bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Effective Crosslinking Density for Pseudo-Semi Interpenetrating Polymer Networks Based on Polyether Glycols Mixture by Dynamic Mechanical Analysis
Authors: Li Yajin , Sun Bingbing , Liu Yunfei , Zhang Zhengzhong , Shen Yupeng , Wang Haiyang , Liu Xiaojun , Xie Wuxi
Published: January 2023

Pseudo-semi interpenetrating polymer networks (pseudo-semi IPNs) are a special example of topological isomerism in macromolecules, which have attracted significant attention in recent years with a high potential in a variety of engineering applications of polymeric materials. In this article, pseudo-semi IPNs were synthetized by sequential polymerization of thermoplastic polymers (TPEs) in the presence of thermosetting elastomer (TSEs) with contents of 10, 20, 30, 40 and 50 wt.% in a vacuum oven at 60 °C for about 72 h. In addition, this article describes a method for researching the elastic modulus, effective crosslinking density and physical crosslinking density of TSEs and pseudo-semi IPNs. The inherent interactions and entanglements of pseudo-semi IPNs were discussed by analyzing the changes in elastic modulus and effective crosslinking density at different temperatures. The results show that after the TPE was added to the TSE matrix as a plastic-reinforced material, the ductility increased from 89.6% to 491%, the effective crosslinking density was increased by 100% at high temperatures and the strength of the material matrix was significantly improved. Two physical events take place in our pseudo-semi IPNs as result of energy dissipation and polymeric chains mobility.
Hierarchical Macroporous PolyDCPD Composites from Surface-Modified Calcite-Stabilized High Internal Phase Emulsions
Authors: Eslek Ali , Mert Hatice Hande , Sözbir Meltem , Alaasar Mohamed , Mert Emine Hilal
Published: January 2023

High Internal Phase Emulsions (HIPEs) of dicyclopentadiene (DCPD) were prepared using mixtures of surface-modified calcite (mCalcite) and a non-ionic surfactant. Twelve different emulsion formulations were created using an experimental design methodology. Three distinctive levels of the internal phase ratio, the amount of mCalcite loading, and the surfactant were used to prepare the HIPEs. Accordingly, macroporous polyDCPD composites were synthesized by performing ring-opening metathesis polymerization (ROMP) on the HIPEs. The variations in the morphological and physical properties of the composites were investigated in terms of experimental parameters. In the end, five different model equations were derived with a confidence level of 95%. The main and binary interaction effects of the experimental parameters on the responses, such as the average cavity size, interconnecting pore size, specific surface area, foam density, and compression modulus, were demonstrated. The synergistic interaction between the amount of surfactant, the amount of mCalcite loading, and the internal phase ratio appeared to have a dominant role in the average cavity diameter. The solo effect of the internal phase ratio on the interconnecting pore size, foam density, and compression modulus was confirmed. In addition, it was demonstrated that the specific surface area of the composites was mainly changed depending on the amount of mCalcite loading.
Macro-Size Regenerated Cellulose Fibre Embedded with Graphene Oxide with Antibacterial Properties
Authors: Mazlan Nyak Syazwani Nyak , Salleh Kushairi Mohd , Khairunnisa-Atiqah Mohamad Khalid , Ainul Hafiza Abdul Hair , Mostapha Marhaini , Ellis Amanda V. , Zakaria Sarani
Published: January 2023

Macro-size regenerated cellulose fibres (RCFs) with embedded graphene oxide (GO) were fabricated by dissolving cellulose in a pre-cooled sodium hydroxide (NaOH)/urea solution and regenerated in sulphuric acid (H2SO4) coagulant. Initially, GO was found to disperse well in the cellulose solution due to intercalation with the cellulose; however, this cellulose–GO intercalation was disturbed during the regeneration process, causing agglomeration of GO in the RCF mixture. Agglomerated GO was confirmed at a higher GO content under a Dino-Lite microscope. The crystallinity index (CrI) and thermal properties of the RCFs increased with increasing GO loadings, up to 2 wt.%, and reduced thereafter. Cellulose–GO intercalation was observed at lower GO concentrations, which enhanced the crystallinity and thermal properties of the RCF–GO composite. It was shown that the GO exhibited antibacterial properties in the RCF–GO composite, with the highest bacterial inhibition against E. coli and S. aureus.
Dielectric Characteristics of Crosslinked Polyethylene Modified by Grafting Polar-Group Molecules
Authors: Gao Jun-Guo , Liu Li-Wei , Sun Wei-Feng
Published: January 2023

