bluuebunny/crossref_metadata_2025 huggingface dataset:
Applicability of Electron-Beam and Hybrid Plasmas for Polyethylene Terephthalate Processing to Obtain Hydrophilic and Biocompatible Surfaces
Authors: Vasilieva Tatiana , Nikolskaya Elena , Vasiliev Michael , Mollaeva Mariia , Chirkina Margarita , Sokol Maria , Yabbarov Nikita , Shikova Tatiana , Abramov Artem , Ugryumov Aleksandr
Published: January 2024

The applicability of beam-plasma chemical reactors generating cold hybrid plasma for the production of noncytotoxic polymeric surfaces with high hydrophilicity and good biocompatibility with human fibroblast culture and human red blood cells was studied. Oxygen hybrid plasma was excited by the joint action of a continuous scanning electron beam and a capacity-coupled RF-gas discharge. Experiments showed that hybrid plasma treatment caused polar oxygen-containing functional group formation in the surface layer of poly (ethylene terephthalate) films. No thermal or radiative damage in tested polymer samples was found. The plasma-modified polymers turned out to be noncytotoxic and revealed good biocompatibility with human fibroblasts BJ-5ta as well as lower hemolytic activity than untreated poly (ethylene terephthalate). Experiments also demonstrated that no phenomena caused by the electrostatic charging of polymers occur in hybrid plasma because the electron beam component of hybrid plasma eliminates the item charge when it is treated. The electron beam can effectively control the reaction volume geometry as well as the fluxes of active plasma particles falling on the item surface. This provides new approaches to the production of abruptly structured patterns or smooth gradients of functionalities on a plane and 3D polymeric items of complicated geometry.
Competitive Effect of Zinc and Cadmium on the Biosorption of Chromium by Orange Waste
Authors: Pérez-Marín Ana Belén , Ortuño Juan Francisco , Aguilar María Isabel , Lloréns Mercedes , Meseguer Víctor Francisco
Published: January 2024

Batch experiments were conducted to test orange waste (OW), an agricultural solid waste byproduct from the orange juice manufacturing industry, as adsorbent for binary solutions of Cd2+-Cr3+ and Zn2+-Cr3+. Fourier transform infrared spectroscopy (FTIR) and the point of zero charge (pHpzc) were used to identify the functional groups on the OW surface involved in biosorption. The biosorption equilibrium data for both binary-metal solutions were obtained and fitted to various isotherm models. The extended Sips and the non-modified Redlich-Peterson isotherm models gave the best fit for the experimental data. According to the extended Sips model, the maximum biosorption capacity of OW was 0.573 mmol·g−1 for Cd2+, 0.453 mmol·g−1 for Zn2+, and 1.96 mmol·g−1 for Cr3+. The sorption capacity dropped to 0.061 mmol·g−1 for Cd2+ and to 0.101 mmol·g1 for Zn2+ in their binary systems with Cr3+ for the higher initial metal concentrations in the solution. However, the maximum sorption capacity of chromium was only slightly affected by the presence of Cd2+ or Zn2+. For both binary systems, the presence of a second metal ion in the solution always conduces to a reduction in the sorption of the other metal in the solution. The presence of Cr3+ decreased the sorption of Cd2+ and Zn2+ more than vice versa. Conclusively, effective removal of Cr3+ ions from an aqueous solution can still be achieved in the presence of Cd2+ or Zn2+.
Monitoring Suspended Sediment Transport in the Lower Yellow River using Landsat Observations
Authors: Duan Mengwei , Qiu Zhiqiang , Li Ruren , Li Keyu , Yu Shujie , Liu Dong
Published: January 2024

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport.
A Lightweight Arbitrarily Oriented Detector Based on Transformers and Deformable Features for Ship Detection in SAR Images
Authors: Chen Bingji , Xue Fengli , Song Hongjun
Published: January 2024

Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, this paper proposes a lightweight transformer-based method for detecting arbitrarily oriented ships in SAR images, called LD-Det, which excels at promptly and accurately identifying rotating ship targets. First, light pyramid vision transformer (LightPVT) is introduced as a lightweight backbone network. Built upon PVT v2-B0-Li, it effectively captures the long-range dependencies of ships in SAR images. Subsequently, multi-scale deformable feature pyramid network (MDFPN) is constructed as a neck network, utilizing the multi-scale deformable convolution (MDC) module to adjust receptive field regions and extract ship features from SAR images more effectively. Lastly, shared deformable head (SDHead) is proposed as a head network, enhancing ship feature extraction with the combination of deformable convolution operations and a shared parameter structure design. Experimental evaluations on two publicly available datasets validate the efficacy of the proposed method. Notably, the proposed method achieves state-of-the-art detection performance when compared with other lightweight methods in detecting rotated targets.
Exploring the Potential of OpenStreetMap Data in Regional Economic Development Evaluation Modeling
Authors: Wang Zhe , Zheng Jianghua , Han Chuqiao , Lu Binbin , Yu Danlin , Yang Juan , Han Linzhi
Published: January 2024

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.
Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review
Authors: Naaz Farheen , Nauman Ali , Khurshaid Tahir , Kim Sung-Won
Published: January 2024

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.
Adaptive Event-Triggered Consensus of Multi-Agent Systems in Sense of Asymptotic Convergence
Authors: Hou Zhicheng , Zhou Zhikang , Yuan Hai , Wang Weijun , Wang Jian , Xu Zheng
Published: January 2024

In this paper, the asymptotic consensus control of multi-agent systems with general linear agent dynamics is investigated. A neighbor-based adaptive event-triggering strategy with a dynamic triggering threshold is proposed, which leads to a fully distributed control of the multi-agent system, depending only on the states of the neighboring agents at triggering moments. By using the Lyapunov method, we prove that the states of the agents converge asymptotically. In addition, the proposed event-triggering strategy is proven to exclude Zeno behavior. The numerical simulation results illustrate that the agent states achieve consensus in sense of asymptotic convergence. Furthermore, the proposed strategy is shown to be scalable in case of variable agent numbers.
A Study on Refraction Error Compensation Method for Underwater Spinning Laser Scanning Three-Dimensional Imaging
Authors: Zhang Jinghui , Wang Yuhang , Zhang Tao , Yang Kai , Zhang Jian , Wang Xinyu
Published: January 2024

Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object’s surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.
Layer-by-Layer Immobilization of DNA Aptamers on Ag-Incorporated Co-Succinate Metal–Organic Framework for Hg(II) Detection
Authors: Patil Shubham S. , Narwade Vijaykiran N. , Sontakke Kiran S. , Hianik Tibor , Shirsat Mahendra D.
Published: January 2024

Layer-by-layer (LbL) immobilization of DNA aptamers in the realm of electrochemical detection of heavy metal ions (HMIs) offers an enhancement in specificity, sensitivity, and low detection limits by leveraging the cross-reactivity obtained from multiple interactions between immobilized aptamers and developed material surfaces. In this research, we present a LbL approach for the immobilization of thiol- and amino-modified DNA aptamers on a Ag-incorporated cobalt-succinate metal–organic framework (MOF) (Ag@Co-Succinate) to achieve a cross-reactive effect on the electrochemical behavior of the sensor. The solvothermal method was utilized to synthesize Ag@Co-Succinate, which was also characterized through various techniques to elucidate its structure, morphology, and presence of functional groups, confirming its suitability as a host matrix for immobilizing both aptamers. The Ag@Co-Succinate aptasensor exhibited extraordinary sensitivity and selectivity towards Hg(II) ions in electrochemical detection, attributed to the unique binding properties of the immobilized aptamers. The exceptional limit of detection of 0.3 nM ensures the sensor’s suitability for trace-level Hg(II) detection in various environmental and analytical applications. Furthermore, the developed sensor demonstrated outstanding repeatability, highlighting its potential for long-term and reliable monitoring of Hg(II).
Using Large Language Models to Enhance the Reusability of Sensor Data
Authors: Berenguer Alberto , Morejón Adriana , Tomás David , Mazón Jose-Norberto
Published: January 2024

