bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Polylactide-Based Active Films Containing Berberine and Quercetin as Systems for Maintaining the Quality and Safety of Blueberries
Authors: Olewnik-Kruszkowska Ewa , Ferri Martina , Cardeira Mariana C. , Gierszewska Magdalena , Rudawska Anna
Published: June 2024

Polymeric thin films based on polylactide with an addition of poly(ethylene glycol) as a plasticizer and flavonoids in the form of quercetin and berberine were subjected to tests that were particularly relevant from the point of view of contact with food. A comparative analysis of the effect of individual flavonoids on the antioxidative properties of tested films and blueberry storage was carried out. The influence of active compounds on the water vapor permeability, as well as UV protection, of the obtained materials was investigated. Also, the specific migration of individual flavonoids from obtained materials to food simulants in the form of acetic acid and ethyl alcohol was determined. The crucial point of this study is the storage of blueberries. The obtained results indicate that the selection of packaging, containing individual active compounds, depends on the purpose and requirements that the packaging must meet for particular types of food.
Investigation of Multidimensional Fractionation in Microchannels Combining a Numerical DEM-LBM Approach with Optical Measurements
Authors: Reinecke Simon Raoul , Zhang Zihao , Blahout Sebastian , Radecki-Mundinger Edgar , Hussong Jeanette , Kruggel-Emden Harald
Published: June 2024

The fractionation in microchannels is a promising approach for the delivery of microparticles in narrow property distributions. The underlying mechanisms of the channels are however often not completely understood and are therefore subject to current research. These investigations are done using different numerical and experimental methods. In this work, we present and evaluate our method of combining a numerical Discrete Element Method (DEM)-Lattice Boltzmann Method (LBM) approach with experimental long-exposure fluorescence microscopy, micro-Particle Image Velocimetry (µPIV) and Astigmatism Particle Tracking Velocimetry (APTV) measurements. The suitability of the single approaches and their synergies are evaluated using the exemplary investigation of multidimensional fractionation in different channel geometries. It shows that both, numerical and experimental method are well suited to evaluate particle dynamics in microchannels. As they furthermore show strengths canceling out weaknesses of the respective other method, the combined method is very well suited for the comprehensive analysis of particle dynamics in microchannels.
Comparison of the Limit of Detection of Paracetamol, Propyphenazone, and Caffeine Analyzed Using Thin-Layer Chromatography and High-Performance Thin-Layer Chromatography
Authors: Bober-Majnusz Katarzyna , Pyka-Pająk Alina
Published: June 2024

TLC (thin-layer chromatography) and HPTLC (high-performance thin-layer chromatography) in normal (NP) and reversed (RP) phase systems were combined with densitometry to analyze caffeine, propyphenazone, and paracetamol. This work aims to check whether comparable limit of detection (LOD) values can be obtained on TLC and HPTLC plates. Analyses were performed on five (NP) or four (RP) different stationary phases (chromatographic plates), testing, in both cases, three mobile phases. It is shown that by using both TLC and HPTLC plates, it is possible to develop chromatographic conditions that enable the detection of compounds analyzed in amounts ranging from a dozen to several dozen µg/spot. In the RP system, lower LOD values for all tested compounds were obtained using TLC than HPTLC. However, performing analyses in the NP, similar (of the same order) LOD values were obtained for caffeine, propyphenazone, and paracetamol when using both TLC and HPTLC plates. For example, during the NP-HPTLC analysis using silica gel 60F254 plates (#1.05548) and mobile phase B (n-hexane—acetone—ammonia, 25:25:0.5, v/v/v), LOD values for caffeine, propyphenazone, and paracetamol were 0.010, 0.046, and 0.030 μg/spot, respectively. During NP-TLC analysis using silica gel 60F254 (#1.05554 plates) and the mobile phase C (chloroform—toluene—ethyl acetate—methanol—80% acetic acid, 18:18:7.5:6:0.3, v/v), the values of LOD were 0.054, 0.029, and 0.016 μg/spot, respectively. During RP-TLC analysis using TLC RP-18F254 plates (#1.05559) and mobile phase F (methanol-water, 40:10, v/v), the LOD values were 0.019, 0.024, and 0.053 μg/spot, respectively. Therefore, for economical reasons, TLC plates should be recommended for analyses of caffeine, propyphenazone, and paracetamol, which are several times cheaper than HPTLC plates.
Special Issue on “Advances in Bioprocess Technology”
Authors: Raganati Francesca , Procentese Alessandra
Published: June 2024