Polar group-modified crosslinked polyethylene (XLPE) materials are developed with a peroxide thermochemical method of individually grafting chloroacetic acid allyl ester (CAAE) and maleic anhydride (MAH) to polyethylene molecular-chains, which are dedicated to ameliorating dielectric characteristics through charge-trapping mechanism. By free radical addition reactions, the CAAE and MAH molecules are successfully grafted to polyethylene molecular chains of XLPE in crosslinking process, as verified by infrared spectroscopy molecular characterizations. Dielectric spectra, electric conductance, and dielectric breakdown strength are tested to evaluate the improved dielectric performances. Charge trap characteristics are investigated by analyzing thermal stimulation depolarization currents in combination with first-principles electronic-structure calculations to reveal the polar-group introduced mechanisms of contributing dipole dielectric polarization, impeding electric conduction, and promoting electrical breakdown field. The grafted polar-group molecules, especially for MAH, can introduce deep-level charge traps in XLPE materials to effectively restrict charge injections and hinder charge carrier transports, which accounts for the significant improvements in electric resistance and dielectric breakdown strength.
New Relaxed Static Output Feedback Stabilization of T–S Fuzzy Systems with Time-Varying Delays
Authors: Qi Shunan , Zhou Kun , Xu Suan , Gao Yanfeng
Published: January 2023

This paper investigates the imperfect premise matching-based (IPMB) static output feedback (SOF) controller for T–S fuzzy systems with time-varying delays. Firstly, by employing integral inequality techniques, the membership-function-independent fuzzy SOF design methods in terms of LMIs are presented based on imperfect premise matching (IPM) strategy. The obtained stabilization conditions do not contain equality constraints, and the output matrices do not have rank constraints. Secondly, some suitable relaxation variables are employed, and more relaxed membership-function-dependent (MFD) stabilization conditions are obtained by considering the local boundary information of membership functions (MFs). Finally, two simulation examples are given to show the progressiveness of the proposed methods in this paper.
One-Layer Real-Time Optimization Using Reinforcement Learning: A Review with Guidelines
Authors: Faria Ruan , Capron Bruno , de Souza Jr. Maurício , Secchi Argimiro
Published: January 2023

This paper reviews real-time optimization from a reinforcement learning point of view. The typical control and optimization system hierarchy depend on the layers of real-time optimization, supervisory control, and regulatory control. The literature about each mentioned layer is reviewed, supporting the proposal of a benchmark study of reinforcement learning using a one-layer approach. The multi-agent deep deterministic policy gradient algorithm was applied for economic optimization and control of the isothermal Van de Vusse reactor. The cooperative control agents allowed obtaining sufficiently robust control policies for the case study against the hybrid real-time optimization approach.
Two-Stage Pretreatment of Jerusalem Artichoke Stalks with Wastewater Recycling and Lignin Recovery for the Biorefinery of Lignocellulosic Biomass
Authors: Chen Yudian , Peng Nian , Gao Yushan , Li Qian , Wang Zancheng , Yao Bo , Li Yonghao
Published: January 2023

Jerusalem artichoke (Helianthus tuberosus L.) is emerging as one of the energy plants considered for biofuel production. Alkali and alkali-involved pretreatment methods have been widely used for the bioconversion of cellulosic materials due to their high sugar yield and low inhibitor release. However, the recovery and treatment of wastewater (black liquor) have been poorly studied. Here, we present a novel two-stage pretreatment process design for recycling black liquor. Jerusalem artichoke stalk (JAS) was first treated with 2% (w/v) NaOH, after which lignin was recovered by H2SO4 at pH 2.0 from the black liquor. The recycled solutions were subsequently used to treat the NaOH-pretreated JAS for the second time to dissolve hemicellulose. CO-pretreated JAS, hydrolysates, and acid-insoluble lignin were obtained after the above-mentioned two-stage pretreatment. A reducing sugar yield of 809.98 mg/g Co-pretreated JAS was achieved after 48 h at 5% substrate concentration using a cellulase dosage of 25 FPU/g substrate. In addition, hydrolysates containing xylose and acid-insoluble lignin were obtained as byproducts. The pretreatment strategy described here using alkali and acid combined with wastewater recycling provides an alternative approach for cellulosic biorefinery.
Biosensing of Haemorheological Properties Using Microblood Flow Manipulation and Quantification
Authors: Kang Yang Jun
Published: January 2023

