Wide Variety Of Scientific Fields Research Articles

Социальный менталитет и ментальное противоборство: психолого-педагогические аспекты

The study of the issues associated with modern mental confrontation affects a wide variety of scientific fields, and not only the humanities. However, the pedagogical aspects of the issue of successfully countering the means of mental intervention carried out by unfriendly countries against our country using psychotronic, organizational, information weapons, and social manipulation remain practically undeveloped. The purpose of the research is to substantiate the necessity and possibilities of studying national and social immunity, mentality, taking into account the specifics of the conditions of modern mental confrontation. The theoretical significance of the article's material is related to the fact that it contains a definition of social mentality, taking into account its specifics in relation to the community of employees of the internal affairs bodies of the Ministry of Internal Affairs of Russia, concretized on the basis of an analysis of prevailing in domestic science (primarily – philosophy) approaches to the definition and characterization of mentality, mentality, the conclusions are substantiated that allow us to purposefully solve tasks aimed at achieving the goal of preserving and progressive development of mentality, national mentality and social mentality of individual professional communities as its component. The practical significance of the presented research results consists not only in the fact that it emphasizes the importance of creating a theoretical and methodological basis for conducting pedagogical research on the identified problems, but also shows the importance of developing methodological support for educational (primarily educational) activities of faculty and senior staff of departmental universities, ensuring the resolution of crucial issues at the present stage of development. There are contradictions in our country, which has positive social consequences. The originality of the work is determined by the fact that this is one of the few studies aimed at solving the problems of mental confrontation using the achievements of pedagogical science and practice, in which, based on the analysis of the results of research conducted in various humanitarian fields, provisions are formulated that contribute to the resolution of these issues.

UV-Visible study on the solubilization of solvatochromic-origin dyes in various micellar systems

The evaluation of interactions between surfactants and dyes is an important challenge for a wide variety of scientific fields, contributing significantly to industrial development. Understanding the interactions between dyes of various classes and surfactants of different types can improve many processes in the future, such as enabling better capture of harmful dyes as industrial waste, thus facilitating their neutralization. In the pharmaceutical industry, there is a constant need to improve the design of new carriers for surfactant-stabilized drugs. Various dyes are very often successfully used as model molecules for therapeutic and/or diagnostic agents. In this regard, the present study evaluated the micellar solubilization of two dyes with different types of solvatochromism - Disperse Orange 3 and 2,6-dichloro-4-(2,4,6-triphenyl-1-pyridino)phenolate. A wide range of surfactants of different chemical natures - ionic (including cationic, anionic, and amphoteric) and nonionic ones - were used to form supramolecular micellar structures. Some shifts in the position of the absorption bands for ionic surfactants were observed, and the results suggest that they have a better ability for micellar solubilization, while having a higher solvatochromic behavior. The results were also compared with spectra for pure solvents allowing a broader determination of the resulting micellar aggregates’ polarity and indicating their place in the widely used ET(30) scale. Experimental studies indicate that both model molecules are not localized in the core, but are located in the more external part of the micelle. This approach allowed us to assess the solubilization of the dyes using each representative surfactant, thereby providing insights into their interaction with micellar systems. This comprehensive analysis allows for a more holistic understanding of the solubilization process and provides insights into the influence of surfactant charge on the solubilization efficiency. Moreover, the extended set of the surfactants as well as chromophores exhibiting different solvatochromic behavior takes account of our considerations and allows a deeper look at the solvatochromic phenomenon in the context of micelle-chromophore interactions.

Major Security Issue That Facing Social Networks with Its Main Defense Strategies

The Social Network Service "SNS" has enabled significant advancements in a wide variety of scientific fields, and as a result, it has become an extremely popular subject in both academia and business. SNSs can be extremely beneficial to users because they eliminate economic and geographical barriers and can be used for job searching, entertainment and education. Regardless of the economic and social benefits, protecting businesses and users' security and privacy remains a critical issue that must be addressed. It is critical to address and evaluate social network service challenges, as they vary according to the variety of SNS sites. Thus, by discussing SNS challenges alongside available and potential solutions, users, developers, and businesses can identify relevant and timely responses to specific threats, resulting in the best SNS-based services possible. The objective of this article is to discuss the inherent challenges of social networking sites and some critical solutions for resolving them. We extracted and analyzed seminal papers to add to the corpus of literature by focusing on several critical challenges in the social network service domain and shedding light on how these challenges affect a variety of domains, including users, sites, and business. The most frequently mentioned difficulties concerned privacy risks, anonymity risks, malware, spam, identity theft, phishing, business data, social content, technical issues, and psychological difficulties. By incorporating previously discovered solutions, this paper addressed these issues. The implications for both researchers and practitioners have been discussed.

