bluuebunny/crossref_metadata_2025 huggingface dataset:
Surprising Effects of Al2O3 Coating on Tribocatalytic Degradation of Organic Dyes by CdS Nanoparticles
Authors: Ke Senhua , Mao Chenyue , Luo Ruiqing , Zhou Zeren , Hu Yongming , Zhao Wei , Chen Wanping
Published: August 2024

With a band gap of 2.4 eV, CdS has been extensively explored for photocatalytic applications under visible light irradiation. In this study, CdS nanoparticles have been investigated for the tribocatalytic degradation of concentrated Rhodamine B (RhB) and methyl orange (MO) solutions. For CdS nanoparticles in a glass beaker, 78.9% of 50 mg/L RhB and 69.8% of 20 mg/L MO solutions were degraded after 8 h and 24 h of magnetic stirring using Teflon magnetic rotary disks, respectively. While for CdS nanoparticles in a beaker with Al2O3 coated on its bottom, 99.8% of the RhB solution was degraded after 8 h of magnetic stirring and 95.6% of the MO solution was degraded after 12 h of magnetic stirring. Moreover, another contrast was observed between the two beaker bottoms—a new peak at 250 nm in UV–visible absorption spectra was only observed for the MO degradation by CdS in the as-received glass beaker, which indicates that MO molecules were only broken into smaller organic molecules in that case. These findings are meaningful for expanding the catalytic applications of CdS and for achieving a better understanding of tribocatalysis as well.
Facile Preparation of Smart Sponge Based on a Zeolitic Imidazolate Framework for the Efficient Separation of Oily Wastewater
Authors: Zhang Yuping , Chen Xinxin , Yuan Pei , Chen Haie , Li Songwei
Published: August 2024

The fabrication of durable materials with excellent oil-adsorption capacity and separation performance for the treatment of oily wastewater is meaningful based on the special property of smart responsiveness. Herein, a solvent-responsive melamine sponge (MS) was developed via silanization and the in situ growth of a zeolitic imidazolate framework-8 (ZIF-8). Detailed characterization of the resultant composite MS was conducted using scanning electron microscopy (SEM), energy-dispersive spectrometry (EDS), and X-ray diffraction (XRD). The multiscale hierarchical MS substrate exhibited highly hydrophobic properties in the pH range of 1–11, along with a satisfactory adsorption capacity in the range of 65.4–134.2 g/g for different oils. The modified surface transformed from superhydrophobic/superlipophilic to superhydrophilic/underwater superoleophobic upon ethanol wetting, reverting to its original superhydrophobic state upon drying. The separation flux of the MS substrate was above 1.5 × 104 L/m2h for both oil and water removal, and the separation efficiency was greater than 98.7%. The absence of obvious changes in separation performance after 50 successive immiscible oil−water separations indicated the excellent durability and robustness of the anchored ZIF-8 nanoparticles on the surface of the modified MS substrate. More importantly, oil-in-water emulsion separation was successfully carried out via the ZIF-8 MS composite, showing high separation efficiency (over 99.1%). The developed smart sponge, which had high oil-adsorption capacity, excellent chemical stability, and fire resistance, has a wide range of potential practical applications in the convenient treatment of oily wastewater.
Impact of Carbon Fiber Content and Length on the Rheological Properties and Coating Performance for Lost Foam Casting
Authors: Sun Guojin , Xu Linqian , Wang Qi
Published: August 2024

This study explores the impact of carbon fiber length and content on the rheological properties and performance of coatings used in lost foam casting. The investigation encompassed fiber lengths of 1 mm, 3 mm, and 6 mm and fiber contents of 0.2%, 0.5%, and 0.8%. The effects on coating viscosity, shear stress, coating weight, and surface morphology were meticulously evaluated. The results demonstrate that incorporating carbon fibers significantly enhances coating viscosity and shear stress compared to fiber-free coatings, with more pronounced effects observed at higher fiber contents and longer fiber lengths. Nevertheless, excessive fiber contents and lengths can lead to agglomeration, negatively impacting coating uniformity. The optimal fiber length and content were identified, striking a balance between improved rheological properties and coating performance. These findings provide critical insights for the development and industrial application of high-performance coatings in lost foam casting.
Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway
Authors: Wang Zihan , Chong Xiaolei , Wang Guanhu , Liu Chaojia , Zhang Jichao
Published: August 2024

