Ubiquitous Applications Research Articles

Violation of Neumann Problem’s Solvability Condition for Poisson Equation, Involved in the Non-Linear PDEs, Containing the Reaction-Diffusion-Convection-Type Equation, at Numerical Solution by Direct Method

In this paper, we consider the 3D problem of laser-induced semiconductor plasma generation under the action of the optical pulse, which is governed by the set of coupled time-dependent non-linear PDEs involving the Poisson equation with Neumann boundary conditions. The main feature of this problem is the non-linear feedback between the Poisson equation with respect to induced electric field potential and the reaction-diffusion-convection-type equation with respect to free electron concentration and accounting for electron mobility (convection’s term). Herein, we focus on the choice of the numerical method for the Poisson equation solution with inhomogeneous Neumann boundary conditions. Despite the ubiquitous application of such a direct method as the Fast Fourier Transform for solving an elliptic problem in simple spatial domains, we demonstrate that applying a direct method for solving the problem under consideration results in a solution distortion. The reason for the Neumann problem’s solvability condition violation is the computational error’s accumulation. In contrast, applying an iterative method allows us to provide finite-difference scheme conservativeness, asymptotic stability, and high computation accuracy. For the iteration technique, we apply both an implicit alternating direction method and a new three-stage iteration process. The presented computer simulation results confirm the advantages of using iterative methods.

Performance Analysis of Downlink Multisatellite Joint Service System Toward SAGOI-Net

With the rapid development of communication, the future Internet of Things will greatly expand its coverage to form the space, air, ground, and ocean integrated network (SAGOI-Net) and provide globally ubiquitous applications and services. In SAGOI-Net, the existing research work focused on system design and neglected the theoretical analysis of system performance. Furthermore, the Nakagami-m fading model can fit the experimental data better than the Rayleigh and Rice fading models. Also, the Nakagami-m fading model can adapt to the SAGOI-Net environment by changing the value of the shaping parameter m. However, there is no work to analyze the performance of SAGOI-Net using Nakatomi-m fading. Therefore, in this paper, the synchronous downlink system is theoretically analyzed using the Nakagami-m fading model. However, Then, the statistical characteristics of multiple access interference (MAI) and MAI-plus noise are theoretically derived. Furthermore, the accurate expression of the Bit-Error Rate (BER) for the fading model is also derived. After that, we establish the relationship between the number of users, BER, and the information transmission rate to optimize energy efficiency. Finally, simulation results verify that the theoretical derivation results are reliable and effective.

Characterization of just one atom using synchrotron X-rays.

Since the discovery of X-rays by Roentgen in 1895, its use has been ubiquitous, from medical and environmental applications to materials sciences1-5. X-ray characterization requires a large number of atoms and reducing the material quantity is a long-standing goal. Here we show that X-rays can be used to characterize the elemental and chemical state of just one atom. Using a specialized tip as a detector, X-ray-excited currents generated from an iron and a terbium atom coordinated to organic ligands are detected. The fingerprints of a single atom, the L2,3 and M4,5 absorption edge signals for iron and terbium, respectively, are clearly observed in the X-ray absorption spectra. The chemical states of these atoms are characterized by means of near-edge X-ray absorption signals, in which X-ray-excited resonance tunnelling (X-ERT) is dominant for the iron atom. The X-ray signal can be sensed only when the tip is located directly above the atom in extreme proximity, which confirms atomically localized detection in the tunnelling regime. Our work connects synchrotron X-rays with a quantum tunnelling process and opens future X-rays experiments for simultaneous characterizations of elemental and chemical properties of materials at the ultimate single-atom limit.

Physiological Impacts of Raphidocelis subcapitata in Response to Lithiated Cobalt Oxide Nanomaterials.