This Special Issue, “Advances in Bioprocess Technology”, focuses on the latest advancements in sustainable bioprocess technologies [...]
Exploration and Frontier of Coal Spontaneous Combustion Fire Prevention Materials
Authors: Han Dandan , Niu Guchen , Zhu Hongqing , Chang Tianyao , Liu Bing , Ren Yongbo , Wang Yu , Song Baolin
Published: June 2024

Mine fires have always been one of the disasters that restrict coal mining in China and endanger the life safety of underground workers. The research and development of new fire prevention materials are undoubtedly important to ensure the safe and efficient production of modern mines. At present, the main inhibiting materials used are grout material, inert gas, retarding agent, foam, gel, and so on. In order to explore the current situation of coal spontaneous combustion (CSC) fire prevention, the existing fire prevention materials were reviewed and prospected from three aspects: physical, chemical, and physicochemical inhibition. The results show that, at present, most of the methods of physicochemical inhibition are used to inhibit CSC. Antioxidants have become popular chemical inhibitors in recent years. In terms of physical inhibition, emerging biomass-based green materials, including foams, gels, and gel foams, are used to inhibit CSC. In addition, CSC fire-fighting materials also have shortcomings, including incomplete research on the mechanism of material action, poor stability of inhibitory properties, low efficiency, and economic and environmental protection to be improved. The future research direction of fire-fighting materials will be based on theoretical experiments and numerical simulation to study the mechanism and characteristics of CSC and develop new directional suppression materials with physicochemical synergies. These findings have extremely important implications for improving materials designed to prevent CSC.
A Preliminary Report on the Sanskrit Manuscript of the Uttaragrantha of the Mūlasarvāstivāda Vinaya
Authors: Lee Hyebin
Published: June 2024

The discovery of the Schøyen–Virginia manuscript of the Uttaragrantha provides significant insights into the Mūlasarvāstivāda Vinaya. This newly identified Sanskrit manuscript offers a fresh perspective on monastic law codes, contributing original Sanskrit terms previously known only through Tibetan and Chinese translations, thereby enhancing our knowledge of Sanskrit–Tibetan–Chinese Vinaya terminologies. Also, by adding itself as a new textual witness to the Mūlasarvāstivāda Vinaya, it demonstrates the complex textual history and underscores the potential multiplicity in the Mūlasarvāstivāda Vinaya traditions or even the “Greater Sarvāstivāda” Vinaya traditions. Variations in chapter sequencing across extant versions of the Uttaragrantha suggest the possibility of the chapters originally existing as independent texts rather than as a collective, the Uttaragrantha. This article presents the latest findings on the Sanskrit manuscript fragments of the Uttaragrantha in the Schøyen Collection and the private collection, Virginia. Furthermore, it attempts to show the role of the S-V manuscript of the Uttaragrantha in improving our textual understanding of the Uttaragrantha and examining the potential multiplicity in the Mūlasarvāstivāda Vinaya traditions.
Revisiting the Institution of Bnay and Bnoth Qyōmo in the Syriac Tradition
Authors: Malki Malatius Malki
Published: June 2024

A group of Syriac Christian believers existed during the fourth century called the Bnay Qyōmo (with their female counterparts known as the Bnoth Qyōmo): the Sons and Daughters of the Covenant. There has been considerable controversy about the nature of this archaic Syrian monastic movement or, as it is known to some scholars, the Syriac “Proto-Monastic Tradition”. The controversy has not only been about the name, but also the origin, habits, and ascetic way of life of the Bnay Qyōmo. The intention of the present article is not to elaborate on the various terminology used to describe the Bnay Qyōmo or the nature of their vows and expected duties as introduced by Aphrahat and other Syrian Fathers, for these have been studied by many scholars. Rather, the intention of this article is to review some of the material discussing this group by key Syriac Fathers to present a fresh reading of the historical record to better apprize the order’s regulations and its social and ecclesiastical roles within the Syriac-speaking Church during the fourth and fifth centuries AD. The main Syriac writers who dealt with this topic were Aphrahat, known as “the Persian Sage” (ca. 260–345), and Rabūla, Bishop of Edessa (flor. 420s). Whilst the order appears to have declined by about the eighth century, understanding the roles of the Bnay Qyōmo during the earlier period (the focus of this writing) is crucial for explaining the development of the Syriac Tradition.
Research on Buddhist Cosmology from the Perspective of Religious Comparison
Authors: Ji Huachuan , Wang Jinjian
Published: June 2024