In view of the fact that there is no suitable measurement method for tire driving resistance during the operation of a transporter on soil pavement, this paper proposes a simple measurement and estimation method for driving resistance on a soil runway based on the Bekker settlement model. In this paper, using the driving resistance equation based on the Bekker model, a simplified equation of driving resistance related to mass is proposed. Using the principle of kinematics, a simple test device for driving resistance was developed, and the test data of the test device were used to determine the parameters of the driving resistance estimation equation of the road surface. The driving resistance of the wheel running state was tested by the simulated aircraft load loading vehicle through the mechanical sensor, and the consistency between the theoretical value and the actual value of the driving resistance value under 8T, 10T, and 12T loads was verified. This research shows that the test method and estimation method have low cost and good accuracy and are suitable for wide promotion. The results of this paper provide a new idea and method for estimating the driving resistance of a wheel when a transporter runs on a soil road and also provide a reference for designing the length of a soil runway.
Impact of Video Motion Content on HEVC Coding Efficiency
Authors: Salih Khalid A. M. , Ali Ismail Amin , Mstafa Ramadhan J.
Published: August 2024

Digital video coding aims to reduce the bitrate and keep the integrity of visual presentation. High-Efficiency Video Coding (HEVC) can effectively compress video content to be suitable for delivery over various networks and platforms. Finding the optimal coding configuration is challenging as the compression performance highly depends on the complexity of the encoded video sequence. This paper evaluates the effects of motion content on coding performance and suggests an adaptive encoding scheme based on the motion content of encoded video. To evaluate the effects of motion content on the compression performance of HEVC, we tested three coding configurations with different Group of Pictures (GOP) structures and intra refresh mechanisms. Namely, open GOP IPPP, open GOP Periodic-I, and closed GOP periodic-IDR coding structures were tested using several test sequences with a range of resolutions and motion activity. All sequences were first tested to check their motion activity. The rate–distortion curves were produced for all the test sequences and coding configurations. Our results show that the performance of IPPP coding configuration is significantly better (up to 4 dB) than periodic-I and periodic-IDR configurations for sequences with low motion activity. For test sequences with intermediate motion activity, IPPP configuration can still achieve a reasonable quality improvement over periodic-I and periodic-IDR configurations. However, for test sequences with high motion activity, IPPP configuration has a very small performance advantage over periodic-I and periodic-IDR configurations. Our results indicate the importance of selecting the appropriate coding structure according to the motion activity of the video being encoded.
An Educational Escape Room Game to Develop Cybersecurity Skills
Authors: Spatafora Alessia , Wagemann Markus , Sandoval Charlotte , Leisenberg Manfred , Vaz de Carvalho Carlos
Published: August 2024

The global rise in cybercrime is fueled by the pervasive digitization of work and personal life, compounded by the shift to online formats during the COVID-19 pandemic. As digital channels flourish, so too do the opportunities for cyberattacks, particularly those exposing small and medium-sized enterprises (SMEs) to potential economic devastation. These businesses often lack comprehensive defense strategies and/or the necessary resources to implement effective cybersecurity measures. The authors have addressed this issue by developing an Educational Escape Room (EER) that supports scenario-based learning to enhance cybersecurity awareness among SME employees, enabling them to handle cyber threats more effectively. By integrating hands-on scenarios based on real-life examples, the authors aimed to improve the knowledge retention and the operational performance of SME staff in terms of cybersafe practices. The results achieved during pilot testing with more than 200 participants suggest that the EER approach engaged the trainees and boosted their cybersecurity awareness, marking a step forward in cybersecurity education.
Caputo Fabrizio Bézier Curve with Fractional and Shape Parameters
Authors: Awais Muhammad , Kirmani Syed Khawar Nadeem , Rana Maheen , Ahmad Raheel
Published: August 2024

In recent research in computer-aided geometric design (CAGD), one of the most popular concerns has been the creation of new basis functions for the Bézier curve. Bézier curves with high degrees often overshoot, which makes it challenging to maintain control over the ideal length of the curved trajectory. To get around this restriction, free-form surfaces and curves can be created using the Caputo Fabrizio basis function. In this study, the Caputo Fabrizio fractional order derivative is used to construct the Caputo Fabrizio basis function, which contains fractional parameter and shape parameters. The Caputo Fabrizio Bézier curve and surface are defined using the Caputo Fabrizio basis function, and their geometric properties are examined. Using fractional and shape parameters in the implementation of the Caputo Fabrizio basis function offers an alternative perspective on how the Caputo Fabrizio basis function can construct complicated curves and surfaces beyond a limited formulation. Curves and surfaces can have additional shape and length control by adjusting a number of fractional and shape parameters without affecting their control points. The Caputo Fabrizio Bézier curve’s flexibility and versatility make it more effective in creating complex engineering curves and surfaces.
Exploring the Premelting Transition through Molecular Simulations Powered by Neural Network Potentials
Authors: Zeng Limin , Gao Ang
Published: August 2024