The biomechanical properties of blood have been used to detect haematological diseases and disorders. The simultaneous measurement of multiple haemorheological properties has been considered an important aspect for separating the individual contributions of red blood cells (RBCs) and plasma. In this study, three haemorheological properties (viscosity, time constant, and RBC aggregation) were obtained by analysing blood flow, which was set to a square-wave profile (steady and transient flow). Based on a simplified differential equation derived using a discrete circuit model, the time constant for viscoelasticity was obtained by solving the governing equation rather than using the curve-fitting technique. The time constant (λ) varies linearly with respect to the interface in the coflowing channel (β). Two parameters (i.e., average value: <λ>, linear slope: dλdβ) were newly suggested to effectively represent linearly varying time constant. <λ> exhibited more consistent results than dλdβ. To detect variations in the haematocrit in blood, we observed that the blood viscosity (i.e., steady flow) is better than the time constant (i.e., transient flow). The blood viscosity and time constant exhibited significant differences for the hardened RBCs. The present method was then successfully employed to detect continuously varying haematocrit resulting from RBC sedimentation in a driving syringe. The present method can consistently detect variations in blood in terms of the three haemorheological properties.
Hybrid Refractive-Diffractive Lens with Reduced Chromatic and Geometric Aberrations and Learned Image Reconstruction
Authors: Evdokimova Viktoria , Podlipnov Vladimir , Ivliev Nikolay , Petrov Maxim , Ganchevskaya Sofia , Fursov Vladimir , Yuzifovich Yuriy , Stepanenko Sergey , Kazanskiy Nikolay , Nikonorov Artem , Skidanov Roman
Published: January 2023

In this paper, we present a hybrid refractive-diffractive lens that, when paired with a deep neural network-based image reconstruction, produces high-quality, real-world images with minimal artifacts, reaching a PSNR of 28 dB on the test set. Our diffractive element compensates for the off-axis aberrations of a single refractive element and has reduced chromatic aberrations across the visible light spectrum. We also describe our training set augmentation and novel quality criteria called “false edge level” (FEL), which validates that the neural network produces visually appealing images without artifacts under a wide range of ISO and exposure settings. Our quality criteria (FEL) enabled us to include real scene images without a corresponding ground truth in the training process.
Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review
Authors: Irkham Irkham , Ibrahim Abdullahi Umar , Nwekwo Chidi Wilson , Al-Turjman Fadi , Hartati Yeni Wahyuni
Published: January 2023

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.
SoftSAR: The New Softer Side of Socially Assistive Robots—Soft Robotics with Social Human–Robot Interaction Skills
Authors: Sun Yu-Chen , Effati Meysam , Naguib Hani E. , Nejat Goldie
Published: January 2023

When we think of “soft” in terms of socially assistive robots (SARs), it is mainly in reference to the soft outer shells of these robots, ranging from robotic teddy bears to furry robot pets. However, soft robotics is a promising field that has not yet been leveraged by SAR design. Soft robotics is the incorporation of smart materials to achieve biomimetic motions, active deformations, and responsive sensing. By utilizing these distinctive characteristics, a new type of SAR can be developed that has the potential to be safer to interact with, more flexible, and uniquely uses novel interaction modes (colors/shapes) to engage in a heighted human–robot interaction. In this perspective article, we coin this new collaborative research area as SoftSAR. We provide extensive discussions on just how soft robotics can be utilized to positively impact SARs, from their actuation mechanisms to the sensory designs, and how valuable they will be in informing future SAR design and applications. With extensive discussions on the fundamental mechanisms of soft robotic technologies, we outline a number of key SAR research areas that can benefit from using unique soft robotic mechanisms, which will result in the creation of the new field of SoftSAR.
A Dihydropyridine Derivative as a Highly Selective Fluorometric Probe for Quantification of Au3+ Residue in Gold Nanoparticle Solution
Authors: Paisuwan Waroton , Sukwattanasinitt Mongkol , Tobisu Mamoru , Ajavakom Anawat
Published: January 2023

