Scientific Applications Research Articles

Solutions for a Generalized Type of the Fokas-Lenells Equation.

This work investigates the following generalization of the Fokas-Lenells equation. ıqt+A(t)qxx+B(t)qxt+C(t)|q|2q+ıD(t)|q|2qx=ı[H(t)qx+F(t)(|q|2q)x+G(t)(|q|2)xq] which is a Schrdinger-type equation with applications in theory of communications. Here, the coefficients are variables and depend on the temporal variable t. The improved tanh-coth method is used to obtain exact solutions for it in a general form. If the coefficients turn constants, the equation is known as the standard Fokas-Lenells equation (FLE) which has several applications in nonlinear science. As a particular case, novel soliton solutions, chirped solutions, and the respective chirps associated with them are derived for (FLE). Also, the work explores the behaviour of the solutions when the coefficients change in time, obtaining novel structures of the solutions which help understand in a better way the phenomenon described by the (FLE). We show the graphs of some of the solutions with the aim to compare the two cases, variable and constant coefficients. Finally, some conclusions are given.

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

Fiber-interfaced hollow-core light cage: a platform for on-fiber-integrated waveguides.

Here, we demonstrate the realization of hollow-core light cages (LCs) on commercial step-index fibers using 3D nanoprinting, resulting in fully fiber-integrated devices. Two different light cage geometries with record-high aspect ratio strands and unique sidewise access to the core have been implemented, exhibiting excellent optical and mechanical properties. These achievements are based on the use of 3D nanoprinting to fabricate light cages and stabilize them with customized support elements. Overall, this approach results in novel, to the best of our knowledge, fiber-interfaced hollow-core devices that combine several advantages in a lab-on-a-fiber platform that is particularly useful for diffusion-related applications in environmental sciences, nanosciences, and quantum technologies.

Discussion: “A Multi-Objective Comprehensive Evaluation of Heat Transfer Performance of a Direct-Contact Heat Exchanger” [J. Thermal Science and Engineering Applications, 2023, 15 (1), Article No. (011014)

Abstract The units of entransy dissipation (G) in Nomenclature are wrong. Also, an opinion on the concept of entransy is presented.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Multienzyme Mimicking Cascade Mn3O4 Catalyst to Augment Reactive Oxygen Species Elimination and Colorimetric Detection: A Study of Phase Variation upon Calcination Temperature.

Over decades, nanozyme has served as a better replacement of bioenzymes and fulfills most of the shortcomings and intrinsic disadvantages of bioenzymes. Recently, manganese-based nanomaterials have been highly noticed for redox-modulated multienzyme mimicking activity and wide applications in biosensing and biomedical science. The redox-modulated multienzyme mimicking activity was highly in tune with their size, surface functionalization, and charge on the surface and phases. On the subject of calcination temperature to Mn3O4 nanoparticles (NPs), its phase has been transformed to Mn2O3 NPs and Mn5O8 NPs upon different calcination temperatures. Assigning precise structure-property connections is made easier by preparing the various manganese oxides in a single step. The present study has focused on the variation of multienzyme mimicking activity with different phases of Mn3O4 NPs, so that they can be equipped for multifunctional activity with greater potential. Herein, spherical Mn3O4 NPs have been synthesized via a one-step coprecipitation method, and other phases are obtained by direct calcination. The calcination temperature varies to 100, 200, 400, and 600 °C and the corresponding manganese oxide NPs are named M-100, M-200, M-400, and M-600, respectively. The phase transformation and crystalline structure are evaluated by powder X-ray diffraction and selected-area electron diffraction analysis. The different surface morphologies are easily navigated by Fourier transform infrared, field-emission scanning electron microscopy, and high-resolution transmission electron microscopy analysis. Fortunately, for the mixed valence state of Mn3O4 NPs, all phases of manganese oxide NPs showed multienzyme mimicking activity including superoxide dismutase (SOD), catalase, oxidase (OD), and peroxidase; therefore, it offers a synergistic antioxidant ability to overexpose reactive oxygen species. Mn3O4 NPs exhibited good SOD-like enzyme activity, which allowed it to effectively remove the active oxygen (O2•-) from cigarette smoke. A sensitive colorimetric sensor with a low detection limit and a promising linear range has been designed to detect two isomeric phenolic pollutants, hydroquinone (H2Q) and catechol (CA), by utilizing optimized OD activity. The current probe has outstanding sensitivity and selectivity as well as the ability to visually detect two isomers with the unaided eye.

Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development.

RETRACTED: Zhong et al. Fractal Behavior of Particle Size Distribution in the Rare Earth Tailings Crushing Process under High Stress Condition. Appl. Sci. 2018, 8, 1058

The Applied Sciences Editorial office retracts the article “Fractal Behavior of Particle Size Distribution in the Rare Earth Tailings Crushing Process under High Stress Condition” [...]

A Virtual Exchange to Boost Students’ Intercultural Communication Skills in English

. How can universities enhance their students’ internationalisation at home? The need to find teaching methodologies that boost students’ intercultural communication skills without travelling abroad has motivated the present study, carried out by two English lecturers from Florida Universitaria in Spain and Jade University of Applied Sciences in Germany. Research was divided into two stages: first, a deep analysis of the intercultural experiences organised in the past at both universities; and second, the implementation of virtual encounters with students from different cultures as tools to promote interculturality at the home university. This paper aims at showing the impact of those exchanges on boosting students’ intercultural communication in English. Two groups of undergraduate students, during their English classes, worked synchronously on topics related to essential aspects of intercultural communication. The main teaching objectives of the Virtual Intercultural Communication (VIC) exchange were to increase participants’ motivation for knowing other cultures, to raise their intercultural awareness, to foster their capacity to change perspectives, and to improve students’ English language skills and their digital abilities. Through the outcomes achieved, students’ self-reported feedback, and lecturers’ observation, it can be concluded that virtual exchanges are effective tools to improve students’ intercultural communication while they meet the English learning objectives.

Volatile Organic Compounds (VOCs) in Heritage Environments and Their Analysis: A Review

In the recent years, there has been an increased interest in indoor air quality in heritage environments, specifically in relation to volatile organic compounds (VOCs). These could originate from objects, furnishings, visitors and staff, as well as from olfactory exhibitions. This interest led to a number of studies investigating the “typical” emissions for diverse materials and their impact on the surrounding environment. The analysis of volatile compounds emitted by objects helps in the characterization of the material composition, its conservation history or its degradation processes. This contribution reviews how volatiles are emitted from objects and the commonly used sampling techniques for heritage science applications. A variety of methods are available, from bulk air sample collection to preconcentration using samplers. The commonly studied object types contributing to indoor VOCs are discussed. These include emissions from heritage objects, conservation products, furnishing materials and display cases. Furthermore, olfactory exhibitions are discussed in terms of indoor air quality. Finally, the findings are compared with the current guidelines on indoor volatile concentrations.

Improving the open cluster census

Context. The census of open clusters has exploded in size thanks to data from the Gaia satellite. However, it is likely that many of these reported clusters are not gravitationally bound, making the open cluster census impractical for many scientific applications. Aims. We aim to test different physically motivated methods for distinguishing between bound and unbound clusters, using them to create a cleaned star cluster catalogue. Methods. We derived completeness-corrected photometric masses for 6956 clusters from our earlier work. Then, we used these masses to compute the size of the Roche surface of these clusters (their Jacobi radius) and distinguish between bound and unbound clusters. Results. We find that only 5647 (79%) of the clusters from our previous catalogue are compatible with bound open clusters, dropping to just 11% of clusters within 250 pc. Our catalogue contains 3530 open clusters in a more strongly cut high-quality sample of objects. The moving groups in our sample show different trends in their size as a function of age and mass, suggesting that they are unbound and undergoing different dynamical processes. Our cluster mass measurements constitute the largest catalogue of Milky Way cluster masses to date, which we also use for further science. Firstly, we inferred the mass-dependent completeness limit of the open cluster census, showing that the census is complete within 1.8 kpc only for objects heavier than 230 M⊙. Next, we derived a completeness-corrected age and mass function for our open cluster catalogue, including estimating that the Milky Way contains a total of 1.3 × 105 open clusters, only ∼4% of which are currently known. Finally, we show that most open clusters have mass functions compatible with the Kroupa initial mass function. Conclusions. We demonstrate Jacobi radii for distinguishing between bound and unbound star clusters, and publish an updated star cluster catalogue with masses and improved cluster classifications.

Computing edge version of metric dimension of certain chemical networks

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression.