The premelting layer on crystal surfaces significantly affects the stability, surface reactivity, and phase transition behaviors of crystals. Traditional methods for studying this layer—experimental techniques, classical simulations, and even first-principle simulations—have significant limitations in accuracy and scalability. To overcome these challenges, we employ molecular dynamic simulations based on neural network potentials to investigate the structural and dynamic behavior of the premelting layer on ice. This approach matches the accuracy of first-principle calculations while greatly improving computational efficiency, allowing us to simulate the ice–vapor interface on a much larger scale. In this study, we conducted a one-nanosecond simulation of the ice–vapor interface involving 1024 water molecules. This significantly exceeds the time and size scales of previous first-principle studies. Our simulation results indicate complete surface melting. Furthermore, our simulation results reveal dynamic heterogeneity within the premelting layer, with molecules segregated into clusters of low and high mobility.
Average Nucleotide Identity and Digital DNA-DNA Hybridization Analysis Following PromethION Nanopore-Based Whole Genome Sequencing Allows for Accurate Prokaryotic Typing
Authors: Versmessen Nick , Mispelaere Marieke , Vandekerckhove Marjolein , Hermans Cedric , Boelens Jerina , Vranckx Katleen , Van Nieuwerburgh Filip , Vaneechoutte Mario , Hulpiau Paco , Cools Piet
Published: August 2024

Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8–81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.
Piezoelectric Surgery, Er:YAG Laser Surgery and Nd:YAG Laser Photobiomodulation: A Combined Approach to Treat Medication-Related Osteonecrosis of the Jaws (MRONJ)
Authors: Vescovi Paolo , De Francesco Pierpaolo , Giovannacci Ilaria , Leão Jair Carneiro , Barone Antonio
Published: August 2024

Medication-related osteonecrosis of the jaw (MRONJ) is a drug complication that can occur in patients taking antiresorptive or antiangiogenic drugs. Although it is a well-documented disease, there is no widely accepted treatment. However, several therapeutic approaches have been proposed. The surgical approach in many advanced cases appears inevitable; however, the results are not yet defined and predictable. This study aimed to propose a combined surgical approach with a piezoelectric device and laser (Er:YAG for bone ablation and Nd:YAG laser for photobiomodulation) in a young patient with breast cancer and bone metastasis under denosumab treatment, affected by spontaneous stage 3 MRONJ with maxillary sinus involvement. The patient under study reported no post-operative discomfort, with painkiller intake limited to the day after surgery. Total mucosal healing was observed without recurrences for more than 4 years after surgery. According to the results of our preliminary study, a combined surgical approach using a piezoelectric device and laser therapy is effective in managing patients affected by MRONJ, leveraging the clinical and biological advantages of these different techniques.
Hyaluronic Acid in Bone Regeneration: Systematic Review and Meta-Analysis
Authors: Lorenzi Claudia , Leggeri Andrea , Cammarota Ilaria , Carosi Paolo , Mazzetti Vincenzo , Arcuri Claudio
Published: August 2024

Aim: The aim of this systematic review and meta-analysis was to assess possible histomorphometric differences in new bone formation and in remaining graft particles when hyaluronic acid (HA) was added and mixed with graft materials in bone regeneration. Materials and methods: This review was registered at the International Prospective Register of Systematic Reviews (PROSPERO) of the National Institute of Health Research (registration number CRD42024530030). Electronic research was performed, and involved studies published up to 29 February 2024 using a specific word combination. The primary outcome was to assess possible histomorphometric differences in new bone formation and in remaining graft particles when HA was added and mixed with graft materials in bone regeneration. The search resulted in 138 potential studies. Meta-analyses were performed using the fixed and random effects model to identify significant changes in new bone formation and in the remaining graft particles. Results: After screening procedures, only three randomized controlled trials fulfilled the inclusion criteria and were selected for qualitative and quantitative analysis. The effect size of HA in the new bone formation was not statistically significant at 95% CI (Z = 1.734, p-value = 0.083, 95 % CI -,399; 6516). The effect size of HA in the remaining graft particles was not statistically significant at 95% CI (Z = −1.042, p-value = 0.297, CI -,835; 255). Conclusions: Within the limitations of the present systematic review and meta-analysis, the addition of HA to bone graft did not result in significant changes in bone regeneration procedures in terms of new bone formation and residues, even if the included studies showed encouraging and promising results.
Optimization and Tradeoff Analysis for Multiple Configurations of Bio-Energy with Carbon Capture and Storage Systems in Brazilian Sugarcane Ethanol Sector
Authors: Bunya Bruno , Sotomonte César A. R. , Vitoriano Julio Alisson Aparecido , Pereira João Luiz Junho , de Souza Túlio Augusto Zucareli , Francisco Matheus Brendon , Coronado Christian J. R.
Published: August 2024