Complex metal oxide nanomaterials, like LiCoO2 (LCO) nanosheets, are among the most widespread classes of nanomaterials on the market. Their ubiquitous application in battery storage technology drives their production to rates of environmental significance without sufficient infrastructure for proper disposal/recycling, thus posing a risk to ecosystem health and sustainability. This study assesses the general toxicological impacts of LCO when exposed to Raphidocelis subcapitata; physiological endpoints relating to growth and energy production are considered. Algal growth inhibition was significantly increased at concentrations as low as 0.1 µg·mL-1 , while exhibiting an EC50 of 0.057 µg·mL-1 . The average biovolume of cells were significantly enlarged at 0.01 µg·mL-1 , thus indicating increased instances of cell cycle arrest in LCO-treated cells. Additionally, LCO-treated cells produced significantly less carbon biomass while significantly overproducing neutral lipid content starting at 0.1 µg·mL-1 , thus indicating interference with CO2 assimilation chemistry and/or carbon partitioning. However, the relative abundance of chlorophyll was significantly increased, likely to maximize light harvesting and compensate for photosynthetic interference. Cells that were treated with dissolved Li+ /Co2+ ions did not significantly impact any of the endpoints tested, suggesting LCO phytotoxicity is mainly induced through nano-specific mechanisms rather than ion-specific. These results indicate that this type of nanomaterial can significantly impact the way this algae proliferates, as well as the way it produces and stores its energy, even at lower, sublethal, concentrations. Furthermore, impairments to crucial cellular pathways, like carbon assimilation, could potentially cause implications at the ecosystem level. Thus, in future work, it will be important to characterize the molecular mechanisms of LCO at the nano-bio interface. This article is protected by copyright. All rights reserved. Environ Toxicol Chem 2023;00:0-0. © 2023 SETAC.

Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties

Rank Aggregation has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Interest in this problem has increased due in part to the need to consolidate lists of rankings and scores output by different decision-making processes and algorithms. Although most attention has focused on the variant of this problem induced by the Kemeny-Snell distance, other robust rank aggregation problems have been proposed. This work delves into the rank aggregation problem under the generalized Kendall-tau distance —a parameterizable-penalty distance measure for comparing rankings with ties— which contains Kemeny aggregation as a special case. First, it derives exact and heuristic solution methods. Second, it introduces a social choice property (GXCC) that encloses existing variations of the Condorcet criterion as special cases, thereby expanding this seminal social choice concept beyond Kemeny aggregation for the first time. GXCC offers both computational and theoretical advantages. In particular, GXCC may help to divide the original problem into smaller subproblems, while still ensuring that solving them independently yields the optimal solution to the original problem. Experiments on two benchmark datasets conducted herein show that the featured exact and heuristic solution methods can benefit from GXCC. Finally, this work derives new theoretical insights into the effects of the generalized Kendall-tau distance penalty parameter on the optimal ranking and on the proposed social choice property.

Multifunctionalized Covalent Organic Frameworks for Broad-Spectrum Extraction and Ultrasensitive Analysis of Per- and Polyfluoroalkyl Substances

The contamination of surface and ground water by per- and polyfluoroalkyl substances (PFASs) has become a growing concern, and the structural diversity of PFASs is the major challenge for their ubiquitous applications. Strategies for monitoring coexistent anionic, cationic, and zwitterionic PFASs even at trace levels in aquatic environments are urgently demanded for effective pollution control. Herein, novel amide group and perfluoroalkyl chain-functionalized covalent organic frameworks (COFs) named COF-NH-CO-F9 are successfully synthesized and used for highly efficient extraction of broad-spectrum PFASs, attributing to their unique structure and the multifunctional groups. Under the optimal conditions, a simple and high-sensitivity method is established to quantify 14 PFASs including anionic, cationic, and zwitterionic species by coupling solid-phase microextraction (SPME) with ultrahigh-performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-MS/MS) for the first time. The established method displays high enrichment factors (EFs) of 66-160, ultrahigh sensitivity with low limits of detection (LODs) of 0.0035-0.18 ng L-1, a wide linearity of 0.1-2000 ng L-1 with correlation coefficient (R2) ≥0.9925, and satisfactory precision with relative standard deviations (RSDs) ≤11.2%. The excellent performance is validated in real water samples with recoveries of 77.1-108% and RSDs ≤11.4%. This work highlights the potential of rational design of COFs with the desired structure and functionality for the broad-spectrum enrichment and ultrasensitive determination of PFASs in real applications.