Novel dihydroquinoline derivatives (DHP and DHP-OH) were synthesized in one pot via a tandem trimerization-cyclization of methylpropiolate. DHP and DHP-OH possess strong blue fluorescence with high quantum efficiencies over 0.70 in aqueous media. DHP-OH displays a remarkable fluorescence quenching selectively to the presence of Au3+ through the oxidation of dihydropyridine to pyridinium ion as confirmed by NMR and HRMS. DHP-OH was used to demonstrate the quantitative analysis of Au3+ in water samples with the limit of detection of 33 ppb and excellent recovery (>95%). This fluorescent probe was also applied for the determination of Au3+ residue in the gold nanoparticle solution and a paper-based sensing strip for the on-site detection of Au3+.
Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry
Authors: Xu Haoxuan , Wang Jianping , Zhang Ya , Zhang Guo , Xiong Zhaolong
Published: January 2023

The variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variational optical flow estimation, we combine the motion laws of incompressible fluids. Introducing the idea of large eddy simulation, the instantaneous motion can be decomposed into large-scale motion and a small-scale turbulence in the data term. The Smagorinsky model is used to model and solve the small-scale turbulence. The improved subgrid scale Horn–Schunck (SGS-HS) optical flow algorithm provides better results in velocity field estimation of turbulent image sequences than the traditional Farneback dense optical flow algorithm. To make the SGS-HS algorithm equally competent for the open channel flow measurement task, a velocity gradient constraint is chosen for the canonical term of the model, which is used to improve the accuracy of the SGS-HS algorithm in velocimetric experiments in the case of the relatively uniform flow direction of the open channel flow field. The experimental results show that our algorithm has better performance in open channel velocimetry compared with the conventional algorithm.
Computation of Analytical Derivatives for Airborne TEM Inversion Using a Cole–Cole Parameterization Based on the Current Waveform of the Transmitter
Authors: Lei Da , Ren Hao , Fu Changmin , Wang Zhongxing , Zhen Qihui
Published: January 2023

Airborne transient electromagnetic (ATEM) technology is a technique often used in mineral exploration and geological mapping. Due to inductive polarization (IP) phenomena, the ATEM response curve often shows a negative response or declines rapidly to the attenuation curve. Traditional resistivity inversion techniques do not explain the IP response of a signal well, so the negative response is usually removed during data processing, resulting in a reduced correctness and authenticity of the findings. In this paper, in the parameter inversion based on the Cole–Cole model, the Jacobian matrix chain analysis method is used to calculate, and the current waveform calculation is also considered in the inversion. The results show that compared with the perturbation method, the analysis technique can greatly reduce the calculation time and improve the inversion efficiency. In the single-point one-dimensional inversion and lateral constraint quasi-two-dimensional inversion, the Cole–Cole four-parameter forward response has strong inversion accuracy, which can successfully invert the actual exploration content and the Cole–Cole four-parameter response. Some measured sounding data in the Qingchengzi survey area of Liaoning Province, China have a negative response to IP, and the resistivity scheme cannot be used alone for inversion, but the real underground resistivity structure can be obtained through the method studied in this paper, and good exploration results can be obtained.
A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
Authors: Javadiha Mohammadreza , Andujar Carlos , Lacasa Enrique
Published: January 2023

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.
Remote Sensing of Seawater Temperature and Salinity Profiles by the Brillouin Lidar Based on a Fizeau Interferometer and Multichannel Photomultiplier Tube
Authors: Wang Yuanqing , Xu Yangrui , Chen Ping , Liang Kun
Published: January 2023

Brillouin spectroscopy is a powerful tool to measure the water temperature and salinity profiles of seawater. Considering the insufficiency of the current spectral measurement methods in real-time, spectral integrity, continuity, and stability, we developed a new lidar system for spectrum measurement on an airborne platform that is based on a Fizeau interferometer and multichannel photomultiplier tube. In this approach, the lidar system uses time-of-flight information to measure the depth and relies on Brillouin spectroscopy as the temperature and salinity indicator. In this study, the system parameters were first optimized and analyzed. Based on the analysis results, the performance of the system in terms of detection depth and accuracy was evaluated. The results showed that this method has strong anti-interference ability, and under a temperature measurement accuracy of 0.5 °C and a salinity measurement accuracy of 1‰, the effective detection depth exceeds 40.51 m. Therefore, the proposed method performs well and will be a good choice for achieving Brillouin lidar application in seawater remote sensing.
Micro-Vision Based High-Precision Space Assembly Approach for Trans-Scale Micro-Device: The CFTA Example
Authors: Zhang Juan , Dai Xi , Wu Wenrong , Du Kai
Published: January 2023