Bio-energy systems with carbon capture and storage (BECCS) will be essential if countries are to meet the gas emission reduction targets established in the 2015 Paris Agreement. This study seeks to carry out a thermodynamic optimization and analysis of a BECCS technology for a typical Brazilian cogeneration plant. To maximize generated net electrical energy (MWe) and carbon dioxide CO2 capture (Mt/year), this study evaluated six cogeneration systems integrated with a chemical absorption process using MEA. A key performance indicator (gCO2/kWh) was also evaluated. The set of optimal solutions shows that the single regenerator configuration (REG1) resulted in more CO2 capture (51.9% of all CO2 emissions generated by the plant), penalized by 14.9% in the electrical plant’s efficiency. On the other hand, the reheated configuration with three regenerators (Reheat3) was less power-penalized (7.41%) but had a lower CO2 capture rate (36.3%). Results showed that if the CO2 capture rates would be higher than 51.9%, the cogeneration system would reach a higher specific emission (gCO2/kWh) than the cogeneration base plant without a carbon capture system, which implies that low capture rates (<51%) in the CCS system guarantee an overall net reduction in greenhouse gas emissions in sugarcane plants for power and ethanol production.
Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine
Authors: Shen Yang , Li Deyi , Wang Wenbo
Published: August 2024

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.
Analysis of Entropy Generation via Non-Similar Numerical Approach for Magnetohydrodynamics Casson Fluid Flow with Joule Heating
Authors: Louati Hanen , Khan Sajid , Mansoor Muavia , Hilali Shreefa O. , Gargouri Ameni
Published: August 2024

This analysis emphasizes the significance of radiation and chemical reaction effects on the boundary layer flow (BLF) of Casson liquid over a linearly elongating surface, as well as the properties of momentum, entropy production, species, and thermal dispersion. The mass diffusion coefficient and temperature-dependent models of thermal conductivity and species are used to provide thermal transportation. Nonlinear partial differential equations (NPDEs) that go against the conservation laws of mass, momentum, heat, and species transportation are the form arising problems take on. A set of coupled dimensionless partial differential equations (PDEs) are obtained from a set of convective differential equations by applying the proper non-similar transformations. Local non-similarity approaches provide an analytical approximation of the dimensionless non-similar system up to two degrees of truncations. The built-in Matlab (Version: 7.10.0.499 (R2010a)) solver bvp4c is used to perform numerical simulations of the local non-similar (LNS) truncations.
The Signaling Paradox: Revisiting the Impacts of Overeducation in the Chinese Labor Market
Authors: Zhang Bingbing , Zhang Qinggen , Yao Congli , Liu Zhiyuan
Published: August 2024

With the widespread popularization of higher education, the increased supply of knowledge laborers has heightened the risk of overeducation, posing a significant threat to employment quality. Utilizing data from the CGSS2015 survey, this study controls for selection bias by matching employed and self-employed individuals using Propensity Score Matching (PSM) to distinguish and estimate the signaling and productivity effects of overeducation in China. The findings indicate that the signaling effects of overeducation are not significant, challenging the Weak Screening Hypothesis (WSH) and suggesting that overeducation in China is not genuine overeducation. It is also acknowledged that overeducation has dual productivity effects, including the loss of personal productivity and the promotion of job productivity. This implies that overeducation has contributed to the increase of social productivity at the cost of personal sacrifice.
Braiding the Ropes: Adding Second or Additional Language Acquisition to Reading and Writing Metaphors
Authors: Lane Jennifer M. , Kennedy Teresa J.
Published: August 2024

Drawing on second language acquisition theories and existing visuals, this article proposes a new educational metaphor, Lane’s L2+ Acquisition Rope, to encompass the complexities of second or additional language learning. This metaphor integrates five major strands—external factors, social factors, affective factors, innate factors, and internal factors—to provide a comprehensive framework for understanding proficient second language acquisition. Implications of this visual highlight the importance of incorporating socio-cultural, neurological, and affective dimensions into educational metaphors to support diverse learners effectively. Lane’s L2+ Acquisition Rope offers a promising avenue for educators to enhance instructional practices and promote equitable learning outcomes for English Language Learners and other student subgroups.
Knowledge Retention of Undergraduate Medical Students in Regional Anatomy Following a One-Month Gross Anatomy Course Setting
Authors: Antipova Veronica , Siwetz Martin , Engelhardt Maren , Fellner Franz A. , Manhal Simone , Niedermair Julian F. , Ondruschka Benjamin , Poilliot Amélie J. , Wree Andreas , Hammer Niels
Published: August 2024

