bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Particle Geometry for PLGA-Based Nanoparticles: Preparation and In Vitro/In Vivo Evaluation
Authors: Kaplan Meryem , Öztürk Kıvılcım , Öztürk Süleyman Can , Tavukçuoğlu Ece , Esendağlı Güneş , Calis Sema
Published: January 2023

The physicochemical properties (size, shape, zeta potential, porosity, elasticity, etc.) of nanocarriers influence their biological behavior directly, which may result in alterations of the therapeutic outcome. Understanding the effect of shape on the cellular interaction and biodistribution of intravenously injected particles could have fundamental importance for the rational design of drug delivery systems. In the present study, spherical, rod and elliptical disk-shaped PLGA nanoparticles were developed for examining systematically their behavior in vitro and in vivo. An important finding is that the release of the encapsulated human serum albumin (HSA) was significantly higher in spherical particles compared to rod and elliptical disks, indicating that the shape can make a difference. Safety studies showed that the toxicity of PLGA nanoparticles is not shape dependent in the studied concentration range. This study has pioneering findings on comparing spherical, rod and elliptical disk-shaped PLGA nanoparticles in terms of particle size, particle size distribution, colloidal stability, morphology, drug encapsulation, drug release, safety of nanoparticles, cellular uptake and biodistribution. Nude mice bearing non-small cell lung cancer were treated with 3 differently shaped nanoparticles, and the accumulation of nanoparticles in tumor tissue and other organs was not statistically different (p > 0.05). It was found that PLGA nanoparticles with 1.00, 4.0 ± 0.5, 7.5 ± 0.5 aspect ratios did not differ on total tumor accumulation in non-small cell lung cancer.
Gliadin Nanoparticles Containing Doxorubicin Hydrochloride: Characterization and Cytotoxicity
Authors: Voci Silvia , Gagliardi Agnese , Ambrosio Nicola , Salvatici Maria Cristina , Fresta Massimo , Cosco Donato
Published: January 2023

Doxorubicin hydrochloride (DOX) is a well-known antitumor drug used as first line treatment for many types of malignancies. Despite its clinical relevance, the administration of the compound is negatively affected by dose-dependent off-target toxicity phenomena. Nanotechnology has helped to overcome these important limitations by improving the therapeutic index of the bioactive and promoting the translation of novel nanomedicines into clinical practice. Herein, nanoparticles made up of wheat gliadin and stabilized by polyoxyethylene (2) oleyl ether were investigated for the first time as carriers of DOX. The encapsulation of the compound did not significantly affect the physico-chemical features of the gliadin nanoparticles (GNPs), which evidenced a mean diameter of ~180 nm, a polydispersity index < 0.2 and a negative surface charge. The nanosystems demonstrated great stability regarding temperature (25–50 °C) and were able to retain high amounts of drug, allowing its prolonged and sustained release for up to a week. In vitro viability assay performed against breast cancer cells demonstrated that the nanoencapsulation of DOX modulated the cytotoxicity of the bioactive as a function of the incubation time with respect to the free form of the drug. The results demonstrate the potential use of GNPs as carriers of hydrophilic antitumor compounds.
In Vitro Evaluation of Photodynamic Activity of Plant Extracts from Senna Species against Microorganisms of Medical and Dental Interest
Authors: Oliveira Analú Barros de , Ferrisse Túlio Morandin , Annunzio Sarah Raquel de , Franca Maria Gleiziane Araújo , Silva Maria Goretti de Vasconcelos , Cavalheiro Alberto José , Fontana Carla Raquel , Brighenti Fernanda Lourenção
Published: January 2023