Eco-friendly and biocompatible gelatin plasmonic filters for UV-vis-NIR light

The quest for environmentally sustainable materials spans many fields and applications including optical materials. Here, we present the development of light filters using a gelatin-based nanocomposite. Owing to the plasmonic properties of metallic nanoparticles (NPs), strong light-matter interactions, these filters can be customized across the UV-Visible-NIR spectrum. The filters are designed for modular use, allowing for the addition or removal of desired spectral ranges. Moreover, the nanocomposites are composed of biodegradable and biocompatible materials which highlight the intersection of chemistry and ecological awareness for the exploration of new eco-friendly alternatives. These plasmonic gelatin-based filters block light due to the Localized Surface Plasmon Resonance (LSPR) of the NPs and can be tailored to meet various requirements, akin to a diner selecting options from a menu. This approach is inspired by culinary techniques, and we anticipate it will stimulate further exploration of biomaterials for applications in optics, materials science or electronics.

A sentiment corpus for the cryptocurrency financial domain: the CryptoLin corpus

AbstractThe objective of this paper is to describe Cryptocurrency Linguo (CryptoLin), a novel corpus containing 2683 cryptocurrency-related news articles covering more than a three-year period. CryptoLin was human-annotated with discrete values representing negative, neutral, and positive news respectively. Eighty-three people participated in the annotation process; each news title was randomly assigned and blindly annotated by three human annotators, one in each different cohort, followed by a consensus mechanism using simple voting. The selection of the annotators was intentionally made using three cohorts with students from a very diverse set of nationalities and educational backgrounds to minimize bias as much as possible. In case one of the annotators was in total disagreement with the other two (e.g., one negative vs two positive or one positive vs two negative), we considered this minority report and defaulted the labeling to neutral. Fleiss’s Kappa, Krippendorff’s Alpha, and Gwet’s AC1 inter-rater reliability coefficients demonstrate CryptoLin’s acceptable quality of inter-annotator agreement. The dataset also includes a text span with the three manual label annotations for further auditing of the annotation mechanism. To further assess the quality of the labeling and the usefulness of CryptoLin dataset, it incorporates four pretrained Sentiment Analysis models: Vader, Textblob, Flair, and FinBERT. Vader and FinBERT demonstrate reasonable performance in the CryptoLin dataset, indicating that the data was not annotated randomly and is therefore useful for further research1. FinBERT (negative) presents the best performance, indicating an advantage of being trained with financial news. Both the CryptoLin dataset and the Jupyter Notebook with the analysis, for reproducibility, are available at the project’s Github. Overall, CryptoLin aims to complement the current knowledge by providing a novel and publicly available Gadi and Ángel Sicilia (Cryptolin dataset and python jupyter notebooks reproducibility codes, 2022) cryptocurrency sentiment corpus and fostering research on the topic of cryptocurrency sentiment analysis and potential applications in behavioral science. This can be useful for businesses and policymakers who want to understand how cryptocurrencies are being used and how they might be regulated. Finally, the rules for selecting and assigning annotators make CryptoLin unique and interesting for new research in annotator selection, assignment, and biases.

Speciális Nyomozástámogató és Viselkedéselemző tevékenység

Aim: The aim of this study is to present the Special Investigation Support and Behavioural Analysis activities established in our country. Methodology: In the past decades, profiling has developed considerably, both in terms of scientific results and practical application, and our country has adapted to these modernisation efforts. Findings: The relevance of the methods developed and applied at national level is supported by international research trends, results and practical application. Value: In addition to presenting for the first time the organisational functioning and methods of special and behavioural analysis-based investigative support in Hungary, the present study raises the need for further development of this activity, which is proposed to be achieved through closer integration of theory and practice.

Darcy–Brinkman model for Boussinesq–Stokes suspension tetra dusty nanofluid flow over stretching/shrinking sheet with suction/injection

This study examines the Darcy–Brinkman model for 2-D magnetohydrodynamic fluid flow across porous stretching/shrinking surfaces. We consider water as a traditional base fluid in which the Al2O3, Silver (Ag), Titanium dioxide (TiO2 ), and Gold (Au) nanoparticles are submerged in preparation of a tetra-dusty nanofluid. The novelty of the present problem is to examine the influence of Boussinesq-Stokes suspension tetra dusty nanofluid flow with the Darcy–Brinkman model. The governing nonlinear partial differential equations are transferred to a set of ordinary differential equations through suitable similarity conversions. The methodology, the governing equations of fluid and dusty phases is transformed into nonlinear ODEs using similarity transformations. The findings demonstrate that the tetra nanofluid phase heat and mass transpiration performance is greater compared to the dusty phase. Developing tetra dusty nanofluid has significantly upsurged the heat transmission for manufacturing with numerous applications in engineering, biological, and physical sciences.