Background: In the achievement of optimal learning outcomes, knowledge retention presents a major concern for medical students and educators. Practical dissection courses facilitate the consolidation of knowledge of anatomy. Previously, it was shown that a regional anatomy dissection course is more beneficial over a 3-month than a 1-month duration for gathering pre examination knowledge. This study aimed to assess if follow-up anatomy interventions help consolidate regional anatomy knowledge and facilitate knowledge retention of undergraduate medical students. It was hypothesized that knowledge retention could be enhanced using post-dissection teaching interventions. Methods: Upon completion of the dissection course, Objective Structured Practical Examinations (OSPEs) were performed for the neck, thorax, and abdomen immediately before the start of the oral examinations, with follow-ups at 6 and 12 months. Between each of the examinations, virtual and in-person lectures and seminars on (radiologic) anatomy and pathology were held, including Cinematic Rendering, but without additional teaching on human tissues. Results: Significant improvements were observed for knowledge of the neck and abdomen regions in the 6- and 12-month follow-up OSPEs. The effects of knowledge gain were less marked in ethanol-glycerin- than in Thiel-embalmed tissues. Student perceptions regarding tissue quality correlated positively with their assessment of tissue suitability for examination preparation. In conclusion, even anatomy teaching interventions not utilizing human tissues may help consolidate and improve regional anatomy knowledge over a one-year term. Conclusion: Knowledge retention can be enhanced by accompanying virtual with physical teaching interventions.
Optimized Preventive Diagnostic Algorithm for Assessing Aluminum Electrolytic Capacitor Condition Using Discrete Wavelet Transform and Kalman Filter
Authors: Amaral Acácio M. R. , Laadjal Khaled , Marques Cardoso Antonio J.
Published: August 2024

Power converters (PCs) are vital elements of critical applications, making their reliable operation crucial. Enhancing PCs’ reliability can be achieved by adding intelligence to the system, enabling it to predict failures and generate early warnings before a failure occurs. In this context, intelligence is integrated into the system through preventive diagnostic algorithms (PDAs) that assess the converter condition. This article introduces a PDA designed to determine the optimal replacement timing for aluminum electrolytic capacitors (AECs) within power converters. AECs, in addition to being a fundamental component of PCs, also represent the most vulnerable element of the PCs’ power section. The aging of AECs is characterized by a decrease in capacitance (C) and an increase in the equivalent series resistance (ESR). Therefore, ESR and C serve as key indicators for assessing the AECs’ health status. One of the most critical functional requirements for designing a PDA is its accuracy, which can be significantly affected by transients. The solution proposed in this paper is resilient to transients, overcoming a common problem in implementing AECs’ PDAs. The proposed algorithm employs discrete wavelet transform (DWT) to extract the converter signal modes. Subsequently, key characteristics of these modes are extracted, enabling the calculation of both ESR and C. Finally, by using the estimated ESR and C values, two fault indicators can be obtained that are resilient to transients. Employing a Kalman filter reduces noise and ensures the indicators’ resilience to transients, making them highly effective for evaluating the AECs’ health status. The proposed PDA was validated through multiple computer simulations conducted in MATLAB/Simulink for a three-phase interleaved boost converter (3ϕIBC), which includes a proportional-integral (PI) controller with anti-windup capability.
Adaptive Transmission Strategy for Non-Uniform Coding of 360∘ Videos
Authors: Guo Jia , Li Shiqiang , Zhu Jinqi , Li Xiang , Sun Bowen , Feng Weijia
Published: August 2024

A 360° video offers a more immersive experience, gaining increasing popularity among users. However, enhancing the transmission efficiency of 360° videos under limited bandwidth conditions remains a significant challenge. This paper segments the video into three areas: the attention area, the edge area, and the viewpoint-switching transition area. Based on the segmentation of these three distinct video areas, a novel non-uniform coding transmission method for 360° videos is presented, along with mathematical modeling to define the optimization problem. A heuristic algorithm is subsequently introduced, which adaptively determines the optimal number of tiles and allocates the bitrate for each tile in real time to enhance the user’s quality of experience (QoE). Finally, a simulation platform has been developed to validate the efficacy of the proposed algorithm by conducting comparative analyses with existing algorithms.
WMLinks: Wearable Smart Devices and Mobile Phones Linking through Bluetooth Low Energy (BLE) and WiFi Signals
Authors: Guo Naixuan , Chen Zhaofeng , Xu Heyang , Liu Yu , Zhao Zhechun , Xu Sen
Published: August 2024