Background: Bacterial resistance requires new treatments for infections. In this context, antimicrobial photodynamic therapy (aPDT) is an effective and promising option. Objectives: Three plant extracts (Senna splendida, Senna alata, and Senna macranthera) were evaluated as photosensitizers for aPDT. Methods: Cutibacterium acnes (ATCC 6919), Streptococcus mutans (ATCC 35668), Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), and Candida albicans (ATCC 90028) were evaluated. Reactive oxygen species production was also verified. Oral keratinocytes assessed cytotoxicity. LC-DAD-MS analysis identified the chemical components of the evaluated extracts. Results: Most species cultured in the planktonic phase showed total microbial reduction (>6 log10 CFU/mL/p < 0.0001) for all extracts. C. albicans cultured in biofilm showed total microbial reduction (7.68 log10 CFU/mL/p < 0.0001) for aPDT mediated by all extracts. Extracts from S. macranthera and S. alata produced the highest number of reactive oxygen species (p < 0.0001). The S. alata extract had the highest cell viability. The LC-DAD-MS analysis of active extracts showed one naphthopyrone and seven anthraquinones as potential candidates for photoactive compounds. Conclusion: This study showed that aPDT mediated by Senna spp. was efficient in microbial suspension and biofilm of microorganisms of medical and dental interest.
Cell Division and Meristem Dynamics in Fern Gametophytes
Authors: Wu Xiao , Liu Xing , Zhang Shaoling , Zhou Yun
Published: January 2023

One of the most important questions in all multicellular organisms is how to define and maintain different cell fates during continuous cell division and proliferation. Plant meristems provide a unique research system to address this fundamental question because meristems dynamically maintain themselves and sustain organogenesis through balancing cell division and cell differentiation. Different from the gametophytes of seed plants that depend on their sporophytes and lack meristems, the gametophytes of seed-free ferns develop different types of meristems (including apical cell-based meristems and multicellular apical and marginal meristems) to promote independent growth and proliferation during the sexual gametophyte phase. Recent studies combining confocal time-lapse imaging and computational image analysis reveal the cellular basis of the initiation and proliferation of different types of meristems in fern gametophytes, providing new insights into the evolution of meristems in land plants. In this review, we summarize the recent progress in understanding the cell growth dynamics in fern gametophytes and discuss both conserved and diversified mechanisms underlying meristem cell proliferation in seed-free vascular plants.
Atmospheric Vapor Impact on Desert Vegetation and Desert Ecohydrological System
Authors: Xin Zhiming , Feng Wei , Zhan Hongbin , Bai Xuying , Yang Wenbin , Cheng Yiben , Wu Xiuqin
Published: January 2023

The ability of plants to absorb unsaturated atmospheric water vapor is a controversial topic. To study how vegetation in arid areas survives under limited water resources, this study uses Tamarisk in the Ulan Buh Desert of China as an example. The in-situ observation of a newly designed Lysimeter and sap flow meter system were used to monitor the precipitation infiltration and the utilization efficiency of Tamarisk of atmospheric vapor. The results show that the annual precipitation of 84 mm in arid areas could still result in deep soil recharge (DSR) with a recharge rate of 5 mm/year. Furthermore, DSR is detectable even in the winter, and the 5-year average DSR was 5.77% of the annual precipitation. It appears that the small precipitation events are critically important for the survival of Tamarisk. When the atmospheric relative humidity reaches 70%, Tamarisk leaves can absorb the unsaturated atmospheric vapor, which accounts for 13.2% of the annual precipitation amount. To adapt to the arid environment, Tamarisk can harvest its water supply from several sources including atmospheric vapor and micro-precipitation events (whose precipitation is below the measurement limit of 0.2 mm of the precipitation gauge) and can still permit a certain amount of recharge to replenish the deep soil moisture. Such an ecohydrological dynamic is of great significance to desert vegetation.
Prediction and Analysis of Nuclear Explosion Radioactive Pollutant Diffusion Model
Authors: Zheng Yang , Liu Wei , Li Xiaoqiang , Yang Ming , Li Peng , Wu Yunhui , Chen Xiaolei
Published: January 2023

This study presents a model for the dispersion of radioactive smoke clouds from a nuclear weapon explosion. A model based on a modified Settlement model is chosen to simulate the dispersion of radioactive contaminants from a nuclear explosion in the atmosphere. The arrival time and dose rate of radioactive fallout at various distances in the downwind direction are given for different equivalents of the surface explosion and typical meteorological conditions. Thus, the prediction of the dispersion of radioactive contaminants from a nuclear explosion can be achieved under the conditions of known nuclear explosion equivalence and local meteorological parameters. This provides a theoretical basis for the estimation of the affected environment and the input of rescue forces after the explosion.
Influence of Spinning Method on Shape Memory Effect of Thermoplastic Polyurethane Yarns
Authors: Benecke Lukas , Tonndorf Robert , Cherif Chokri , Aibibu Dilbar
Published: January 2023