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

Generation and measurement of irregular polygonal perfect vortex optical beam based on all-dielectric geometric metasurface.

The perfect optical vortex (POV) beam carrying orbital angular momentum with topological charge-independent radial intensity distribution possesses ubiquitous applications in optical communication, particle manipulation, and quantum optics. But the mode distribution of conventional POV beam is relatively single, limiting the modulation of the particles. Here, we originally introduce the high-order cross-phase (HOCP) and ellipticity γ into the POV beam and construct all-dielectric geometric metasurfaces to generate irregular polygonal perfect optical vortex (IPPOV) beams following the trend of miniaturization and integration of optical systems. By controlling the order of the HOCP, conversion rate u, and ellipticity factor γ, various shapes of IPPOV beams with different electric field intensity distributions can be realized. In addition, we analyze the propagation characteristics of IPPOV beams in free-space, and the number and rotation direction of bright spots at the focal plane give the magnitude and sign of the topological charge carried by the beam. The method does not require cumbersome devices or complex calculation process, and provides a simple and effective method for simultaneous polygon shaping and topological charge measurement. This work further improves the beam manipulation ability while maintaining the characteristics of the POV beam, enriches the mode distribution of the POV beam, and provides more possibilities for particle manipulation.

Embedding-based Method for the Supervised Link Prediction in Social Networks

In recent years, social network analysis has received a lot of interest. Link prediction is an important area of research in this field that uses information from current networks to predict the likely links that will emerge in the future. It has attracted considerable attention from interdisciplinary research communities due to its ubiquitous applications in biological networks, computer science, transportation networks, bioinformatics, telecommunication networks, and so on. Currently, supervised machine learning is one of the critical techniques in the link prediction task. Several algorithms have been developed by many authors to predict the future link in the network, but there is still scope to improve the previous approaches. In the supervised link prediction process, feature selection is a crucial step. Most existing algorithms use one type of similarity-based feature to represent data, which is not well described due to the large scale and heterogeneity of social networks. One of the newest techniques for link prediction is embedding methods, which are used to preparing the feature vector for each the nonexisting links in the network. In this paper, we introduce a novel approach to supervised link prediction based on feature embedding methods in order to achieve better performance. Our contribution considers a set of embedding methods as the feature vector for training the machine learning classifiers. The main focus of this work is to investigate the relevance of different feature embedding methods to improve the performance of the supervised link prediction models. The experimental results on some real-world temporal networks revealed satisfactory results, which encourage us for further analysis. Moreover, the use of feature embedding methods will provide better performance in this regard.

RFPad: Enabling Device-Free Handwriting Recognition With a Tag Square

As a natural interaction approach, handwriting acts as an essential role in human–computer interaction. In order to achieve ubiquitous and reliable applications, a handwriting recognition system should be easy to use without any intrusion and robust to environment changes, which cannot be satisfied in most of existing approaches. In this article, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RFPad</i> , a device-free handwriting recognition system with a tag square consisting of four low-cost passive radio frequency identification (RFID) tags. With such an ingenious tag square, we transform the flat surface into a handwriting platform, and build a geometry-based theoretical model between the finger positions and the tags' phase variations so that the finger trajectory can be accurately tracked and handwriting can be recognized with the phases segmented from continuous signals. We implement a prototype of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RFPad</i> using commercial off-the-shelf RFID devices and conduct extensive experiments to evaluate its performance. Experiment results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RFPad</i> can track finger movement with an average error of 1 cm and achieve average recognition accuracy of 94.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for all 26 handwriting capital letters.