Wearable smart devices have gradually become indispensable devices in people’s lives. Their security and privacy have gained increasing popularity among the public due to their ability to monitor and record various aspects of users’ daily activities and health data. These devices maintain a wireless connection with mobile phones through periodic signal transmissions, which can be intercepted and analyzed by external observers. While these signal packets contain valuable information about the device owner, the identity of the actual user remains unknown. In this study, we propose two approaches to link wearable smart devices with users’ mobile phones, which serve as electronic identities, to enable novel applications such as multi-device authentication and user-device graph construction for targeted advertising. To establish this linkage, we propose two approaches: a passive-sniffing-based linking approach and an active-interference-based linking approach, which solve the problem of sniffing Bluetooth Low Energy broadcast packets in two stages of Bluetooth Low Energy communication. Through experiments conducted across three scenarios, we demonstrate that seven wearable devices can be successfully linked with an accuracy rate exceeding 80%, with accuracy rates approaching 100% when a device is recorded more than 11 times. Additionally, we find that four wearable devices can be linked via an active-interference-based linking approach with an accuracy rate exceeding 70%. Our results highlight the potential of wearable devices and mobile phones as a means of establishing user identities and enabling the development of more sophisticated applications in the field of wearable technology.
LD-YOLOv10: A Lightweight Target Detection Algorithm for Drone Scenarios Based on YOLOv10
Authors: Qiu Xiaoyang , Chen Yajun , Cai Wenhao , Niu Meiqi , Li Jianying
Published: August 2024

Due to the limited computing resources and storage capacity of edge detection devices, efficient detection algorithms are typically required to meet real-time and accuracy requirements. Existing detectors often require a large number of parameters and high computational power to improve accuracy, which reduces detection speed and performance on low-power devices. To reduce computational load and enhance detection performance on edge devices, we propose a lightweight drone target detection algorithm, LD-YOLOv10. Firstly, we design a novel lightweight feature extraction structure called RGELAN, which utilizes re-parameterized convolutions and the newly designed Conv-Tiny as the computational structure to reduce the computational burden of feature extraction. The AIFI module was introduced, utilizing its multi-head attention mechanism to enhance the expression of semantic information. We construct the DR-PAN Neck structure, which obtains weak features of small targets with minimal computational load. Wise-IoU and EIoU are combined as new bounding box regression loss functions to adjust the competition between anchor boxes of different quality and the sensitivity of anchor box aspect ratios, providing a more intelligent gradient allocation strategy. Extensive experiments on the VisdroneDET-2021 and UAVDT datasets show that LD-YOLOv10 reduces the number of parameters by 62.4% while achieving a slight increase in accuracy and has a faster detection speed compared to other lightweight algorithms. When deployed on the low-power NVIDIA Jetson Orin Nano device, LD-YOLOv10 achieves a detection speed of 25 FPS.
Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
Authors: Shen Haoran , Li Jian , Wang Ziwei
Published: August 2024

As an important space-based intelligence acquisition and combat command receiving component, the low-orbit satellite broadcast signal receiving terminal is an important guarantee for realizing the full-dimensional joint operations of our military’s multi-services. This design is based on the SoC platform. Compared to the traditional low-orbit reconnaissance satellite reception and processing terminal business process, a narrowband anti-interference module is added and the interference-to-signal ratio reaches 57 dB and 47 dB when resisting interference from a single frequency band and three frequency bands, respectively. Digital beamforming is used for signal processing and beamforming gain of the whole machine reaches 14–15 dBic; compared to traditional capture and tracking modules, this terminal uses a time domain parallel frequency domain FFT fast acquisition method and a bit synchronization loop is added to the tracking loop to aiming at the problem that the frequency signal acquisition speed is not fast enough and addressing the issue that information symbol rate is not an integer multiple of the pseudo-code, thus complete bit synchronization within 60 ms, with a bit error rate of 0; the coding gain can reach 7 dB. Combined with the solution of decoding and positioning solution algorithms, this scheme used the JFM7K325T chip to complete the design simulation of the complete receiving and processing terminal.
DCG-Net: Enhanced Hyperspectral Image Classification with Dual-Branch Convolutional Neural Network and Graph Convolutional Neural Network Integration
Authors: Zhu Wenkai , Sun Xueying , Zhang Qiang
Published: August 2024

In recent years, graph convolutional neural networks (GCNs) and convolutional neural networks (CNNs) have made significant strides in hyperspectral image (HSI) classification. However, existing models often encounter information redundancy and feature mismatch during feature fusion, and they struggle with small-scale refined features. To address these issues, we propose DCG-Net, an innovative classification network integrating CNN and GCN architectures. Our approach includes the development of a double-branch expanding network (E-Net) to enhance spectral features and efficiently extract high-level features. Additionally, we incorporate a GCN with an attention mechanism to facilitate the integration of multi-space scale superpixel-level and pixel-level features. To further improve feature fusion, we introduce a feature aggregation module (FAM) that adaptively learns channel features, enhancing classification robustness and accuracy. Comprehensive experiments on three widely used datasets show that DCG-Net achieves superior classification results compared to other state-of-the-art methods.
An Improved Co-Resident Attack Defense Strategy Based on Multi-Level Tenant Classification in Public Cloud Platforms
Authors: Peng Yuxi , Jiang Xinchen , Wang Shaoming , Xiang Yanping , Xing Liudong
Published: August 2024