With regard to the assertion of the nature of the world, primitive Buddhism advocates “all phenomena that arise from causes” and opposes the existence of “God” or “Creator”, who created everything in the universe, which is significantly different from monotheistic beliefs such as Brahmanism, Christianity, and Islam and is therefore often called “atheism”. This paper introduces the Buddhist cosmology of Mount Sumeru and the tri-sahasra mahā-sahasra lokadhātu under the perspective of comparative religions and the first human beings who came to this world from the ābhāsvara-deva as recorded in the Buddhist scriptures and explores the question of whether Buddhism is atheistic. It is believed that the key to the debate between Chinese and Western scholars on whether Buddhism is atheistic is the difference in understanding the concept of “God”. Buddhism does not deny the supernatural power of “ghosts and gods”, so its essence is still theism.
Mediterranean Wild Pear Fruits as a Neglected but Valuable Source of Phenolic Compounds
Authors: Molinu Maria Giovanna , Sanna Federico , D’hallewin Guy , Re Giovanni Antonio , Sulas Leonardo , Piluzza Giovanna
Published: June 2024

The genus Pyrus has a long history in Sardinia (Italy), where two wild pear species (P. spinosa Forssk. and P. pyraster (L.) Burgsd.) and Pyrus communis L. cultivars are extensively distributed. Even if neglected, these taxa represent well-adapted key resources for redesigning sustainable farming systems. This report aims at shedding light on the phenolic fingerprint and antioxidant properties of wild pear fruits and comparing their traits with those of the studied pear cultivar germplasm (PCG). Fruits of wild pear species were collected, and flesh, peel, and core subsamples were analyzed. Moreover, available data from previous research on PCG were analyzed. The contents of total phenolics (TotP), total flavonoids (TotF), and condensed tannins (CT), as well as the antioxidant capacity, were similar in the flesh of the two wild species. However, P. spinosa had significantly higher values of TotP (89 g GAE kg−1 DM) and CT (33 g DE kg−1 DM) in the peel. Eleven individual phenolic compounds were identified and quantified in the fruit flesh, 14 in both peel and core. For both wild species, arbutin and chlorogenic acid were the main phenolic compounds, followed by the quercetin glycosides. Comparing the antioxidant capacity and TotF fruit flesh values of wild pears with those of PCG, the latter resulted up to 15-fold lower. The wild types showed unique metabolite profiles. Results support novel insights on the phytochemicals of wild pear fruits.
Post-Recycling Sodium Polyacrylate with Nanostructural Halloysite Additive as the Basic Components of the New Hybrid Soil Modifier—The Circular Economy Rules for SAP in Agriculture
Authors: Sakiewicz Piotr , Piotrowski Krzysztof , Rajca Mariola , Cieślak Szczepan
Published: June 2024

Post-recycling sodium polyacrylate (SPA) derived from the selective recycling of personal hygiene products was tested as a potential key component of soil modifiers. Recycled SPA in combination with layered aluminosilicate (halloysite) with the sorption capacity of fertilizers allows for the creation of a fully functional, original system responsible for both water retention in soil and controlled dosing of fertilizers. The use of post-recycling SPA (possibly partially supplemented with virgin SPA) is advantageous for the natural environment. The results of different water types retention in post-recycling SPA (deionized water, rainwater, tap water) are discussed. The most beneficial effects of R-SPA swelling and water accumulation are achieved when it comes into contact with rainwater. The clear impact of Mg2+ ions on the sorption capacity of R-SPA introduces the need for a technological compromise between the solution composition (N, P, K or N, P, K, Mg) affecting its fertilization value and the possibility of obtaining maximum R-SPA swelling. The sorption capacity of R-SPA can be slightly increased by using R-SPA/V-SPA mixtures, but R-SPA still plays a decisive role in this system. The possibility of technological adjustment of the leaching rate of fertilizer components under dynamic conditions using different combinations of (R-SPA/V-SPA)/HAL was demonstrated by column tests.
Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University
Authors: Makarova Maria , Poberovskii Anatoly , Polyakov Alexander , Imkhasin Khamud H. , Ionov Dmitry , Makarov Boris , Kostsov Vladimir , Foka Stefani , Abakumov Evgeny
Published: June 2024

Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.
Oxygen and Air Density Retrieval Method for Single-Band Stellar Occultation Measurement
Authors: Li Zheng , Wu Xiaocheng , Tu Cui , Yang Junfeng , Hu Xiong , Yan Zhaoai
Published: June 2024