Achieving outstanding toughness of PMMA while retaining its strength, stiffness, and transparency using in situ developed TPEE nanofibrils

Poly(methyl methacrylate) (PMMA) is an amorphous thermoplastic with high strength, high stiffness, and excellent light transmittance. However, PMMA is very brittle at room temperature, which limits its ubiquitous applications. In this article, we report a scalable, cost-effective, and environmentally-friendly method for enhancing the toughness of PMMA without sacrificing its strength, stiffness, and transparency. PMMA composites comprising in situ developed nanofibrils of thermoplastic polyester elastomer (TPEE) were fabricated following the established concept of converting instead of adding. Remarkably, even only 1 wt% of TPEE nanofibrils with an average diameter of ∼50 nm provided a ∼255% increment in the toughness and ∼29.2% improvement in the impact strength of PMMA, while still maintaining its strength, stiffness, and transparency. In comparison, when TPEE was dispersed and distributed as spheroids, no toughening effect was observed at 1 wt% of TPEE content, and even 10 wt% of TPEE content provided only a ∼150% increment in the toughness, which was, however, accompanied by a decrease in strength, stiffness, and transparency. Herein, we present the effect of both TPEE structure (spheroids vs nanofibrils) and concentration on the rheological, thermomechanical, light transmittance, and mechanical properties of the PMMA/nanofibril-TPEE composites. Based on the fractography and small-angle X-ray scattering analysis, we propose that nanofibril-induced multiple crazing is one of the major sources of the greatly improved toughness. The composites presented in this article can be used in applications that require unique combinations of high toughness, high strength, high stiffness, and excellent transparency.

Binding of serum albumin to perfluorooctanoic acid reduced cytotoxicity

With the ubiquitous applications of perfluorinated compounds such as perfluorooctanoic acid (PFOA) in industrial and commercial products, the toxicity of these engineered materials in environmental and public health is received growing attention. As a typical organic pollutant, PFOA has been extensively found in wildlife and human bodies, and can preferentially bind to serum albumin in vivo. However, the importance of protein–PFOA interactions on the cytotoxicity of PFOA could not be stressed enough. In this study, we used both experimental and theoretical approaches, to investigate the interactions of PFOA with bovine serum albumin (BSA, the most abundant protein in blood). It was found that PFOA could mainly interact with Sudlow site I of BSA to form BSA-PFOA complex, in which van der Waals forces and hydrogen bonds played dominant roles. Moreover, the strong binding of BSA could greatly alter the cellular uptake and distribution of PFOA in human endothelial cells, and result in the decreases of reactive oxygen species formation and cytotoxicity for these BSA-coated PFOA. Consistently, the addition of fetal bovine serum into cell culture medium also significantly mitigated PFOA-induced cytotoxicity, which was attributed to the extracellular complexation between PFOA and serum proteins. Altogether, our study demonstrates that the binding of serum albumin to PFOA could reduce its toxicity by affecting the cellular responses.

Introduction for factors of success and failure by using IoT in project management

The Internet is a relatively recent endeavor that has continued without conclusion for a considerable length of time, growing in importance to individuals, organizations, and governments everywhere until we finally settled on the term Internet of Things (IoT) to describe its ubiquitous application of multiple technologies across all spheres of human endeavor. In addition, the variety of IoT applications and use cases expands annually. There will be a need for this trend to grow substantially in order to satisfy the enormous amount of demand. Whether or not an IoT application is successful, simple to deploy, and user-friendly depends on a number of things. Based on research on the causes of success and failure in the implementation of IoT, it has become clear that most studies focus on either success factors alone or failure factors alone; this paper aims to bridge that gap by discussing the factors that contribute to the Internet of Things' success and the factors that lead to its failure, both of which aid in the technology's growth and dissemination and provide guidance to experts in the field.