Co-resident attacks are serious security threats in multi-tenant public cloud platforms. They are often implemented by building side channels between virtual machines (VMs) hosted on the same cloud server. Traditional defense methods are troubled by the deployment cost. The existing tenant classification methods can hardly cope with the real dataset that is quite large and extremely unevenly distributed, and may have problems in the processing speed considering the computation complexity of the DBSCAN algorithm. In this paper, we propose a novel co-resident attack defense strategy which solve these problems through an improved and efficient multi-level clustering algorithm and semi-supervised classification method. We propose a novel multi-level clustering algorithm which can efficiently reduce the complexity, since only a few parameter adjustments are required. Built on the proposed clustering algorithm, a semi-supervised classification model is designed. The experimental results of the classification effect and training speed show that our model achieves F-scores of over 85% and is significantly faster than traditional SVM classification methods. Based on the classification of unlabeled tenants into different security groups, the cloud service provider may modify the VM placement policy to achieve physical isolation among different groups, reducing the co-residency probability between attackers and target tenants. Experiments are conducted on a large-scale dataset collected from Azure Cloud Platform. The results show that the proposed model achieves 97.86% accuracy and an average 96.06% F-score, proving the effectiveness and feasibility of the proposed defense strategy.
A Multi-Hop Reasoning Knowledge Selection Module for Dialogue Generation
Authors: Ma Zhiqiang , Liu Jia , Xu Biqi , Lv Kai , Guo Siyuan
Published: August 2024

Knowledge selection plays a crucial role in knowledge-driven dialogue generation methods, directly influencing the accuracy, relevance, and coherence of generated responses. Existing research often overlooks the handling of disparities between dialogue statements and external knowledge, leading to inappropriate knowledge representation in dialogue generation. To overcome this limitation, this paper proposes an innovative Multi-hop Reasoning Knowledge Selection Module (KMRKSM). Initially, multi-relational graphs containing rich composite operations are encoded to capture graph-aware representations of concepts and relationships. Subsequently, the multi-hop reasoning module dynamically infers along multiple relational paths, aggregating triple evidence to generate knowledge subgraphs closely related to dialogue history. Finally, these generated knowledge subgraphs are combined with dialogue history features and synthesized into comprehensive knowledge features by a decoder. Through automated and manual evaluations, the exceptional performance of KMRKSM in selecting appropriate knowledge is validated. This module efficiently selects knowledge matching the dialogue context through multi-hop reasoning, significantly enhancing the appropriateness of knowledge representation and providing technical support for achieving more natural and human-like dialogue systems.
Implementation of an FPGA-Based 3D Shape Measurement System Using High-Level Synthesis
Authors: Kim Tae-Hyeon , Lee Hyunki , Ok Seung-Ho
Published: August 2024

Three-dimensional(3D) shape measurement using point clouds has recently gained significant attention. Phase measuring profilometry (PMP) is widely preferred for its robustness against external lighting changes and high-precision results. However, PMP suffers from long computation times due to complex calculations and its high memory usage. It also faces a 2π ambiguity issue, as the measured phase is limited to the 2π range. This is typically resolved using dual-wavelength methods. However, these methods require separate measurements of phase changes at two wavelengths, increasing the data processing volume and computation times. Our study addresses these challenges by implementing a 3D shape measurement system on a System-on-Chip (SoC)-type Field-Programmable Gate Array (FPGA). We developed a PMP algorithm with dual-wavelength methods, accelerating it through high-level synthesis (HLS) on the FPGA. This hardware implementation significantly reduces computation time while maintaining measurement accuracy. The experimental results demonstrate that our system operates correctly on the SoC-type FPGA, achieving computation speeds approximately 11.55 times higher than those of conventional software implementations. Our approach offers a practical solution for real-time 3D shape measurement, potentially benefiting applications in fields such as quality control, robotics, and computer vision.
Multimodal Recipe Recommendation with Heterogeneous Graph Neural Networks
Authors: Ouyang Ruiqi , Huang Haodong , Ou Weihua , Liu Qilong
Published: August 2024