The stellar occultation technique is capable of atmospheric trace gas detection using the molecule absorption characteristics of the stellar spectra. In this paper, the non-iterative and iterative retrieval methods for oxygen and air density detection by stellar occultation are investigated. For the single-band average transmission data in the oxygen 761 nm A-band, an onion-peeling algorithm is used to calculate the effective optical depth of each atmospheric layer, and then the optical depth is used to retrieve the oxygen number density. The iteration method introduces atmospheric hydrostatic equilibrium and the ideal gas equation of state, and it achieves a more accurate retrieval of the air density under the condition of a priori temperature deviation. Finally, this paper analyzes the double solution problem in the iteration process and the ideas to improve the problem. This paper provides a theoretical basis for the development of a new type of atmospheric density detection method.
Radio Frequency Interference Mitigation in Data and Image Bi-Domains for an Aperture Synthesis Radiometer
Authors: Zhang Juan , Li Hong , Li Yinan , Zhuang Lehui , Dou Haofeng
Published: June 2024

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the “zero trap” region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the “zero trap” region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing.
Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines
Authors: Ruiz Desiree , Casas Abraham , Escobar Cesar Adolfo , Perez Alejandro , Gonzalez Veronica
Published: June 2024

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.
Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques
Authors: Gelgi Metehan , Guan Yueting , Arunachala Sanjay , Samba Siva Rao Maddi , Dragoni Nicola
Published: June 2024

Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.
Transferring Learned Behaviors between Similar and Different Radios
Authors: Muller Braeden P. , Olds Brennan E. , Wong Lauren J. , Michaels Alan J.
Published: June 2024

Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC.
Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment
Authors: Bamashmos Saeed , Chilamkurti Naveen , Shahraki Ahmad Salehi
Published: June 2024

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.
Gamified Exercise with Kinect: Can Kinect-Based Virtual Reality Training Improve Physical Performance and Quality of Life in Postmenopausal Women with Osteopenia? A Randomized Controlled Trial
Authors: Riaz Saima , Shakil Ur Rehman Syed , Hassan Danish , Hafeez Sana
Published: June 2024

Background: Osteopenia, caused by estrogen deficiency in postmenopausal women (PMW), lowers Bone Mineral Density (BMD) and increases bone fragility. It affects about half of older women’s social and physical health. PMW experience pain and disability, impacting their health-related Quality of Life (QoL) and function. This study aimed to determine the effects of Kinect-based Virtual Reality Training (VRT) on physical performance and QoL in PMW with osteopenia. Methodology: The study was a prospective, two-arm, parallel-design, randomized controlled trial. Fifty-two participants were recruited in the trial, with 26 randomly assigned to each group. The experimental group received Kinect-based VRT thrice a week for 24 weeks, each lasting 45 min. Both groups were directed to participate in a 30-min walk outside every day. Physical performance was measured by the Time Up and Go Test (TUG), Functional Reach Test (FRT), Five Times Sit to Stand Test (FTSST), Modified Sit and Reach Test (MSRT), Dynamic Hand Grip Strength (DHGS), Non-Dynamic Hand Grip Strength (NDHGS), BORG Score and Dyspnea Index. Escala de Calidad de vida Osteoporosis (ECOS-16) questionnaire measured QoL. Both physical performance and QoL measures were assessed at baseline, after 12 weeks, and after 24 weeks. Data were analyzed on SPSS 25. Results: The mean age of the PMW participants was 58.00 ± 5.52 years. In within-group comparison, all outcome variables (TUG, FRT, FTSST, MSRT, DHGS, NDHGS, BORG Score, Dyspnea, and ECOS-16) showed significant improvements (p < 0.001) from baseline at both the 12th and 24th weeks and between baseline and the 24th week in the experimental group. In the control group, all outcome variables except FRT (12th week to 24th week) showed statistically significant improvements (p < 0.001) from baseline at both the 12th and 24th weeks and between baseline and the 24th week. In between-group comparison, the experimental group demonstrated more significant improvements in most outcome variables at all points than the control group (p < 0.001), indicating the positive additional effects of Kinect-based VRT. Conclusion: The study concludes that physical performance and QoL measures were improved in both the experimental and control groups. However, in the group comparison, these variables showed better results in the experimental group. Thus, Kinect-based VRT is an alternative and feasible intervention to improve physical performance and QoL in PMW with osteopenia. This novel approach may be widely applicable in upcoming studies, considering the increasing interest in virtual reality-based therapy for rehabilitation.
ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry
Authors: Huang Xiaotao , Chen Xingbin , Zhang Ning , He Hongjie , Feng Sang
Published: June 2024

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.
RSDNet: A New Multiscale Rail Surface Defect Detection Model
Authors: Du Jingyi , Zhang Ruibo , Gao Rui , Nan Lei , Bao Yifan
Published: June 2024

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.
Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems
Authors: Ding Yong , Deng Honggao , Xie Yuelei , Wang Haitao , Sun Shaoshuai
Published: June 2024

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.
Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device
Authors: Sfravara Felice , Barberi Emmanuele , Bongiovanni Giacomo , Chillemi Massimiliano , Brusca Sebastian
Published: June 2024