Uncertainty calculation methodologies in microflow measurements: Comparison of GUM, GUM-S1 and Bayesian approach

The importance of measurement quality cannot be over emphasized in medical applications, as one is dealing with life issues and the wellbeing of society, from oncology to new-borns, and more recently to patients of the COVID-19 pandemic. In all these dire situations, the accuracy of fluid delivered according to a prescribed dose can be critical.Microflow applications are growing in importance for a wide variety of scientific fields, namely drug development and administration, Organ-on-a-Chip, or bioanalysis, but accurate and reliable measurements are a tough challenge in micro-to-femto flow operating ranges, from 2.78 × 10−4 mL/s down to 2.78 × 10−7 mL/s (1000 μL/h down to 1 μL/h). Several sources of error have been established such as the mass measurement, the fluid evaporation dependent on the gravimetric methodology implemented, the tube adsorption and the repeatability, believed to be closely related to the operating mode of the stepper motor and drive screw pitch of a syringe pump. In addition, the difficulty in dealing with microflow applications extends to the evaluation of measurement uncertainty which will qualify the quality of measurement. This is due to the conditions entailed when measuring very small values, close to zero, of a quantity such as the flow rate which is inherently positive. Alternative methods able to handle these features were developed and implemented, and their suitability will be discussed.

Thermometric Characterization of Fluorescent Nanodiamonds Suitable for Biomedical Applications

Nanodiamonds have been studied for several biomedical applications due to their inherent biocompatibility and low cytotoxicity. Recent investigations have shown perspectives in using fluorescent nanodiamonds as nanothermometers because of their optical properties’ dependence on temperature. Easy and accurate localized temperature sensing is essential in a wide variety of scientific fields. Our work demonstrated how the fluorescence spectrum of high-pressure high-temperature fluorescent nanodiamonds of three different sizes: 35 nm, 70 nm and 100 nm, changes with temperature within an important biological temperature range (25 °C to 60 °C). Taking advantage of this phenomenon, we obtained nanothermic scales (NS) from the zero phonon lines (ZPL) of the NV0 and NV− colour centres. In particular, the 100 nm-sized features the more intense fluorescence spectra whose linear dependence with temperature achieved 0.98 R2 data representation values for both NV0 and NV−. This model predicts temperature for all used nanodiamonds with sensitivities ranging from 5.73% °C−1 to 6.994% °C−1 (NV0) and from 4.14% °C−1 to 6.475% °C−1 (NV−). Furthermore, the non-cytotoxic interaction with HeLa cells tested in our study enables the potential use of fluorescence nanodiamonds to measure temperatures in similar nano and microcellular aqueous environments with a simple spectroscopic setup.

Bayes linear analysis for ordinary differential equations

Differential equation models are used in a wide variety of scientific fields to describe the behaviour of physical systems. Commonly, solutions to given systems of differential equations are not available in closed-form; in such situations, the solution to the system is generally approximated numerically. The numerical solution obtained will be systematically different from the (unknown) true solution implicitly defined by the differential equations. Even if it were known, this true solution would be an imperfect representation of the behaviour of the real physical system that it was designed to represent. A Bayesian framework is proposed which handles all sources of numerical and structural uncertainty encountered when using ordinary differential equation (ODE) models to represent real-world processes. The model is represented graphically, and the graph proves to be useful tool, both for deriving a full prior belief specification and for inferring model components given observations of the real system. A general strategy for modelling the numerical discrepancy induced through choice of a particular solver is outlined, in which the variability of the numerical discrepancy is fixed to be proportional to the length of the solver time-step and a grid-refinement strategy is used to study its structure in detail. A Bayes linear adjustment procedure is presented, which uses a junction tree derived from the originally specified directed graphical model to propagate information efficiently between model components, lessening the computational demands associated with the inference. The proposed framework is illustrated through application to two examples: a model for the trajectory of an airborne projectile moving subject to gravity and air resistance, and a model for the coupled motion of a set of ringing bells and the tower which houses them.

О некоторых моментах бинарного взаимодействия науки и философии

The article deals with the main problems and issues that arise in the process of interaction between scientific and philosophical knowledge. The main methodological tool of the research is the concept of binary analysis, developed by the author in recent years. The work can be useful both for scientists working in a wide variety of scientific fields, and for philosophers. And of course, teachers who teach sciences and philosophical disciplines.

