretation of causality.

The ability to interpret spatiotemporal contingencies in terms of causal relationships plays a key role in human understanding of the external world. Indeed, the detection of such simple properties enables us to attribute causal attributes to interactions between objects. Here, we investigated the degree to which this perception of causality depends on recent experience, as has been found for other low-level properties of visual stimuli. Participants were shown launching sequences of colliding circles with varying collision lags and were asked to report their impression of causality. We found short-term attractive and long-term repulsive and attractive effects of perceptual history on the interpretation of causality. Stimuli directly following a causal impression were more likely to be judged as causal and vice versa. However, prior judgments on less recent (>5) trials biased current perception with both positive/attractive and negative/repulsive influences. We interpret these results in terms of two potential mechanisms: adaptive temporal binding windows and updating of internal representations of causality. Overall, these results demonstrate the important role of prior experience even for causality, a fundamental building block of how we understand our world.

Acoustic Switch via a Compressible Minimal Surface Structure

Acoustic metamaterials exhibit novel physical phenomena due to their excellent properties for manipulating the propagation of sound waves. Specifically, acoustic switches are gaining increasing attention due to their enormous potential applications in acoustic sensing and acoustic logical computing. However, acoustic switch elements with simple and stable mechanical properties remain elusive. Herein, an acoustic switch based on the compressible minimal surface structure is proprosed. The acoustic switch is fabricated with a rubber‐like material, exhibiting good compression mechanical properties. The simulation and experiment results show that when the soft structure is compressed, the acoustic signal can be efficiently switched from the OFF state to the ON state. The study provides a guide to design acoustic switches, which exhibit enormous application potential in acoustic signal sensing and fault diagnosis.

PELATIHAN FOTOGRAFI PRODUK MENGGUNAKAN SMARTPHONE UNTUK MENINGKATKAN VISUAL BRANDING PADA UMKM KERAJINAN TANDUK SAPI DESA PUCANG, MAGELANG

Product photos with smartphones can be a solution for MSMEs in facing the risk of access to the market. Smartphones are now equipped with cameras and photo editing applications that are very adequate and easy to create visual branding. One of the product photos used in the Community Service (PkM) training this time is product photography. The partner of this PkM is "Toko Subur", which is a horn and souvenir handicraft business which is located in Pucang Village, Secang, Magelang. The problem raised is the limitation of soft skills in applying smartphones for product photography purposes. For this reason, the PkM Team provides a solution in the form of simple product photography training using a smartphone and other supporting properties. The method used is direct training using a smartphone or simple property for SMEs "Toko Subur" as well as joint discussions regarding the obstacles they face. The output of this PkM is the formation of soft skills for MSME actors in the field of product photography to create visual branding so that they are able to attract potential consumers and increase sales from both social media and e-commerce.

A false vacuum Skyrme model for nuclear matter

The low energy regime of quantum chromodynamics (QCD) presents enormous challenges due to its large coupling. Effective field theories, like the Skyrme model, are useful approaches to study properties of strong interaction at hadronic scales. We propose a Skyrme-type model with a self-dual sector and that treats the density of the baryonic charge as a self-interacting fluid. The dynamics reduces to Coleman’s false vacuum problem for a scalar field that is a fractional power of that density. The main result is that such a Skyrme-type model is the first one to reproduce, with good accuracy, the experimental values of radii and binding energies for a very wide range of the mass number. The robust and simple properties of the model lead to many possible generalizations with implications not only in nuclear physics but also in other areas of physics.

Eigenproperties of perception (dynamic touch) and action (phase dynamic) out of diversities

This study explores perception-action heuristics from a fundamental theoretical perspective to describe the comprehensive frameworks of movement as a process within a system dynamic. We address issues related to the identification of dynamics by using a nonrepresentational perspective, namely, functional nonlinearity. Experimental-based tools and calculation procedures for perception (dynamic touch) and action (inter-limb synchrony) revealed a basic pattern of response. The applied models and analyses strongly reflect the invariant principles of a collective structure, which could be the key to understanding complex behavioral processes with simple underlying properties. Our results provide an empirical perspective on dynamic systems and may potentially lead to a set of interconnected elements whose interactions lead to various syntheses.

Locally associated graphical models and mixed convex exponential families

The notion of multivariate total positivity has proved to be useful in finance and psychology but may be too restrictive in other applications. In this paper, we propose a concept of local association, where highly connected components in a graphical model are positively associated and study its properties. Our main motivation comes from gene expression data, where graphical models have become a popular exploratory tool. The models are instances of what we term mixed convex exponential families and we show that a mixed dual likelihood estimator has simple exact properties for such families as well as asymptotic properties similar to the maximum likelihood estimator. We further relax the positivity assumption by penalizing negative partial correlations in what we term the positive graphical lasso. Finally, we develop a GOLAZO algorithm based on block-coordinate descent that applies to a number of optimization procedures that arise in the context of graphical models, including the estimation problems described above. We derive results on existence of the optimum for such problems.