The Internet of Things generates vast data volumes via diverse sensors, yet its potential remains unexploited for innovative data-driven products and services. Limitations arise from sensor-dependent data handling by manufacturers and user companies, hindering third-party access and comprehension. Initiatives like the European Data Act aim to enable high-quality access to sensor-generated data by regulating accuracy, completeness, and relevance while respecting intellectual property rights. Despite data availability, interoperability challenges impede sensor data reusability. For instance, sensor data shared in HTML formats requires an intricate, time-consuming processing to attain reusable formats like JSON or XML. This study introduces a methodology aimed at converting raw sensor data extracted from web portals into structured formats, thereby enhancing data reusability. The approach utilises large language models to derive structured formats from sensor data initially presented in non-interoperable formats. The effectiveness of these language models was assessed through quantitative and qualitative evaluations in a use case involving meteorological data. In the proposed experiments, GPT-4, the best performing LLM tested, demonstrated the feasibility of this methodology, achieving a precision of 93.51% and a recall of 85.33% in converting HTML to JSON/XML, thus confirming its potential in obtaining reusable sensor data.
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
Authors: Khoo Lin Sze , Lim Mei Kuan , Chong Chun Yong , McNaney Roisin
Published: January 2024

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.
Hand Gesture Recognition Using FSK Radar Sensors
Authors: Yang Kimoon , Kim Minji , Jung Yunho , Lee Seongjoo
Published: January 2024

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user’s pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.
STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring
Authors: Kushnir Anna , Kachmar Oleh , Bonnechère Bruno
Published: January 2024

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient’s progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients’ quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.
Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network
Authors: Tao Haohan , Jia Peng , Wang Xiangyu , Wang Liquan
Published: January 2024

This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods.
Effects of Manual Therapy on Parkinson’s Gait: A Systematic Review
Authors: Delafontaine Arnaud , Vialleron Thomas , Barbier Gaëtan , Lardon Arnaud , Barrière Mélodie , García-Escudero María , Fabeck Laurent , Descarreaux Martin
Published: January 2024

Manual therapy (MT) is commonly used in rehabilitation to deal with motor impairments in Parkinson’s disease (PD). However, is MT an efficient method to improve gait in PD? To answer the question, a systematic review of clinical controlled trials was conducted. Estimates of effect sizes (reported as standard mean difference (SMD)) with their respective 95% confidence interval (95% CI) were reported for each outcome when sufficient data were available. If data were lacking, p values were reported. The PEDro scale was used for the quality assessment. Three studies were included in the review. MT improved Dynamic Gait Index (SMD = 1.47; 95% CI: 0.62, 2.32; PEDro score: 5/10, moderate level of evidence). MT also improved gait performances in terms of stride length, velocity of arm movements, linear velocities of the shoulder and the hip (p < 0.05; PEDro score: 2/10, limited level of evidence). There was no significant difference between groups after MT for any joint’s range of motion during gait (p > 0.05; PEDro score: 6/10, moderate level of evidence). There is no strong level of evidence supporting the beneficial effect of MT to improve gait in PD. Further randomized controlled trials are needed to understand the impact of MT on gait in PD.
Hydrogen Peroxide and Dopamine Sensors Based on Electrodeposition of Reduced Graphene Oxide/Silver Nanoparticles
Authors: Zhang Yuhang , Li Na , Liu Bo , Zhang Hangyu
Published: January 2024

In this work, silver nanoparticles (AgNPs)/reduced graphene oxide (rGO) nanocomposites were electrodeposited on glassy carbon electrodes (GCE) to construct electrochemical sensors for the detection of hydrogen peroxide (H2O2) and dopamine (DA). The AgNPs were synthesized on graphene oxide (GO) by the hydrothermal method, followed by the reduction of the GO during the electrodeposition process, resulting in the formation of the nanocomposites on the surface of the electrodes. The generation of AgNPs on the graphene sheets was verified by scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The AgNPs/rGO/GCE showed a linear response to H2O2 in the range of 5 μM to 620 μM, with a sensitivity of 49 μA mM−1cm−2 and a limit of detection (LOD) of 3.19 μA. The linear response of the AgNPs/rGO/GCE to DA ranged from 1 μM to 276 μM, the sensitivity was 7.86 μA mM−1cm−2, and the LOD was 0.18 μM. Furthermore, DA and H2O2 were detected simultaneously in the same solution without interferences, and the sensors displayed good stability over time. The preparation method for the sensors is relatively eco-friendly, convenient, and efficient, exhibiting great potential for sensitive detection of DA and H2O2.
Towards an AI-Driven Data Reduction Framework for Smart City Applications
Authors: Pioli Laercio , de Macedo Douglas D. J. , Costa Daniel G. , Dantas Mario A. R.
Published: January 2024