Shape memory polymers are gaining increasing attention, especially in the medical field, due to their ability to recover high deformations, low activation temperatures, and relatively high actuation stress. Furthermore, shape memory polymers can be applied as fiber-based solutions for the development of smart devices used in many fields, e.g., industry 4.0, medicine, and skill learning. These kind of applications require sensors, actors, and conductive structures. Textile structures address these applications by meeting requirements such as being flexible, adaptable, and wearable. In this work, the influence of spinning methods and parameters on the effect of shape memory polymer yarns was investigated, comparing melt and wet spinning. It is shown that the spinning method can significantly influence the strain fixation and generated stress during the activation of the shape memory effect. Furthermore, for wet spinning, the draw ratio could affect the stress conversion, influencing its efficiency. Therefore, the selection of the spinning process is essential for the setting of application-specific shape-changing properties.
Different Production Processes for Thermoplastic Composite Materials: Sustainability versus Mechanical Properties and Processes Parameter
Authors: Valente Marco , Rossitti Ilaria , Sambucci Matteo
Published: January 2023

Up to now, fiber-reinforced composites with thermoplastic matrix have seen limited fields of use in the structural scope due to their high viscosity in the molten state, which results in poor impregnability of the reinforcement, leading to mechanical properties of the finished product that are not comparable to those of thermosets. Although the latter still dominate the various sectors of automotive, aerospace, transportation and construction, new applications involving the production of thermoplastic composites are growing rapidly, offering new approaches to the solution of this problem. The aim of this work is to study and evaluate the state of the art on the manufacturing processes of thermoplastic matrix composite, analyzing the parameters that come into play and that most influence the process and material performance. The advantages of film stacking and powder impregnation techniques are contrasted by the versatility of hybrid fabrics and, at the same time, parameters such as pressure and temperature must be carefully considered. A description of different thermoplastic composite processes such as powder impregnation, film stacking molding, hybrid woven fabric, hybrid yarn and products follows, which represent the current possibilities to move from a thermosetting matrix composite to a thermoplastic one, upon which the concept of sustainability is based. This article wants to present an overview of research that has been done in manufacturing thermoplastic reinforced composites and will serve as a baseline and aid for further research and development efforts.
Controllable Preparation of Superparamagnetic Fe3O4@La(OH)3 Inorganic Polymer for Rapid Adsorption and Separation of Phosphate
Authors: Lu Yao , Jin Xuna , Li Xiang , Liu Minpeng , Liu Baolei , Zeng Xiaodan , Chen Jie , Liu Zhigang , Yu Shihua , Xu Yucheng
Published: January 2023

Superparamagnetic Fe3O4 particles have been synthesized by solvothermal method, and a layer of dense silica sol polymer is coated on the surface prepared by sol-gel technique; then La(OH)3 covered the surface of silica sol polymer in an irregular shape by controlled in situ growth technology. These magnetic materials are characterized by TEM, FT-IR, XRD, SEM, EDS and VSM; the results show that La(OH)3 nanoparticles have successfully modified on Fe3O4 surface. The prepared Fe3O4@La(OH)3 inorganic polymer has been used as adsorbent to remove phosphate efficiently. The effects of solution pH, adsorbent dosage and co-existing ions on phosphate removal are investigated. Moreover, the adsorption kinetic equation and isothermal model are used to describe the adsorption performance of Fe3O4@La(OH)3. It was observed that Fe3O4@La(OH)3 exhibits a fast equilibrium time of 20 min, high phosphate removal rate (>95.7%), high sorption capacity of 63.72 mgP/g, excellent selectivity for phosphate in the presence of competing ions, under the conditions of phosphate concentration 30 mgP/L, pH = 7, adsorbent dose 0.6 g/L and room temperature. The phosphate adsorption process by Fe3O4@La(OH)3 is best described by the pseudo-second-order equation and Langmuir isotherm model. Furthermore, the real samples and reusability experiment indicate that Fe3O4@La(OH)3 could be regenerated after desorption, and 92.78% phosphate removing remained after five cycles. Therefore, La(OH)3 nanoparticles deposited on the surface of monodisperse Fe3O4 microspheres have been synthesized for the first time by a controlled in-situ growth method. Experiments have proved that Fe3O4@La(OH)3 particles with fast separability, large adsorption capacity and easy reusability can be used as a promising material in the treatment of phosphate wastewater or organic pollutants containing phosphoric acid functional group.
Study on Anti-Aging Performance Enhancement of Polymer Modified Asphalt with High Linear SBS Content
Authors: Han Daqian , Hu Guosheng , Zhang Jingting
Published: January 2023