Oscillating Water Column (OWC) systems harness wave energy using a partially submerged chamber with an underwater opening. The Savonius turbine, a vertical-axis wind turbine, is well-suited for this purpose due to its efficiency at low speeds and self-starting capability, making it an ideal power take-off (PTO) mechanism in OWC systems. This study tested an OWC device with a Savonius turbine in an air duct to evaluate its performance under varying flow directions and loads. An innovative aspect was assessing the influence of power augmenters (PAs) positioned upstream and downstream of the turbine. The experimental setup included load cells, Pitot tubes, differential pressure sensors and rotational speed sensors. Data obtained were used to calculate pressure differentials across the turbine and torque. The primary goal of using PA is to increase the CP–λ curve area without modifying the turbine geometry, potentially enabling interventions on existing turbines without rotor dismantling. Additionally, another novelty is the implementation of a regression Machine-Learning algorithm based on decision trees to analyze the influence of various features on predicting pressure differences, thereby broadening the scope for further testing beyond physical experimentation.
Efficiency–Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation
Authors: Xiao Bo , Perry Stuart , Gao Xiujing , Huang Hongwu
Published: June 2024

The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.
Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management
Authors: Luo Peng , Wang Buhong , Tian Jiwei , Liu Chao , Yang Yong
Published: June 2024

Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).
Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection
Authors: Jiang Liangwei , Yang Hongyin , Liu Weijun , Ye Zhongtao , Pei Junwen , Liu Zhangjun , Fan Jianfeng
Published: June 2024

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.
Federated Learning in the Detection of Fake News Using Deep Learning as a Basic Method
Authors: Machová Kristína , Mach Marián , Balara Viliam
Published: June 2024

This article explores the possibilities for federated learning with a deep learning method as a basic approach to train detection models for fake news recognition. Federated learning is the key issue in this research because this kind of learning makes machine learning more secure by training models on decentralized data at decentralized places, for example, at different IoT edges. The data are not transformed between decentralized places, which means that personally identifiable data are not shared. This could increase the security of data from sensors in intelligent houses and medical devices or data from various resources in online spaces. Each station edge could train a model separately on data obtained from its sensors and on data extracted from different sources. Consequently, the models trained on local data on local clients are aggregated at the central ending point. We have designed three different architectures for deep learning as a basis for use within federated learning. The detection models were based on embeddings, CNNs (convolutional neural networks), and LSTM (long short-term memory). The best results were achieved using more LSTM layers (F1 = 0.92). On the other hand, all three architectures achieved similar results. We also analyzed results obtained using federated learning and without it. As a result of the analysis, it was found that the use of federated learning, in which data were decomposed and divided into smaller local datasets, does not significantly reduce the accuracy of the models.
Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies
Authors: Zawada Stephanie J. , Ganjizadeh Ali , Hagen Clint E. , Demaerschalk Bart M. , Erickson Bradley J.
Published: June 2024

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.
Integrating Cosmic Microwave Background Readings with Celestial Navigation to Enhance Deep Space Navigation
Authors: Albuquerque Pedro Kukulka de , Santos Willer Gomes dos , Costa Paulo , Barreto Alexandre
Published: June 2024

This research unveils a cutting-edge navigation system for deep space missions that utilizes cosmic microwave background (CMB) sensor readings to enhance spacecraft positioning and velocity estimation accuracy significantly. By exploiting the Doppler-shifted CMB spectrum and integrating it with optical measurements for celestial navigation, this approach employs advanced data processing through the Unscented Kalman Filter (UKF), enabling precise navigation amid the complexities of space travel. The simulation results confirm the system’s exceptional precision and resilience in deep space missions, marking a significant advancement in astronautics and paving the way for future space exploration endeavors.
Discontinuous Deformation Monitoring of Smart Aerospace Structures Based on Hybrid Reconstruction Strategy and Fiber Bragg Grating
Authors: Chen Kangyu , Fan Hengzhen , Bao Hong
Published: June 2024

A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment.
Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level
Authors: Liu Zhiyang , Fan Qi , Liu Jianjian , Zhou Luoyu , Zhang Zhengbing
Published: June 2024

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells’ dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield’s central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.
Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms
Authors: Duong Minh-Thien , Nguyen Thi Bao-Tran , Lee Seongsoo , Hong Min-Cheol
Published: June 2024

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.
Occlusion Robust Cognitive Engagement Detection in Real-World Classroom
Authors: Xiao Guangrun , Xu Qi , Wei Yantao , Yao Huang , Liu Qingtang
Published: June 2024

Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced–You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students’ Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%.