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.
Satellite Navigation Message Authentication in GNSS: Research on Message Scheduler for SBAS L1
Authors: Song Jiangyao , Liu Ting , Chen Xiao , Wu Zhongwang
Published: January 2024

SBAS is mainly used in civil aviation and navigation, and will be applied to autonomous driving in the future. Given the open signal format of the Satellite Based Augmentation System (SBAS), which exposes security threats such as spoofing attacks, the utilization of SBAS navigation message authentication technology can improve the SBAS anti-spoofing ability from the system side. SBAS message authentication technology has become the future direction of SBAS system development. However, during the initial design of SBAS on L1, message authentication technology was not considered, and the addition of authentication messages will lead to further strain on existing message bandwidth resources. Therefore, in response to the issue of insufficient bandwidth resources after adding authentication messages to SBAS L1, a study on message scheduler for SBAS L1 authentication was conducted. A fixed time sequence dynamic message scheduler for incorporating authentication messages was proposed. This scheduler reduces the frequency of broadcasting clock error parameters to mitigate the impact of adding authentication messages. Furthermore, an optimized fixed time sequence dynamic message scheduler based on SBAS clock error messages was introduced. The results show that the message scheduler can not only improve the flexibility of SBAS message broadcasting, but also shorten the update interval of various types of messages under the premise of meeting the maximum update interval requirement. With minimal impact on the maximum message update interval, it improves the integrity, authenticity, and availability of messages. This approach can increase the effective message ratio in SBAS to over 91%, and the optimal solution reduces the initial user positioning time to 26 s.
An Investigation of the Energy Harvesting Capabilities of a Novel Three-Dimensional Super-Cell Phononic Crystal with a Local Resonance Structure
Authors: Xiang Hang , Chai Zhemin , Kou Wenjun , Zhong Huanchao , Xiang Jiawei
Published: January 2024

Using the piezoelectric (PZT) effect, energy-harvesting has become possible for phononic crystal (PnC). Low-frequency vibration energy harvesting is more of a challenge, which can be solved by local resonance phononic crystals (LRPnCs). A novel three-dimensional (3D) energy harvesting LRPnC is proposed and further analyzed using the finite element method (FEM) software COMSOL. The 3D LRPnC with spiral unit-cell structures is constructed with a low initial frequency and wide band gaps (BGs). According to the large vibration deformation of the elastic beam near the scatterer, a PZT sheet is mounted in the surface of that beam, to harvest the energy of elastic waves using the PZT effect. To further improve the energy-harvesting performance, a 5 × 5 super-cell is numerically constructed. Numerical simulations show that the present 3D super-cell PnC structure can make full use of the advantages of the large vibration deformation and the PZT effect, i.e., the BGs with a frequency range from 28.47 Hz to 194.21 Hz with a bandwidth of 142.7 Hz, and the maximum voltage output is about 29.3 V under effective sound pressure with a peak power of 11.5 µW. The present super-cell phononic crystal structure provides better support for low-frequency vibration energy harvesting, when designing PnCs, than that of the traditional Prague type.
Sensing the Intentions to Speak in VR Group Discussions
Authors: Chen Jiadong , Gu Chenghao , Zhang Jiayi , Liu Zhankun , Konomi Shin‘ichi
Published: January 2024

While virtual reality (VR) technologies enable remote communication through the use of 3D avatars, it is often difficult to foster engaging group discussions without addressing the limitations to the non-verbal communication among distributed participants. In this paper, we discuss a technique to detect the intentions to speak in group discussions by tapping into intricate sensor data streams from VR headsets and hand-controllers. To this end, we developed a prototype VR group discussion app equipped with comprehensive sensor data-logging functions and conducted an experiment of VR group discussions (N = 24). We used the quantitative and qualitative experimental data to analyze participants’ experiences of group discussions in relation to the temporal patterns of their different speaking intentions. We then propose a sensor-based mechanism for detecting speaking intentions by employing a sampling strategy that considers the temporal patterns of speaking intentions, and we verify the feasibility of our approach in group discussion settings.
A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks
Authors: Ardabili Sevda Zafarmandi , Bahmani Soufia , Lahijan Lida Zare , Khaleghi Nastaran , Sheykhivand Sobhan , Danishvar Sebelan
Published: January 2024