Bayesian tests for circular uniformity

Circular data are data measured in angles or directions, which occur in a wide variety of scientific fields. An often investigated hypothesis is that of circular uniformity, or isotropy. Frequentist methods for assessing the circular uniformity null hypothesis exist, but do not allow the user faced with an insignificant result to distinguish lack of power from support for the null hypothesis. Bayesian hypothesis tests, which solve this issue and several others, are developed here. They are easy to compute and perform well, which is shown in a simulation. Two alternative hypotheses are considered. One is based on the von Mises distribution and performs well against unimodal alternatives. Another is based on a kernel density, which acts as an omnibus test against all other scenarios. Assessing the performance of the tests using different priors, it is shown that they are powerful and allow more elaborate conclusions than classical tests of circular uniformity.

Two-Photon-Induced [2 + 2] Cycloaddition of Bis-thymines: A Biocompatible and Reversible Approach.

Despite having great value across a wide variety of scientific fields, two-photon polymerizations currently suffer from two significant problems: the need for photoinitiators, which generate toxic side products, and the irreversibility of the process. Hence, the design of a versatile approach that circumvents these issues represents a major scientific challenge. Herein, we report a two-photon absorption strategy where reversible [2 + 2] cycloaddition of bis-thymines was achieved without the need for any photoinitiator. The cycloaddition and cycloreversion reactions could be induced by simply changing the irradiation wavelength, and repeated writing and erasing cycles were performed. The simplicity, reversibility, and biocompatibility of this strategy open up a whole new toolbox for applications across a wide variety of scientific fields.

An efficient K-means clustering algorithm for tall data

The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial topic in unsupervised learning. Cluster analysis algorithms are a key element of exploratory data analysis and, among them, the K-means algorithm stands out as the most popular approach due to its easiness in the implementation, straightforward parallelizability and relatively low computational cost. Unfortunately, the K-means algorithm also has some drawbacks that have been extensively studied, such as its high dependency on the initial conditions, as well as to the fact that it might not scale well on massive datasets. In this article, we propose a recursive and parallel approximation to the K-means algorithm that scales well on the number of instances of the problem, without affecting the quality of the approximation. In order to achieve this, instead of analyzing the entire dataset, we work on small weighted sets of representative points that are distributed in such a way that more importance is given to those regions where it is harder to determine the correct cluster assignment of the original instances. In addition to different theoretical properties, which explain the reasoning behind the algorithm, experimental results indicate that our method outperforms the state-of-the-art in terms of the trade-off between number of distance computations and the quality of the solution obtained.

Working to the Beat: A Self-Regulatory Framework Linking Music Characteristics to Job Performance

With changes in musical technology, it is increasingly common for employees to listen to music at work. Research from a wide variety of scientific fields has demonstrated that music affects our beh...

Deep learning enabled inverse design in nanophotonics

AbstractDeep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.

Documented composition of cadaveric skeletal remains in the University of Cape Town Human Skeletal Collection, South Africa

The University of Cape Town Human Skeletal Collection is one of the oldest documented skeletal collections in South Africa. The skeletal remains housed in this collection have been used for a variety of studies, but no complete description of the composition of the collection currently exists in the published literature. The aim of this study is to provide a detailed description of the composition of the cadaveric collection to encourage use thereof by national and international researchers.As of 2016, the collection contained the skeletal remains of 343 cadaveric individuals, 163 of which have mostly complete crania and postcrania. The male to female ratio of the collection is approximately 3:2. White individuals constitute 70% of the collection, with smaller contributions by individuals of the Black and Coloured population groups. Most of the individuals in the collection were 60–90 years old at death, though the mean age of the Black and Coloured groups was much lower, likely due to differences in origin of the cadavers received by the Faculty of Health Sciences. The most common reported cause of death in the collection was circulatory disease, followed by neoplasms and respiratory disease.Despite differences in the composition of this collection and the provincial and national demographic profiles, the collection remains an invaluable resource for training and research in a wide variety of scientific fields.

The time fourth-order compact ADI methods for solving two-dimensional nonlinear wave equations

Nonlinear wave equation is extensively applied in a wide variety of scientific fields, such as nonlinear optics, solid state physics and quantum field theory. In this paper, two high-performance compact alternating direction implicit (ADI) methods are developed for the nonlinear wave equations. The first scheme is developed a three-level nonlinear difference scheme for nonlinear wave equations, where in x-direction, series of linear tridiagonal systems are solved by Thomas algorithm, while in y-direction, nonlinear algebraic system are computed by Newton’s iterative method. In contrast, the second scheme is linear, and permits the multiple uses of the Thomas algorithm in both x- and y-directions, thus it saves much time cost. By using the discrete energy analysis method, it is shown that both the developed schemes can attain numerical accuracy of order O(τ4+hx4+hy4) in H1-norm. Meanwhile, by the fixed point theorem and symmetric positive-definite properties of coefficient matrix, it is proved that they are both uniquely solvable. Besides, the proposed schemes are extended to the numerical solutions of the coupled sine-Gordon wave equations and damped wave equations. Finally, numerical results confirm the convergence orders and exhibit efficiency of our algorithms.