For assembly of trans-scale micro-device capsule fill tube assemblies (CFTA) for inertial confinement fusion (ICF) targets, a high-precision space assembly approach based on micro-vision is proposed in this paper. The approach consists of three modules: (i) a posture alignment module based on a multi-vision monitoring model that is designed to align two trans-scale micro-parts in 5DOF while one micro-part is in ten microns and the other one is in hundreds of microns; (ii) an insertion depth control module based on a proposed local deformation detection method to control micro-part insertion depth; (iii) a glue mass control module based on simulation research that is designed to control glue mass quantitatively and to bond micro-parts together. A series of experiments were conducted and experimental results reveal that attitude alignment control error is less than ±0.3°, position alignment control error is less than ±5 µm, and insertion depth control error is less than ±5 μm. Deviation of glue spot diameter is controlled at less than 15 μm. A CFTA was assembled based on the proposed approach, the position error in 3D space measured by computerized tomography (CT) is less than 5 μm, and glue spot diameter at the joint is 56 μm. Through volume calculation by the cone calculation formula, the glue mass is about 23 PL when the cone height is half the diameter.
An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups
Authors: Dhal Sambandh Bhusan , Mahanta Shikhadri , Gumero Jonathan , O’Sullivan Nick , Soetan Morayo , Louis Julia , Gadepally Krishna Chaitanya , Mahanta Snehadri , Lusher John , Kalafatis Stavros
Published: January 2023

Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions. Our solution entails sensing the nutrient concentrations in the hydroponic solution, specifically calcium, sulfate, and phosphate, and sending them to a Machine Learning (ML) model hosted on an Android application. The ML algorithm used in this case was a Linear Support Vector Machine (Linear-SVM) trained on top three nutrient predictors chosen after applying a pipeline of Feature Selection methods namely a pairwise correlation matrix, ExtraTreesClassifier and Xgboost classifier on a dataset recorded from three aquaponic farms from South-East Texas. The ML algorithm was then hosted on a cloud platform which would then output the maximum tolerable levels of iron, copper and zinc in real time using the concentration of phosphorus, calcium and sulfur as inputs and would be controlled using an array of dispensing and detecting equipments in a closed loop system.
Optimising Multispectral Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae
Authors: Courtecuisse Emilie , Marchetti Elias , Oxborough Kevin , Hunter Peter D. , Spyrakos Evangelos , Tilstone Gavin H. , Simis Stefan G. H.
Published: January 2023

This study assesses the ability of a new active fluorometer, the LabSTAF, to diagnostically assess the physiology of freshwater cyanobacteria in a reservoir exhibiting annual blooms. Specifically, we analyse the correlation of relative cyanobacteria abundance with photosynthetic parameters derived from fluorescence light curves (FLCs) obtained using several combinations of excitation wavebands, photosystem II (PSII) excitation spectra and the emission ratio of 730 over 685 nm (Fo(730/685)) using excitation protocols with varying degrees of sensitivity to cyanobacteria and algae. FLCs using blue excitation (B) and green–orange–red (GOR) excitation wavebands capture physiology parameters of algae and cyanobacteria, respectively. The green–orange (GO) protocol, expected to have the best diagnostic properties for cyanobacteria, did not guarantee PSII saturation. PSII excitation spectra showed distinct response from cyanobacteria and algae, depending on spectral optimisation of the light dose. Fo(730/685), obtained using a combination of GOR excitation wavebands, Fo(GOR, 730/685), showed a significant correlation with the relative abundance of cyanobacteria (linear regression, p-value < 0.01, adjusted R2 = 0.42). We recommend using, in parallel, Fo(GOR, 730/685), PSII excitation spectra (appropriately optimised for cyanobacteria versus algae), and physiological parameters derived from the FLCs obtained with GOR and B protocols to assess the physiology of cyanobacteria and to ultimately predict their growth. Higher intensity LEDs (G and O) should be considered to reach PSII saturation to further increase diagnostic sensitivity to the cyanobacteria component of the community.
Assessment of Finger Fat Pad Effect on CSRR-Based Sensor Scattering Parameters for Non-Invasive Blood Glucose Level Detection
Authors: Hannachi Chaouki , Deshours Frédérique , Alquie George , Kokabi Hamid
Published: January 2023