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.
A Dual-Branch Fusion Network Based on Reconstructed Transformer for Building Extraction in Remote Sensing Imagery
Authors: Wang Yitong , Wang Shumin , Dou Aixia
Published: January 2024

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods.
High-Sensitivity and Wide-Range Flexible Ionic Piezocapacitive Pressure Sensors with Porous Hemisphere Array Electrodes
Authors: Wu Bang , Wu Weiguang , Ma Rui , Chen Haobing , Zhao Yilin , Li Yunfan , Lei Xiao , Liu Feng
Published: January 2024

The development of high-performance flexible pressure sensors with porous hierarchical microstructures is limited by the complex and time-consuming preparation processes of porous hierarchical microstructures. In this study, a simple modified heat curing process was first proposed to achieve one-step preparation of porous hemispherical microstructures on a polydimethylsiloxane (PDMS) substrate. In this process, a laser-prepared template was used to form surface microstructures on PDMS film. Meanwhile, the thermal decomposition of glucose monohydrate additive during heat curing of PDMS led to the formation of porous structures within PDMS film. Further, based on the obtained PDMS/CNTs electrodes with porous hemisphere array and ionic polymer dielectric layers, high-performance ionic piezocapacitive sensors were realized. Under the synergistic effect of the low-stiffness porous hemisphere microstructure and the electric double layer of the ionic polymer film, the sensor based on an ionic polymer film with a 1:0.75 ratio of P(VDF-HFP):[EMIM][TFSI] not only achieves a sensitivity of up to 106.27 kPa−1 below 3 kPa, but also has a wide measurement range of over 400 kPa, which has obvious advantages in existing flexible piezocapacitive sensors. The rapid response time of 110 s and the good stability of 2300 cycles of the sensor further elucidate its practicality. The application of the sensor in pulse monitoring, speech recognition, and detection of multiple dynamic loads verifies its excellent sensing performance. In short, the proposed heat curing process can simultaneously form porous structures and surface microstructures on PDMS films, greatly simplifying the preparation process of porous hierarchical microstructures and providing a simple and feasible way to obtain high-performance flexible pressure sensors.
Smart Shelf System for Customer Behavior Tracking in Supermarkets
Authors: Jose John Anthony C. , Bertumen Christopher John B. , Roque Marianne Therese C. , Umali Allan Emmanuel B. , Villanueva Jillian Clara T. , TanAi Richard Josiah , Sybingco Edwin , San Juan Jayne , Gonzales Erwin Carlo
Published: January 2024

Transactional data from point-of-sales systems may not consider customer behavior before purchasing decisions are finalized. A smart shelf system would be able to provide additional data for retail analytics. In previous works, the conventional approach has involved customers standing directly in front of products on a shelf. Data from instances where customers deviated from this convention, referred to as “cross-location”, were typically omitted. However, recognizing instances of cross-location is crucial when contextualizing multi-person and multi-product tracking for real-world scenarios. The monitoring of product association with customer keypoints through RANSAC modeling and particle filtering (PACK-RMPF) is a system that addresses cross-location, consisting of twelve load cell pairs for product tracking and a single camera for customer tracking. In this study, the time series vision data underwent further processing with R-CNN and StrongSORT. An NTP server enabled the synchronization of timestamps between the weight and vision subsystems. Multiple particle filtering predicted the trajectory of each customer’s centroid and wrist keypoints relative to the location of each product. RANSAC modeling was implemented on the particles to associate a customer with each event. Comparing system-generated customer–product interaction history with the shopping lists given to each participant, the system had a general average recall rate of 76.33% and 79% for cross-location instances over five runs.
Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm
Authors: Xu Maoxuan , Yang Feng , Fang Yuanjin , Li Fanruo , Yan Rui
Published: January 2024

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.
Ground Reaction Forces, Asymmetries and Performance of Change of Direction Tasks in Youth Elite Female Basketball Players
Authors: Arboix-Alió Jordi , Buscà Bernat , Miró Adrià , Bishop Chris , Fort-Vanmeerhaeghe Azahara
Published: January 2024