Modified asphalt with high content SBS is widely used in asphalt pavement due to its excellent high and low temperature performance. However, its anti-aging performance is insufficient. In order to improve the anti-aging performance of SBS modified asphalt, nano-ZnO, nano-TiO2, nano-SiO2 and polyphosphoric acid (PPA) were added to high content (6.5 wt%) linear SBS modified asphalt as anti-aging agents in this study. Moreover, Dynamic Shear Rheometer (DSR), Fluorescence Microscope, and Fourier Transform Infrared Spectroscopy were employed to reveal the mechanism, through the investigation of the rheological and microscopic properties of modified asphalt before and after aging. The results showed that the influence of nanoparticles on the rutting resistance and fatigue resistance of high content SBS modified asphalt is weak, mainly because there is only weak physical interaction between nanoparticles and the SBS modifier, but no obvious chemical reaction. The significant cross-networking structure of high content SBS modified asphalt even has an adverse effect on the anti-aging performance of nano-modifiers. However, PPA obviously makes the cross-linked network structure of SBS modified asphalt more compact, and significantly improves the performance after short-term aging and long-term aging, mainly due to the chemical reaction between PPA and the active groups in SBS modified asphalt.
MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach
Authors: Soliman Abanob , Hadj-Abdelkader Hicham , Bonardi Fabien , Bouchafa Samia , Sidibé Désiré
Published: January 2023

Developing new sensor fusion algorithms has become indispensable to tackle the daunting problem of GPS-aided micro aerial vehicle (MAV) localization in large-scale landscapes. Sensor fusion should guarantee high-accuracy estimation with the least amount of system delay. Towards this goal, we propose a linear optimal state estimation approach for the MAV to avoid complicated and high-latency calculations and an immediate metric-scale recovery paradigm that uses low-rate noisy GPS measurements when available. Our proposed strategy shows how the vision sensor can quickly bootstrap a pose that has been arbitrarily scaled and recovered from various drifts that affect vision-based algorithms. We can consider the camera as a “black-box” pose estimator thanks to our proposed optimization/filtering-based methodology. This maintains the sensor fusion algorithm’s computational complexity and makes it suitable for MAV’s long-term operations in expansive areas. Due to the limited global tracking and localization data from the GPS sensors, our proposal on MAV’s localization solution considers the sensor measurement uncertainty constraints under such circumstances. Extensive quantitative and qualitative analyses utilizing real-world and large-scale MAV sequences demonstrate the higher performance of our technique in comparison to most recent state-of-the-art algorithms in terms of trajectory estimation accuracy and system latency.
Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment
Authors: Hasan Md. Mahadi , Islam Muhammad Usama , Sadeq Muhammad Jafar , Fung Wai-Keung , Uddin Jasim
Published: January 2023

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe
Authors: Pospelova Vera , López-Baldominos Inés , Fernández-Sanz Luis , Castillo-Martínez Ana , Misra Sanjay
Published: January 2023

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice.
MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction
Authors: Hasan Fuad , Huang Hailong
Published: January 2023

Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer’s mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction.
A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review
Authors: Yen Jia Min , Lim Jeong Hoon
Published: January 2023

Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed.
Continuous and Secure Integration Framework for Smart Contracts
Authors: Reyes Alvaro , Jimeno Miguel , Villanueva-Polanco Ricardo
Published: January 2023

As part of agile methodologies seen in the past few years, IT organizations have continuously adopted new practices in their software delivery life-cycle to improve both efficiency and effectiveness of development teams. Two of these practices are continuous integration and continuous deployment, which are part of the DevOps cycle which has helped organizations build software effectively and efficiently. These practices must be considered for new technologies such as smart contracts, where security concerns and bugs might cost more once deployed than traditional software. This paper states the importance of using a proper DevOps routine and how it is possible to apply this practice to a smart contract build. Specifically, this paper introduces a framework to implement DevOps for smart contracts development by describing multiple DevOps tools and their applicability to smart contract development.
Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving
Authors: Xie Xing , Wei Haowen , Yang Yongjie
Published: January 2023