This paper examines the effect of finger fat pad thickness on the accuracy performance of complementary split-ring resonator (CSRR)-based microwave sensors for non-invasive blood glucose level detection. For this purpose, a simplified four-layer Cole–Cole model along with a CSRR-based microwave sensor have been comprehensively analyzed and validated through experimentation. Computed scattering parameter (S-parameter) responses to different fat layer thicknesses are employed to verify the concordance of the studied model with the measurement results. In this respect, a figure of merit (FM) based on the normalized squared difference is introduced to assess the accuracy of the considered Cole–Cole model. We have demonstrated that the analyzed model agrees closely with the experimental validation. In fact, the maximum error difference for all five fingertips does not exceed 1.73 dB over the entire frequency range of interest, from 1 GHz to 4 GHz.
Efficiency Maximization for Battery-Powered OFDM Transmitter via Amplifier Operating Point Adjustment
Authors: Kryszkiewicz Pawel
Published: January 2023

While Orthogonal Frequency Division Multiplexing (OFDM) is a dominating spectrum access technology in modern, wideband access networks, it is important to maximize its transmission efficiency considering the underlying radio front-end characteristics. A practical front-end contains nonlinear components, e.g., a Power Amplifier (PA), resulting in nonlinear distortion being injected into OFDM band deteriorating symbols detection. A PA operating point, defined here by Input Back-Off (IBO), can be adjusted to balance the wanted signal power and nonlinear distortion power. While it is the most common to maximize the spectral efficiency (SE), recently, energy efficiency (EE) maximization gained momentum. However, EE maximization requires, in addition to PA nonlinearity modeling, modeling of the power consumption of the PA and all other transmitter components. While it is commonly overlooked, if a battery is used to power the transmitter, its model should be considered as well. This paper derives mathematical expressions for EE and SE of an OFDM transmitter considering Rapp and soft-limiter models of PA nonlinearity, class A, class B, and perfect PA power consumption models, and two battery models: perfect and worst-case. While closed-form expressions cannot be obtained for most of the derived integrals, numerical methods have been used to obtain the optimal IBO value in each case. The numerical results show, in addition to optimal IBO values, the expected Signal-to-Noise and Distortion Ratios (SNDRs). It is shown that the optimal IBO value changes significantly with the wireless channel properties, utilized hardware architecture, or the utilized optimization goal. As such, the proposed optimization is an important topic for 5G and beyond transmitters.
Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame Information
Authors: Galván-Ruiz Jesús , Travieso-González Carlos M. , Pinan-Roescher Alejandro , Alonso-Hernández Jesús B.
Published: January 2023

Nowadays, according to the World Health Organization (WHO), of the world’s population suffers from a hearing disorder that makes oral communication with other people challenging. At the same time, in an era of technological evolution and digitization, designing tools that could help these people to communicate daily is the base of much scientific research such as that discussed herein. This article describes one of the techniques designed to transcribe Spanish Sign Language (SSL). A Leap Motion volumetric sensor has been used in this research due to its capacity to recognize hand movements in 3 dimensions. In order to carry out this research project, an impaired hearing subject has collaborated in the recording of 176 dynamic words. Finally, for the development of the research, Dynamic Time Warping (DTW) has been used to compare the samples and predict the input with an accuracy of 95.17%.
JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution
Authors: Pavón-Pulido Nieves , Blasco-García Jesús Damián , López-Riquelme Juan Antonio , Feliu-Batlle Jorge , Oterino-Bono Roberto , Herrero María Trinidad
Published: January 2023

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.
Non-Stationary Characteristics of AQM Based on the Queue Length
Authors: Chydzinski Andrzej
Published: January 2023