Microwave Photon Detection at Parametric Criticality

The detection of microwave fields at single-photon power levels is a much-sought-after technology, with practical applications in nanoelectronics and quantum information science. Here we demonstrate a simple yet powerful criticality-enhanced method of microwave photon detection by operating a magnetic-field-tunable Kerr Josephson parametric amplifier at the border of a first-order phase transition and close to the critical point. We obtain an efficiency of 73% and a dark-count rate of 167 kHz, corresponding to a responsivity of 1.3×1017W−1 and noise-equivalent power of 3.28 zW/Hz. We verify the single-photon operation by extracting the Poissonian statistics of a coherent probe signal. Published by the American Physical Society 2024

Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

E-learning provides a flexible way of learning, which can provide corresponding learning content and tasks according to students’ interests, abilities and learning progress, so that students can choose learning content and learning progress more independently. In traditional teaching methods, teachers need to spend a lot of time and energy to evaluate students’ learning. The e-learning system can automatically record students’ learning behavior and learning results, and give feedback and evaluation in time during the learning process. Automatic speech recognition systems have been widely applied in various fields, and the demand for more user-friendly and convenient user operating systems is becoming increasingly urgent. This article proposes an English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm to improve recognition accuracy and stability, and applies it to the computer field. Label inference and optimization for speech recognition were carried out using the conditional random field algorithm. By establishing the correlation between features and labels, label inference was performed on the speech signal, which was compared with existing speech recognition systems to evaluate whether the recognition accuracy and stability of the new system had been improved. The results show that the English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm exhibits high performance in terms of recognition accuracy and stability. Compared with traditional speech recognition systems, this system has made significant improvements in the application of computer science.

Promoting planting in front gardens: a systematic approach to intervention development.

Planting in front gardens is associated with a range of human and environmental health benefits. Effective interventions aimed at cultivating this practice are, however, hampered by the paucity of theory- and evidence-based behavioural research in this context. This study aims to systematically determine a set of behaviour change interventions likely to be effective at promoting planting in front gardens amongst UK householders. The Behaviour Change Wheel framework was applied. Behavioural systems mapping was used to identify community actors relevant to front gardening. Potential behavioural influences on householders' front gardening were identified using the Capability, Opportunity, Motivation, Behaviour model. Using peer-reviewed scientific findings as evidence, behavioural influences were systematically linked to potential intervention strategies, behaviour change techniques and real-world implementation options. Finally, intervention recommendations were refined through expert evaluations and local councillor and public stakeholder feedback, evaluating them against the Acceptability, Practicability, Effectiveness, Affordability, Side effects and Equity criteria in a UK implementation context. This study formulated 12 intervention recommendations, implementable at a community level, to promote front gardening. Stakeholder feedback revealed a preference for educational and supportive (social and practical) strategies (e.g., community gardening workshops, front gardening 'starter kits') over persuasive and motivational approaches (e.g., social marketing, motivational letters from the council to householders). Householders' front gardening behaviour is complex and influenced by the behaviour of many other community actors. It also needs to be understood as a step in a continuum of other behaviours (e.g., clearing land, gardening, waste disposal). This study demonstrates the application of behavioural science to an understudied implementation context, that is, front gardening promotion, drawing on a rigorous development process promoting a transparent approach to intervention design. Stakeholder consultation allowed relevance, feasibility and practical issues to be considered. These improve the likely effectiveness of interventions in practice. The next steps include evaluating the proposed interventions in practice.

Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction

Abstract Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction, which could be described by the Lipkin-Meshkov-Glick (LMG) model. We optimize the squeezing process, encoding process, and anti-squeezing process, finding that the two particular cases of the LMG model, one-axis twisting and two-axis twisting outperform in robustness and precision, respectively. Moreover, we propose a Floquet driving method to realize equivalent time reverse in the atomic system, which leads to high performance in precision, robustness, and operability. Our study sets a benchmark in achieving high precision and robustness in atomic nonlinear interferometry.