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.
Speeding Task Allocation Search for Reconfigurations in Adaptive Distributed Embedded Systems Using Deep Reinforcement Learning
Authors: Rotaeche Ramón , Ballesteros Alberto , Proenza Julián
Published: January 2023

A Critical Adaptive Distributed Embedded System (CADES) is a group of interconnected nodes that must carry out a set of tasks to achieve a common goal, while fulfilling several requirements associated with their critical (e.g., hard real-time requirements) and adaptive nature. In these systems, a key challenge is to solve, in a timely manner, the combinatorial optimization problem involved in finding the best way to allocate the tasks to the available nodes (i.e., the task allocation) taking into account aspects such as the computational costs of the tasks and the computational capacity of the nodes. This problem is not trivial and there is no known polynomial time algorithm to find the optimal solution. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems and, in this work, we explore the application of such approaches to the task allocation problem in CADESs. We first discuss the potential advantages of using a DRL-based approach over several heuristic-based approaches to allocate tasks in CADESs and we then demonstrate how a DRL-based approach can achieve similar results for the best performing heuristic in terms of optimality of the allocation, while requiring less time to generate such allocation.
Design and Implementation of a Passive Autoranging Circuit for Hybrid FBG-PZT Photonic Current Transducer
Authors: Mir Burhan , Niewczas Pawel , Fusiek Grzegorz
Published: January 2023

In this paper, we present a novel technique for passively autoranging a photonic current transducer (PCT) that incorporates a current transformer (CT), piezoelectric transducer (PZT) and fiber Bragg grating (FBG). Due to the usage of single-mode fiber and FBG, multiple PCTs can be interconnected and distributed over a long distance, for example along a power network, greatly reducing the cost of sensor deployment and offering other unique advantages. The autoranging technique relies on the usage of multiple, serially connected CT burden resistors and associated static MOSFET switches to realize instantaneous shortening of the resistors in response to increasing measured current. This functionality is realized passively, utilizing a modular, μW-power comparator circuit that powers itself from the electrical energy supplied by the CT within a small fraction of the 50/60 Hz cycle. The resultant instantaneous changes in sensor gain will be ultimately detected by the central FBG interrogator through real-time analysis of the optical signals and will be used to apply appropriate gain scaling for each sensor. The technique will facilitate the usage of a single PCT to cover an extended dynamic range of the measurement that is required to realize a combined metering- and protection-class current sensor. This paper is limited to the description of the design process, construction, and testing of a prototype passive autoranging circuitry for integration with the PCT. The two-stage circuitry that is based on two burden resistors, 1 Ω and 10 Ω, is used to prove the concept and demonstrate the practically achievable circuit characteristics. It is shown that the circuit correctly reacts to input current threshold breaches of approximately 2 A and 20 A within a 3 ms reaction time. The circuit produces distinct voltage dips across burden resistors that will be used for signal scaling by the FBG interrogator.
Battery-Free and Real-Time Wireless Sensor System on Marine Propulsion Shaft Using a Wireless Power Transfer Module
Authors: Lee Young Chul , Hoang Van Ai
Published: January 2023

In this paper, we present a wireless sensor system (WSS) that integrated an inductive wireless power transfer (I-WPT) module for battery-free real-time monitoring of the status of rotating shaft in ships. Firstly, an optimized I-WPT module for seamless power supply was implemented using multiple Tx and Rx coils, and its power capability of 1.75 W with an efficiency of 75% was achieved. Secondly, as a result of the high-power transfer performance of the implemented I-WPT module, an entire WSS that integrated four sensors was designed on the rotary shaft. Finally, the designed WSS was installed on a small-scale test bench system with a shaft diameter of 200 mm; it was demonstrated that the status of a propulsion shaft could be monitored in real time without a battery.
Measurement Reduction Methods for Processing Tomographic Images
Authors: Chulichkov Alexey I. , Balakin Dmitriy A.
Published: January 2023