We performed a non-stationary analysis of a class of buffer management schemes for TCP/IP networks, in which the arriving packets were rejected randomly, with probability depending on the queue length. In particular, we derived formulas for the packet waiting time (queuing delay) and the intensity of packet losses as functions of time. These results allow us to observe how the evolution of the waiting time and losses depend on initial conditions (e.g., the full buffer) and system parameters (e.g., dropping probabilities, load, packet size distribution). As side results, the stationary waiting time and packet loss probability were obtained. Numerical examples demonstrate applicability of the theoretical results.
Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics
Authors: Peyman Mohammad , Fluechter Tristan , Panadero Javier , Serrat Carles , Xhafa Fatos , Juan Angel A.
Published: January 2023

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.
Ultrasound Diagnostic and Physiotherapy Approach for a Patient with Parsonage–Turner Syndrome—A Case Report
Authors: Wolny Tomasz , Glibov Katarzyna , Granek Arkadiusz , Linek Paweł
Published: January 2023

Parsonage–Turner syndrome (PTS) is a rare neurological disorder that causes major diagnostic problems. This paper presents a case report of a patient with PTS and proposes a new physiotherapy program. Case description: a 23-year-old man presents a sudden severe pain of his right arm. The man is consulted by several doctors and physiotherapists. Three magnetic resonance imagings (MRI), a nerve conduction study (NCS), and needle electromyography (EMG) are performed. After 6 months, based on medical history, physical examination and ultrasound imaging (UI), the physiotherapist suggests PTS, which is confirmed by a neurologist. Intervention: due to the lack of physiotherapy treatment standards in PTS, we apply neurodynamic techniques. Outcomes: neurodynamic techniques are effective in reducing pain and paraesthesia, improving sensation, and reducing nerve swelling (assessed by UI), as well as improving manual dexterity and overall health status. Conclusions: the patient with PTS is challenging for making an accurate diagnosis. This study shows an important role for UI, which shows changes in the musculocutaneous nerve, despite the lack of abnormalities in the MRI, NCS, and EMG, and helps in making an accurate diagnosis. This report also confirms that physiotherapy based on neurodynamic techniques may have beneficial effects in PTS.
Surface Decarburization Depth Detection in Rods of 60Si2Mn Steel with Magnetic Barkhausen Noise Technique
Authors: Li Peng , Wang Xianxian , Ding Dongdong , Gao Zhaoxiang , Fang Wen , Zhang Chaolei , He Cunfu , Liu Xiucheng
Published: January 2023

Magnetic Barkhausen noise (MBN), sensitive to the microstructure of materials, can be applied in the surface decarburization depth detection of ferromagnetic specimens. However, the effects of core microstructures on the determination results of decarburization depth have not been explored. In this study, MBN was employed to evaluate the magnetic properties of the decarburized 60Si2Mn spring steels with martensitic and pearlitic core microstructures. Spring steel samples were austenitized at different times to generate different decarburization depths. Seven magnetic features were extracted from the MBN butterfly profiles. We used the variation coefficient, linear correlation coefficient, and normalized sensitivity to discuss the influence of the core microstructures on these seven features. The different core microstructures led to a large difference in the ability of MBN features to characterize the decarburization layer depth. However, three features of MBN butterfly profiles demonstrated an approximately linear dependency (linear correlation coefficient > 94%) on surface decarburization depth and monotonically increased with the increase in depth in both core microstructures of spring steels.
Does Innovation Spur Integrated Reporting?
Authors: Pinto Ricardo , Lourenço Isabel , Simões Ana
Published: January 2023

This study analyses whether country-level innovation performance and firm-level innovation commitment are associated to adopting the integrated reporting in Europe. The empirical analysis relies on a logistic regression model applied to 388 firm-year observations regarding firms located in Europe and data between 2016–2019. The results show a positive and significant association between country-level innovation performance and integrated reporting uptake. Moreover, at a firm level, the data partially support that the influence of innovation commitment on the likelihood of publishing integrated reports is higher for firms with a higher-level of sustainability performance. This research contributes to the literature by focusing simultaneously on the impact of country-level innovation performance and firm-level innovation commitment on integrated reporting acceptance in the European setting. In addition, the adopted country-level conceptual support is based on the institutional theory combined with the framework of the national innovation systems. The latter, to the best of our knowledge, has not yet been applied in this line of research.
Influence of Environmental Regulation on the International Competitiveness of the High-Tech Industry: Evidence from China
Authors: Tu Wenjuan , Shi Rui
Published: January 2023