Recipe recommendation is the process of recommending suitable recipes to users based on factors such as user preferences and dietary needs. Recipes typically involve multiple modalities, with text and images being common, while most typical recipe recommendation methods recommend recipes to users based on text. Obviously, the expressiveness of a single modal is often not enough, and the semantic information of images is more abundant. Moreover, it is difficult to grasp the feature fusion granularity of different kinds of modal information and the relationship between recipes and users. To solve the above problem, this paper proposes a Multimodal Heterogeneous Graph Neural Network Recipe Recommendation (MHGRR) architecture, which aims to fully fuse the various kinds of modal information of recipes and handle the relationship between users and recipes. We use embedding and shallow Convolutional Neural Networks (CNNs) to extract original text and image information for unifying feature fusion granularity, and use Heterogeneous Graph Neural Networks based on GraphSAGE to capture the complex relationship between users and recipes. To verify the effectiveness of our proposed model, we perform some comparative experiments on a real dataset; the experiments show that our method outperforms most popular recipe recommendation methods. Through an ablation experiment, we found that adding image information to recipe recommendation is more effective, and we additionally found that as the output dimensions of GraphSAGE increased, the performance of the model varied little.
Study of Human–Robot Interactions for Assistive Robots Using Machine Learning and Sensor Fusion Technologies
Authors: Raj Ravi , Kos Andrzej
Published: August 2024

In recent decades, the potential of robots’ understanding, perception, learning, and action has been widely expanded due to the integration of artificial intelligence (AI) into almost every system. Cooperation between AI and human beings will be responsible for the bright future of AI technology. Moreover, for a perfect manually or automatically controlled machine or device, the device must perform together with a human through multiple levels of automation and assistance. Humans and robots cooperate or interact in various ways. With the enhancement of robot efficiencies, they can perform more work through an automatic method; therefore, we need to think about cooperation between humans and robots, the required software architectures, and information about the designs of user interfaces. This paper describes the most important strategies of human–robot interactions and the relationships between several control techniques and cooperation techniques using sensor fusion and machine learning (ML). Based on the behavior and thinking of humans, a human–robot interaction (HRI) framework is studied and explored in this article to make attractive, safe, and efficient systems. Additionally, research on intention recognition, compliance control, and perception of the environment by elderly assistive robots for the optimization of HRI is investigated in this paper. Furthermore, we describe the theory of HRI and explain the different kinds of interactions and required details for both humans and robots to perform different kinds of interactions, including the circumstances-based evaluation technique, which is the most important criterion for assistive robots.
Enhancing Fault Diagnosis in IoT Sensor Data through Advanced Preprocessing Techniques
Authors: Sung Sang-Ha , Hong Soongoo , Choi Hyung-Rim , Park Do-Myung , Kim Sangjin
Published: August 2024

Through innovation in the data collection environment, data-driven fault diagnosis has become increasingly important. This study aims to develop an algorithm to improve the accuracy of fault diagnosis based on Internet of Things (IoT) sensor data. In this research, current data collected through IoT sensors is utilized, focusing on diagnosing four states: bearing defects, shaft misalignment, rotor imbalance, and belt looseness. Additionally, to enhance the efficiency of the fault diagnosis algorithm, we introduce a preprocessing technique that utilizes descriptive statistics to reduce the data dimensionality. The experiments are conducted based on current data and vibration data, ensuring reliability from both types of data. The experimental results indicate a significant improvement in the accuracy and computational time of the fault diagnosis algorithm. After experimenting with various candidate algorithms, XGBoost version 1.7.6 exhibited the highest performance of classification. This research contributes to enhancing safety and reliability based on IoT sensors and suggests potential applications in the field of fault diagnosis.
A Compact V-Band Transit Time Oscillator with Reflective Modulation Cavity
Authors: Chen Zulong , Wang Lei , Ling Junpu , Song Lili , He Juntao , Yao Jinmei , Xu Weili
Published: August 2024

Improving compactness is essential for high-power microwave (HPM) sources. In this paper, a novel reflective modulation cavity is proposed and investigated in a V-band relativistic coaxial transit-time oscillator (RCTTO). The cold cavity analyses and particle-in-cell simulations show that the reflective modulation cavity has larger reflection coefficients of TEM mode and stronger electron beam modulation capability when compared with a uniform modulation cavity. When the input diode voltage is 391 kV, the beam current is 4.91 kA, and when the guiding magnetic field is 0.6 T, the compact V-band RCTTO produces an output microwave power of 518 MW (conversion efficiency of 27.0%). Compared with the original RCTTO, the compact V-band RCTTO featuring a reflective modulation cavity exhibits a 24.8% increase in output power and a 5.4% improvement in efficiency, and the axial length of the magnetic field uniform region is reduced by 24.2%. The compact V-band RCTTO also demonstrates a broad operation voltage range, indicating potential for stable operation with voltage fluctuations in experiments. Furthermore, the reflective modulation cavity can be integrated into other high-frequency O-type HPM devices to enhance compactness, thereby diminishing the demands on the magnetic field region, which is advantageous for the future permanent packaging of HPM sources.