The importance of development of new methods for reconstruction of an object image given its sinogram and some additional information about the object stems from the possibility of artifact presence in the reconstructed image, or its insufficient sharpness when the used additional information does not hold. The problem of recovering artifact-free images of the studied object from tomography data is considered in the framework of the theory of computer-aided measuring systems. Methods for solving it are developed. They are based on narrowing the class of possible images using less artifact-inducing information. An example of such information is the natural condition of non-negativeness of the estimated brightnesses. The main problem that arises is the large dimensionality of the images, which prevents the use of direct algorithms. One proposed method is based on local approach, namely correction of the result of unfiltered backprojection by applying a locally (in the space of the output image) optimal linear transformation. Another method processes a sinogram directly, without using backprojection, using iterative implementation of the measurement reduction technique. Examples of use of the proposed methods for processing teeth sinograms are given.
ESPRESS.0: Eustachian Tube-Inspired Tactile Sensor Exploiting Pneumatics for Range Extension and SenSitivity Tuning
Authors: Jenkinson George P. , Conn Andrew T. , Tzemanaki Antonia
Published: January 2023

Optimising the sensitivity of a tactile sensor to a specific range of stimuli magnitude usually compromises the sensor’s widespread usage. This paper presents a novel soft tactile sensor capable of dynamically tuning its stiffness for enhanced sensitivity across a range of applied forces, taking inspiration from the Eustachian tube in the mammalian ear. The sensor exploits an adjustable pneumatic back pressure to control the effective stiffness of its 20 mm diameter elastomer interface. An internally translocated fluid is coupled to the membrane and optically tracked to measure physical interactions at the interface. The sensor can be actuated by pneumatic pressure to dynamically adjust its stiffness. It is demonstrated to detect forces as small as 0.012 N, and to be sensitive to a difference of 0.006 N in the force range of 35 to 40 N. The sensor is demonstrated to be capable of detecting tactile cues on the surface of objects in the sub-millimetre scale. It is able to adapt its compliance to increase its ability for distinguishing between stimuli with similar stiffnesses (0.181 N/mm difference) over a large range (0.1 to 1.1 N/mm) from only a 0.6 mm deep palpation. The sensor is intended to interact comfortably with skin, and the feasibility of its use in palpating tissue in search of hard inclusions is demonstrated by locating and estimating the size of a synthetic hard node embedded 20 mm deep in a soft silicone sample. The results suggest that the sensor is a good candidate for tactile tasks involving unpredictable or unknown stimuli.
Sensor Technology for Sports Monitoring
Authors: Linnamo Vesa
Published: January 2023

Over the past decades, huge steps have been made in the development of sensor technology related to sports monitoring [...]
3-D Data Interpolation and Denoising by an Adaptive Weighting Rank-Reduction Method Using Multichannel Singular Spectrum Analysis Algorithm
Authors: Bayati Farzaneh , Trad Daniel
Published: January 2023

Addressing insufficient and irregular sampling is a difficult challenge in seismic processing and imaging. Recently, rank reduction methods have become popular in seismic processing algorithms for simultaneous denoising and interpolating. These methods are based on rank reduction of the trajectory matrices using truncated singular value decomposition (TSVD). Estimation of the ranks of these trajectory matrices depends on the number of plane waves in the processing window; however, for the more complicated data, the rank reduction method may fail or give poor results. In this paper, we propose an adaptive weighted rank reduction (AWRR) method that selects the optimum rank in each window automatically. The method finds the maximum ratio of the energy between two singular values. The AWRR method selects a large rank for the highly curved complex events, which leads to remaining residual errors. To overcome the residual errors, a weighting operator on the selected singular values minimizes the effect of noise projection on the signal projection. We tested the efficiency of the proposed method by applying it to both synthetic and real seismic data.
Toward Large-Scale Energy Harvesting by a UV-Curable Organic-Coating-Based Triboelectric Nanogenerator
Authors: Chen Jian , Tang Ning , Cheng Li , Zheng Youbin
Published: January 2023