Environmental regulation has become a significant way to achieve sustainable development. Taking the panel data of China’s high-tech industries from 2007–2016 in 30 provinces and cities as a sample, regression models are established to study the impact of three different types of environmental regulations on international competitiveness and the moderating effect of R&D (research & development) investment. The results show that the impact of environmental regulations on the international competitiveness of high-tech industries is related to the type of environmental regulation. There is a significant negative correlation between economic environmental regulation (EER) and the international competitiveness of high-tech industries, and R&D investment has a significant inhibitory effect on the relationship between the two. For both commanding environmental regulation (CER) and participatory environmental regulation (PER), R&D investment plays a significant moderating role in environmental regulation and the international competitiveness of high-tech industries. The impact of all three environmental regulations on R&D investment was positive but not significant. Finally, based on these research conclusions, a few countermeasures and suggestions are discussed for the formulation of China’s environmental regulation policies and the development of high-tech industries.
Effects of Decentralized Water Regulation on Agriculture in China: A Quasi-Natural Experiment Based on Incentives for Promoting Officials
Authors: Chen Xiaojia , Li Yuanfen , Chen Yue , Xu Wei
Published: January 2023

Environmental performance is becoming increasingly essential for promoting local officials in China; thus, their pursuit of promotion may affect agricultural output. This study spatially matched Chinese local official promotion data, regional agricultural output, river-water-quality-monitoring stations, and riverside enterprise discharge data. Based on the difference-in-difference model, the exogenous impact of the natural experiment based on the promotion of officials is quantified as how the promotion behavior of local officials in pursuit of environmental achievements affects agricultural output. This was examined under the decentralization system of China’s environmental governance. The results show that local officials improve agricultural production by controlling environmental pollution through promotion incentives. However, since the central government can observe the regulatory effect of upstream officials through the readings of water monitoring stations, upstream officials strictly enforce the central environmental regulations due to promotion motivation, while downstream officials do not strictly enforce their counterparts. This can result in differentiated impacts on agriculture in upstream and downstream regions. We also carried out a parallel test, placebo test, and measurement error test for the quasi-natural experiment, and the conclusions derived from the analysis remained robust. Our study has important implications for designing compatible environmental governance contracts and incentive policies for promoting agricultural production.
Recent Advances in Alternative Cementitious Materials for Nuclear Waste Immobilization: A Review
Authors: Rakhimova Nailia
Published: January 2023

Since the emergence of the problem of nuclear waste conditioning, cementation has become an important and developing part of the waste management system, owing to its simplicity and versatility. The continued development of the cementation technique is driven by the improvement and expansion of cementitious materials that are suitable and efficient for nuclear waste solidification. Advances in cement theory and technology have significantly impacted improvements in nuclear waste cementation technology, the quality of fresh and hardened waste forms, waste loading rates, and the reliability and sustainability of the nuclear industry. Modern mineral matrices for nuclear waste immobilization are a broad class of materials with diverse chemical–mineralogical compositions, high encapsulation capacities, and technological and engineering performance. These matrices include not only traditional Portland cement, but also non-Portland clinker inorganic binders. This review focuses on recent trends and achievements in the development of calcium aluminate, calcium sulfoaluminate, phosphate, magnesium silicate, and alkali-activated cements as cementitious matrices for nuclear waste stabilization/solidification.
Placemaking and Tourism to Build Resilience: A Quest for Sustaining Peripheral Island Communities in Taiwan
Authors: Ni Chin-Cheng , Say Dietermar
Published: January 2023

Peripheral islands are prone to natural disasters. In the past, the literature on island community development focused on sustainability or vulnerability. However, resilience theory has gained attention as an alternate strategy due to unpredictable global evolution changes. Thus, this study explored how peripheral communities face disadvantageous global situations through adaption and cooperation within placemaking and tourism. We focused on two peripheral well-developed island communities, Nanliao and Xihu, in Penghu, Taiwan, and their approach to resilience. This study conducted a literature review, contextual analysis, field survey, and in-depth interview with a case study. The research results included the exploration of mixed placemaking, charity tourism, and the use of online interaction between the two communities. A resilient perspective, in which adaptive development (recovery), cooperative stability, and simultaneous transformation correspond to a third path, was explored. Our findings have challenged traditional dualism concepts, such as “top-down or bottom-up,” “global or local,” and “insiders or outsiders,” which seem to be increasingly meaningless in sustaining island communities.