The Effect of Liquid–Solid Interactions upon Nucleate Boiling on Rough Surfaces: Insights from Molecular Dynamics

Despite the fact that engineered surface enabling remarkable phase change heat transfer have elicited increasing attention due to their ubiquitous applications in thermal management, the underlying mechanisms of intrinsic rough structures as well as the surface wettability on bubble dynamics remain to be explored. Therefore, a modified molecular dynamics simulation of nanoscale boiling was conducted in the present work to investigate bubble nucleation on rough nanostructured substrates with different liquid-solid interactions. Specifically, the initial stage of nucleate boiling was mainly investigated and the bubble dynamic behaviors were quantitively studied under different energy coefficients. Results shows that as the contact angle decreases, the nucleation rate increases, because liquid obtains more thermal energy there compared with that on less wetting surfaces. The rough profiles of the substrate can provide nanogrooves, which can enhance initial nucleate embryos, thereby improving thermal energy transfer efficiency. Moreover, atomic energies are calculated and adopted to explain how bubble nuclei are formed on various wetting substrates. The simulation results are expected to provide guidance towards surface design in state-of-the art thermal management systems, such as the surface wettability and the nanoscale surface patterns.

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.

A Novel Pressure-Controlled Molecular Dynamics Simulation Method for Nanoscale Boiling Heat Transfer

Pool boiling, enabling remarkable phase-change heat transfer, has elicited increasing attention due to its ubiquitous applications in solar thermal power stations. An explicit understanding of the effect of system pressure on pool boiling is required to enhance the phase-change heat transfer. Despite its wide application when exploring the potential mechanism of boiling, the molecular dynamics method still needs to be improved when discussing the working mechanism of system pressure. Therefore, in the present study, a novel molecular dynamics simulation method of nanoscale pool boiling was proposed. This method provides a way to change and control pressure during the phase-change process. Furthermore, the bubble nucleation and growth in nanoscale pool boiling are quantitatively investigated through pressure-control molecular dynamics simulations. We expect that this study will improve the present simulation method of pool boiling and provide useful insights to the physics of the process.

Approximate Condorcet Partitioning: Solving large-scale rank aggregation problems

Rank aggregation has ubiquitous applications in computer science, operations research, and various other fields. Most attention on this problem has focused on an NP-hard variant known as Kemeny aggregation, for which solution approaches with provable guarantees that can handle difficult high-dimensional instances remain elusive. This work introduces exact and approximate methodologies inspired by the social choice foundations of the problem, namely the Condorcet Criterion. We formalize the concept of the finest-Condorcet partition for rankings that may contain ties and specify its required conditions. We prove that this partition is unique and devise an efficient algorithm to obtain it. To deal with instances where it does not yield many subsets, we propose Approximate Condorcet Partitioning (ACP), with which larger subsets can be further broken down and more easily solved. ACP is a scalable solution technique capable of handling large instances while still providing provable guarantees. Although ACP approximation factors are instance-specific, their values were lower than those offered by all known constant-factor approximation schemes — inexact algorithms whose resulting objective values are guaranteed to be within a specified fixed percent of the optimal objective value — for all 113 instances tested herein (containing up to 2,820 items). What is more, ACP obtained solutions that deviated by at most two percent from the optimal objective function values for a large majority of these instances.

Guest Editorial: Next-Generation Network Automation for Industrial Internet-of-Things in Industry 5.0

Network automation has originated in the early 21st century by the International Business Machines Corporation (IBM), which was initialized conceptually, including automated configuration, optimization, healing, and protection of network deployment. In the framework of 5G and upcoming 6G, softwarization and virtualization, as well as the conceived pervasive artificial intelligence (AI), have been activating and further proliferating network automation, supporting ubiquitous applications with diverse network demands, which have recently attracted plenty of research efforts.

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

Google Meet's Ubiquitous and Pervasive Application with BigBlueButton

In this pandemic period, the education processes apply online distance learning. There are many educational learning media that can be used and one is by using Google Meet. A platform for video conferences is called Google Meet. With the ability to host video conferences and host presentations, it is an excellent platform for cooperation. so that you can attend meetings straight from an email or Calendar event. Google Meet is important to inspect teachers' perceptions and attitudes. When they have positive perceptions about online education, teachers also adopt positive attitudes toward educational technology. They would thus be open to change and resist the move to online virtual education. Google Meet is now experiencing a pandemic with various difficulties. This research paper aims to introduce Google Meet, how to utilize the application of Google Meet, how to generate Google meet links, and their benefits. This paper focuses on the effect of the Google Meet media-assisted lecture method on building student knowledge.