Fully recyclable and high-performance carbon fiber composites based on thermoplastic epoxy polymer

In this paper, a linear and highly polymerized thermoplastic epoxy (TPE) is treated as the matrix to prepare the fully recyclable and high performance carbon fiber reinforced TPE composites (CFRTPE). The TPE is synthesized from bisphenol A epoxy and aniline through a simple polymerization process, whose mechanical and thermal properties (tensile strength: 65 MPa, tensile modulus: 2.3 GPa, initial decomposition temperature: 365.4 °C) are comparable to that of traditional bisphenol A epoxy based thermoset. The CFRTPE composites samples could be prepared by prepreg through molding and vacuum bag method with the mild conditions (180 °C for 20 min). The mechanical and thermomechanical properties testing results show that the CFRTPE could achieve comparable and even better mechanical performance compared with the reference specimen and literature reports. Most importantly, both the full-length CFs and TPE of CFRTPE composites could be recycled conveniently and rapidly at room temperature without damaging the morphologies and mechanical properties, which indicates promising potential for practical applications.

A Universal Machine Learning Framework for Electrocatalyst Innovation: A Case Study of Discovering Alloys for Hydrogen Evolution Reaction

AbstractMassive efforts have been made to develop efficient electrocatalysts for green hydrogen production. The introduction of machine learning (ML) has brought new opportunities to the design of electrocatalysts. However, current ML studies have shown that the efficiency and accuracy of this method in electrocatalyst development are severely hindered by two major problems, high computational cost paid for electronic or geometrical structures with high accuracy, and large errors resulted from those easily accessible and relatively simple physical and chemical properties with lower level of accuracy. Here, a universal ML framework is proposed that achieves local structure optimization by using local machine learning potential (MLP) to efficiently obtain accurate structure descriptors, and by combining simple physical properties with graph convolutional neural networks, 43 high‐performance alloys are successfully screened as potential hydrogen evolution reaction electrocatalysts from 2973 candidates. More importantly, part of the best candidates identified from this framework have been verified in experiments, and one of them (AgPd) is systematically investigated by ab initio calculations under realistic electrocatalytic environments to further demonstrate the accuracy. More significantly, the computational efficiency and accuracy can be compromised with this local MLP optimized structural descriptor as the input, and a new paradigm could be established in designing high‐performance electrocatalysts.

Temperature-controlled direct dry synthesis of Co-Ni free cathode materials 0.7Li2MnO3·0.3LiFeO2 for lithium-ion batteries

Cobalt-nickel (Co-Ni) free Lithium-rich cathode material (1 − x)Li2MnO3·xLiFeO2 is more environmental friendly and cheaper than other traditional cathode materials. However, the complex synthesis method limits its large-scale practical application. This work explores a simple dry synthesis route to directly obtain 0.7Li2MnO3·0.3LiFeO2 with good electrochemical performance. Calcination temperature is a key factor controlling the phase growth. With the calcination temperature increasing, the crystallization and ordered layered structure are improved, but the LiFe5O8 spinel phase appears in X-ray diffraction (XRD) patterns from 600 ℃. The results of selected area electron diffraction (SAED), element mapping (EDS) and vibrating sample magnetometer (VSM) demonstrate convincingly the growth progress of LiFe5O8 as the temperature rises. The spinel phase deteriorates the electrochemical performance. Furthermore, it is proved that the crystallization and activity of Li2MnO3 precursor are effective to retard the formation of LiFe5O8 and enhance the electrochemical performance. The sample calcined at 600 ℃ exhibits a high initial capacity above 200 mAh g−1, but capacity fades quickly to 120 mAh g−1 after 50 cycles at 0.2 C. The sample calcined at 650 ℃ with a little LiFe5O8 delivers a low initial capacity about 160 mAh g−1, but stabilizes at 160 mAh g−1 after 50 cycles. Therefore, how to control the formation of thermally stable phase LiFe5O8 is an important issue, and it needs further investigation to achieve Co-Ni free Li-rich cathode materials with excellent performance. Significantly, the simple synthesis and acceptable electrochemical properties make the practical application of Fe-Mn based Li-rich material possible.