The magnitude and direction of inter-limb asymmetries in a change of direction (COD) have increased interest in scientific research in recent years. This present study aimed to investigate the magnitude of asymmetries in an elite youth female basketball sample (n = 18, age = 17.79 ± 0.67 y) and determine its directionality using force platform technology. Participants performed 70° and 180° COD tests analyzing the following variables: time, ground contact time (GCT) and ground reaction forces (GRF) along the anterior–posterior, mediolateral, and vertical axes. Inter-limb asymmetries were evident in both COD tests, with substantial differences observed between limbs (p < 0.01). The asymmetry values ranged from 3.02% to 24.31% in COD 180° and from 1.99% to 21.70% in COD 70°, with anterior–posterior GRF consistently exhibiting the highest asymmetry magnitude. Additionally, the directionality exhibited variability between the tests, indicating poor agreement and suggesting the independent directionality of asymmetries across tasks. Moreover, players required more time to complete the COD 180°, the GCT was noticeably longer for the COD 180° than for the COD 70°, and GRF varied across the axis, suggesting that players adapt uniquely to the specific demands of each task. The utilization of force platforms presents a comprehensive approach to assess asymmetries and COD variables performance variables which are “angle-dependent”, which could have important implications for COD screening and effective training interventions.
Overcoming Barriers to Sustainable Supply Chain Management in Small and Medium-Sized Enterprises: A Multi-Criteria Decision-Making Approach
Authors: Gonçalves Hugo , Magalhães Vanessa S. M. , Ferreira Luís M. D. F. , Arantes Amílcar
Published: January 2024

Sustainable supply chain management (SSCM) integrates economic, social, and environmental goals within the supply chain to enhance long-term performance. It assists organizations in monitoring their performance concerning social, environmental, and economic factors to bolster sustainability. Yet, implementing SSCM poses significant challenges for most organizations, particularly small and medium-sized enterprises (SMEs). This study aims to identify key barriers and strategies for overcoming them in SMEs. A thorough literature review revealed 80 barriers across nine categories: technological, economic and financial, supplier, information, market and networking, human resources, social and cultural, regulatory and institutional, and organizational barriers. Eight experts from SMEs in focus group discussions considered 55 relevant barriers and prioritized them using the best–worst method. Then, based on the top 15 barriers they deemed key, they adapted and improved 24 strategies based on the literature that, in their opinion, could support the implementation of and improve the SSCM in SMEs. The findings from this study highlight economic and financial barriers as the foremost challenges to the implementation of SSCM, mainly due to the lack of funding and capital to make changes in activities to include sustainability. The findings are valuable for SMEs seeking to implement SSCM, offering insights into potential barriers and strategies for surmounting them. Moreover, this study provides a structured approach that can be replicated to identify the most pressing barriers to overcoming and improving long-term sustainability.
Energy-Efficient Strategies for Mitigating Airborne Pathogens in Buildings—Building Stage-Based Sustainable Strategies
Authors: Kapoor Nishant Raj , Kumar Aman , Kumar Ashok , Arora Harish Chandra , Kumar Anuj , Gaur Sulakshya
Published: January 2024

The coronavirus disease (COVID-19) pandemic has had widespread global effects. The advent of novel variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, along with the spread of diverse airborne viruses across different geographical locations, has caused reflective apprehension on a global scale. This resurgence emphasises the critical importance of carefully constructed structures installed with efficient ventilation systems, including both natural and mechanical ventilation techniques, as well as mixed-mode ventilation approaches in buildings. Building engineering and architectural designs must go beyond traditional considerations of economics and structural durability in order to protect public health and well-being. To attain a high quality of life, it is necessary to prioritise sustainability, energy efficiency, and the provision of safe, high-quality indoor environments. Empirical scientific investigations underscore the pivotal role played by conducive indoor environments in averting the transmission of viral diseases such as COVID-19 and mitigating challenges associated with sick building syndrome, primarily stemming from suboptimal indoor air quality. This work provides a summary and a SWOT (strength, weakness, opportunities, and threat) analysis of strategies designed for engineers, architects, and other experts in the field to implement. These strategies are intended for integration into new constructions and the retrofitting of extant structures. Their overarching objective is the minimisation of viral transmission within indoor spaces, accomplished in an energy-efficient manner consonant with sustainable development objectives. The significance of these strategies lies in their ability to impact changes to national and international building codes and regulations, strengthening infrastructures against probable airborne viral threats. Encompassing both object-centric and subject-centric approaches, these strategies collectively furnish a holistic framework for mitigating the dissemination of pathogens, exemplified by the SARS-CoV-2 virus and similar airborne viruses, across diverse typologies of buildings.