Triboelectric nanogenerators (TENGs) stand out as an attractive form of technology for the efficient harvest of mechanical energy and the powering of wearable devices due to their light weight, simplicity, high power density, and efficient vibration energy scavenging capabilities. However, the requirement for micro/nanostructures and/or complex and expensive instruments hinders their cheap mass production, thus limiting their practical applications. By using a simple, cost-effective, fast spray-coating process, we develop high-performance UV-curable triboelectric coatings for large-scale energy harvesting. The effect of different formulations and coating compositions on the triboelectric output is investigated to design triboelectric coatings with high output performance. The TENG based on a hybrid coating exhibits high output performance of 54.5 μA current, 1228.9 V voltage, 163.6 nC transferred charge and 3.51 mW output power. Moreover, the hybrid coatings show good long-term output stability. All the results indicate that the designed triboelectric coatings show great potential for large-scale energy harvesting with the advantages of cost-effectiveness, fast fabrication, easy mass production and long-term stability.
Classification of Acoustic Influences Registered with Phase-Sensitive OTDR Using Pattern Recognition Methods
Authors: Barantsov Ivan A. , Pnev Alexey B. , Koshelev Kirill I. , Tynchenko Vadim S. , Nelyub Vladimir A. , Borodulin Aleksey S.
Published: January 2023

This article is devoted to the development of a classification method based on an artificial neural network architecture to solve the problem of recognizing the sources of acoustic influences recorded by a phase-sensitive OTDR. At the initial stage of signal processing, we propose the use of a band-pass filter to collect data sets with an increased signal-to-noise ratio. When solving the classification problem, we study three widely used convolutional neural network architectures: AlexNet, ResNet50, and DenseNet169. As a result of computational experiments, it is shown that the AlexNet and DenseNet169 architectures can obtain accuracies above 90%. In addition, we propose a novel CNN architecture based on AlexNet, which obtains the best results; in particular, its accuracy is above 98%. The advantages of the proposed model include low power consumption (400 mW) and high speed (0.032 s per net evaluation). In further studies, in order to increase the accuracy, reliability, and data invariance, the use of new algorithms for the filtering and extraction of acoustic signals recorded by a phase-sensitive reflectometer will be considered.
Omnidirectional Haptic Stimulation System via Pneumatic Actuators for Presence Presentation
Authors: Yoshida Shogo , Xie Haoran , Miyata Kazunori
Published: January 2023

Recently, remote meetings and work-from-home have become more common, reducing the opportunities for face-to-face communication. To facilitate communication among remote workers, researchers have focused on virtual space technology and spatial augmented reality technology. Although these technologies can enhance immersiveness in collaborative work, they face the challenge of fostering a sense of physical contact. In this work, we aimed to foster a sense of presence through haptic stimulation using pneumatic actuators. Specifically, we developed a choker-type wearable device that presents various pressure patterns around the neck; the pattern presented depends on the message the device must convey. Various combinations of haptic presentation are achieved by pumping air to the multiple pneumatic actuators attached to the choker. In addition, we conducted experiments involving actuators of different shapes to optimize the haptic presentation. When linked with a smartphone, the proposed device can present pressure patterns to indicate incoming calls and notifications, to give warning about an obstacle that one who is texting might miss while walking, and to provide direction to a pedestrian. Furthermore, the device can be used in a wide range of applications, from those necessary in daily living to those that enhance one’s experience in the realm of entertainment. For example, haptic feedback that synchronizes with the presence of a singer or with the rhythm of a song one listens to or with a performer’s movements during a stage performance will immerse users in an enjoyable experience.
CTD Sensors for Ocean Investigation Including State of Art and Commercially Available
Authors: Xiao Shiyu , Zhang Mingliang , Liu Changhua , Jiang Chongwen , Wang Xiaodong , Yang Fuhua
Published: January 2023

Over 70% of the earth’s surface is covered by oceans; globally, oceans provides a huge source of wealth to humans. In the literature, several sensors have been developed to investigate oceans. Electrical conductivity temperature depth (CTD) sensors were used frequently and extensively. Long-term accurate CTD data is important for the study and utilization of oceans, e.g., for weather forecasting, ecological evolution, fishery, and shipping. Several kinds of CTD sensors based on electrics, optical, acoustic wave and radio waves have been developed. CTD sensors are often utilized by measuring electrical signals. The latest progress of CTD sensors will be presented in order of performance. The principles, structure, materials and properties of many CTD sensors were discussed in detail. The commercially available CTD sensors were involved and their respective performances were compared. Some possible development directions of CTD sensors for ocean investigation are proposed.
Assessing Tenth-Grade Students’ Mathematical Literacy Skills in Solving PISA Problems
Authors: Almarashdi Hanan Shaher , Jarrah Adeeb M.
Published: January 2023