Bypassing the Kohn-Sham equations with machine learning

Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn–Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.

Optimization Methods for the Design of Sensitive Surface ESR Resonators

Electron spin resonance (ESR) is a powerful spectroscopic technique that has many applications in a wide variety of scientific fields, including chemistry, biology, materials science, and physics. One significant drawback of conventional ESR, however, is its relatively low sensitivity compared to other spectroscopic techniques. Arguably, the most dominant element affecting ESR sensitivity is the microwave resonator used to pick up the ESR signal of the spins. Traditionally, ESR mostly employs a limited set of resonator configurations (e.g., rectangular cavity, dielectric, or loop-gap resonator) that are suboptimal with respect to a wide range of samples. In principle, a smart resonator design can be used to optimize spin sensitivity for a given sample’s properties. In this work, we make use of an efficient Genetic Algorithm (GA) approach to numerically solve, analyze, and optimize a unique class of surface microresonators. The GA is based on a method of moments code, customized directly to render the complexity of a particular resonator’s geometries in our search. The main purposes of the algorithm are to routinely generate more sensitive microresonators, optimized for a predefined sample’s dimensions, and to study the functional relations between the devices’ resonance frequency, quality, and filling factors and their topology, in order to reach a rational optimal design. Preliminary results associated with new, unique, and sensitive surface microresonators are shown and analyzed. Such resonators are cheap and easy to produce on a mass scale with an arbitrary surface geometry.

Bayesian estimation and hypothesis tests for a circular Generalized Linear Model

Motivated by a study from cognitive psychology, we develop a Generalized Linear Model for circular data within the Bayesian framework, using the von Mises distribution. Although circular data arise in a wide variety of scientific fields, the number of methods for their analysis is limited. Our model allows inclusion of both continuous and categorical covariates. In a frequentist setting, this type of model is plagued by the likelihood surface of its regression coefficients, which is not logarithmically concave. In a Bayesian context, a weakly informative prior solves this issue, while for other parametersnoninformative priors are available. In addition to an MCMC sampling algorithm, we develop Bayesian hypothesis tests based on the Bayes factor for both equality and inequality constrained hypotheses. In a simulation study, it can be seen that our method performs well. The analyses are available in the package CircGLMBayes. Finally, we apply this model to a dataset from experimental psychology, and show that it provides valuable insight for applied researchers. Extensions to dependent observations are within reach by means of the multivariate von Mises distribution.

An efficient approximation to the K-means clustering for massive data

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial settings and the large number of distance computations that it can require to converge, the K-means algorithm remains as one of the most popular clustering methods for massive datasets. In this work, we propose an efficient approximation to the K-means problem intended for massive data. Our approach recursively partitions the entire dataset into a small number of subsets, each of which is characterized by its representative (center of mass) and weight (cardinality), afterwards a weighted version of the K-means algorithm is applied over such local representation, which can drastically reduce the number of distances computed. In addition to some theoretical properties, experimental results indicate that our method outperforms well-known approaches, such as the K-means++ and the minibatch K-means, in terms of the relation between number of distance computations and the quality of the approximation.

Selective superhydrophilic/phobic coating using capillary pressure for positive-displacement nanoliter dispensing

Owing to the fact that liquid-dispensing devices are used in a wide variety of scientific fields, including chemistry, biology, pharmacology, and mechanics, droplet dispensing has come to be regarded as a key technology with respect to micro/nanoengineering. Positive-placement dispensing technology is being used widely because it allows for a high degree of controllability without requiring a complex dispensing system; however, adhesion and slip-related issues limit the performance of this technology. In this letter, we report a technique for the fabrication of selective superhydrophilic/phobic coatings in syringe-type positive-displacement dispensing nozzle tips. The superhydrophilic capillary in the nozzle tip was coated selectively with superhydrophobic materials by exploiting the difference in the capillary pressure. The surface of the front part of the capillary was made superhydrophobic; it thus allowed the liquid to flow without adhesion. In contrast, the back part was made superhydrophilic; it retarded the flow of the liquid, holding it in place. Together, the two surfaces minimized the volume of the dispensed droplets. High-speed images of the dispensed liquid were taken to compare the two droplet-dispensation processes. It was found that the volume of the water droplets dispensed from the selectively coated nozzle tip was as low as 27nL and much smaller than that of the droplets dispensed from the superhydrophobic nozzle tip.