Michael L. Raposa Plays with Peirce, Love, and Signs: Review Essay on Theosemiotic: Religion, Reading, and the Gift of Meaning

Michael L. Raposa Plays with Peirce, Love, and Signs: Review Essay on <i>Theosemiotic: Religion, Reading, and the Gift of Meaning</i>

The precise designation of natural gas volumetric flow by measuring simple thermodynamic properties and using artificial intelligence methods

The precise designation of natural gas volumetric flow by measuring simple thermodynamic properties and using artificial intelligence methods

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound’s interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Overview of Intellectual Property Rights (IPRs)

Intellectual Property Right is exclusive right is granted by government of India for protection originality of work of inventor. Simple intellectual property right is intangible creation of human mind. In this intellectual Property right includes in Patent, Trademark, Trades crates, Industrial design, Layout design and Copyright oriented rights. Intellectual right is important for maintaining the quality, safety, efficacy of any Pharmaceutical product and services. It is certification authority and standard authority for certification and identification of product in would wide market. This intellectual property right is the rights given to people over the creation of their minds. They usually give the creator an exclusive right over the use of his/her creations for a certain period of time. Intellectual property refers to creations of the mind, inventions in artistic, literary, scientific and industrial field. It is important application for Protection of invention of inventor and maintaining the quality as well as standard of work of inventor. the present review describes The Basic concept in IPR, Objectives in IPR, Type of IPR (Patents, Trademarks, Copyrights And Related Rights, Geographical Indications, Industrial Designs, Trade Secrets, Layout Design For Integrated Circuits, Protection of New Plant Variety), Duration of Intellectual Property Rights, Concept Related Patents (Types of Patent, Tangible And Intangible Property, Novelty, Non-Obviousness, Utility, Anticipation, Prior Art, Global Perspective Of Patent System, Role Of International Organization, Indian Patent Act 1970, Patentability, Patent Infringement, Commercialization, Patent Licensing), Applications of IPR.

Some Elementary Properties of Kurzweil-Henstock-Stieltjes Integral on \(\mathbb{R}^n\)

Kurzweil-Henstock integral is a generalization of the Reimann integral. In this paper, we established the definition of Kurzweil-Henstock-Stieltjes integral on \(\mathbb{R}\)n via gauge type approach where integrand and integrator are all real-valued functions defined on a compact interval in \(\mathbb{R}\)n. Moreover, the Cauchy Criterion is established. To this end, some underlying simple properties of this integral are studied, specifically, uniqueness, linearity, monotonocity, integrability over a subset, and additivity. Results gathered in this paper may serve as a foundation to some related studies such as the notion of convergence with respect to this integral, and the formulation of the Saks-Henstock Lemma.

Simulation of the deformation diagram of a viscoelastic material based on a structural model

A serious problem in computer simulation of the stress state of polymer structures is to ensure the adequacy of the mathematical description of the mechanical properties of materials. The structural model of a viscoelastic material has a number of advantages in describing both the rheology of the material and trajectories of the material deformation. In this model, the material is described as a structure consisting of several elements with relatively simple rheological properties. Reproduction of a complex behavior of the material under alternating non-isothermal loading is ensured through the interaction of simple elements. A technique developed for modeling a viscoelastic material is intended for strength calculations of structures made of materials operating under conditions of prolonged repeated thermomechanical exposure using the finite element method. Application of the developed procedure to a polymeric material, polymethyl methacrylate (PMMA), is considered. The results of testing the material under uniaxial compression at a constant temperature are presented. The methodology and results of identification of the developed structural model using a specialized software are described. Formulas for approximation of the deformation characteristics of the material at a constant deformation rate and the time dependence of material deformation during the holding the material at a constant stress level are obtained. Approximation is an important step in identification of the material model which facilitates the systematization of the initial experimental data and their further mathematical processing. The best approximation of the deformation characteristics of a viscoelastic material is given by a hyperbolic tangent function, whereas the logarithmic function provides the best results for deformation upon exposure. Further construction of the structural model was carried out by selection of sequential parameters of bilinear rheological functions of the individual elements the model and iterative refinement of those parameters. The simulation results were compared with the experiments carried out at different strain rates and with exposure at different stress levels. We just present the results of the initial stage of the carried out experimental and theoretical studies.

On the class of an integer triangle

Any mathematics student who has ever used the cosine rule to investigate simple properties of an integer triangle will immediately have realised that the cosine of each angle of the triangle must be a rational number. It is clear, however, that the same is not in general true for the sines. In [1], it is shown how to use a property of the sines of the angles of an integer triangle to categorise the triangle as being of a particular class. In this article, we develop some of the concepts and results of [1] to derive a method for generating integer triangles of a given class. Finally, we apply our results to find all primitive integer triangles in the particular case of Heronian triangles.

Before We Can Find a Model, We Must Forget about Perfection

With Reinforcement Learning we assume that a model of the world does exist. We assume furthermore that the model in question is perfect (i.e. it describes the world completely and unambiguously). This article will demonstrate that it does not make sense to search for the perfect model because this model is too complicated and practically impossible to find. We will show that we should abandon the pursuit of perfection and pursue Event-Driven (ED) models instead. These models are generalization of Markov Decision Process (MDP) models. This generalization is essential because nothing can be found without it. Rather than a single MDP, we will aim to find a raft of neat simple ED models each one describing a simple dependency or property. In other words, we will replace the search for a singular and complex perfect model with a search for a large number of simple models.