We tested the mathematical literacy (ML) proficiency of 204 grade 10 “advanced track” students in the United Arab Emirates (UAE) using 34 questions from the Programme for International Student Assessment (PISA) 2012 released items. Using a quantitative descriptive approach, we analyzed the performance of males (n = 106) and females (n = 98) on items spread across the four content subdomains (quantity, space and shape, change and relationship, and uncertainty), the processes of problem-solving (formulate, employ, reasoning, and interpret), and the four PISA contexts (personal, occupational, societal, and scientific) across six PISA proficiency levels. The test was formulated in three types of response format (multiple choice, closed- and open-ended). Results showed that overall student performance was in the very low category. Female students outperformed male students in all aspects of mathematical literacy. The highest percentage achieved in the modelling process was in the interpreting and then employ and formulate tasks. Students performed almost perfectly in problems that covered the uncertainty content area but fared poorly with problems dealing with change and relationship. Students performed better on personal mathematical problems than occupational, societal or scientific problems. These findings are important for UAE teachers regarding direct application to classroom practice and for researchers and officials to shape future research and recommendations for future new educational policies.
Analysis of Systemic Risk Scenarios and Stabilization Effect of Monetary Policy under the COVID-19 Shock and Pharmaceutical Economic Recession
Authors: Dong Hao , Zheng Yingrong , Li Na
Published: January 2023

The Global Financial Crisis (GFC) will cause turbulence in the pharmaceutical market and the stagnation of market liquidity, leading to a deep recession in the pharmaceutical economy. After the COVID-19 outbreak, the pharmaceutical economic recession and the rising pharmaceutical financial crisis caused by the closure and control of the COVID-19 outbreak in China were important reasons for the accumulation of systemic financial risks in China. To realize the pharmaceutical economy and financial stability, this paper studies the weakening mechanism of the stabilization effect in systemic risk scenarios and analyzes how the evolution of systemic risk under the COVID-19 shock affects the stabilization effect of monetary policy. Under the COVID-19 shock, in the stage of falling China Financial Stress Index (CFSI), the systemic risk is relatively low, and the impact of traditional policy on macroeconomic stability is more significant; in the rising stage of CFSI, the systemic risk is relatively high, and the impact of traditional policy on macroeconomic stability is limited. This paper develops a Time-Varying Modified CRITIC weighting method and constructs a Time-Varying CFSI. This paper identifies systemic risk scenarios under the COVID-19 shock based on the Markov-Switching Mean Heteroskedastic Vector Auto-Regressive (MSMH-VAR) model and evaluates the stabilizing effects of monetary policy in different economic and financial regional systems (normal times and systemic risk scenarios). The results show that in normal times, loose monetary policy increases price levels, and tight monetary policy reduces price levels with a time lag. In systemic risk scenarios under the COVID-19 shock, the easing effect of policy on output growth is relatively small, and tighter policy increases output growth and prices in the short run and increases volatility in output growth and price levels in the long run. That is, under the COVID-19 shock in systemic risk scenarios, it is difficult to achieve stable growth and stable prices with monetary policy, and the stabilization effect is weakened. This paper focuses on the relationship between systemic risks, monetary policy, and output stability under the COVID-19 shock, analyzes the weakening of stabilization effects after the crisis, and expands the theoretical path of monetary policy stabilization and enriches the research scope of the new framework.
Numerical Contrivance for Kawahara-Type Differential Equations Based on Fifth-Kind Chebyshev Polynomials
Authors: Abd-Elhameed Waleed Mohamed , Alkhamisi Seraj Omar , Amin Amr Kamel , Youssri Youssri Hassan
Published: January 2023

This article proposes a numerical algorithm utilizing the spectral Tau method for numerically handling the Kawahara partial differential equation. The double basis of the fifth-kind Chebyshev polynomials and their shifted ones are used as basis functions. Some theoretical results of the fifth-kind Chebyshev polynomials and their shifted ones are used in deriving our proposed numerical algorithm. The nonlinear term in the equation is linearized using a new product formula of the fifth-kind Chebyshev polynomials with their first derivative polynomials. Some illustrative examples are presented to ensure the applicability and efficiency of the proposed algorithm. Furthermore, our proposed algorithm is compared with other methods in the literature. The presented numerical method results ensure the accuracy and applicability of the presented algorithm.