bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Adherend Thickness on Near-Field Ultrasonic Welding of Single-Lap CF/LMPAEK Thermoplastic Composite Joints
Authors: Guevara-Sotelo Natalia Sofia , Fernandez Villegas Irene
Published: October 2023

Ultrasonic welding is a fast and promising joining technique for thermoplastic composite parts. Understanding how changing the part thickness affects the process is crucial to its future upscaling and industrialization. This article presents an initial insight into the effect of the adherend’s thickness on the near-field ultrasonic welding of CF/LMPAEK thermoplastic composites. Different thicknesses of the top and bottom adherend were welded and analyzed using the output data of the welding equipment, temperature measurements, and other visual characterization techniques. Increasing the thickness of both the top and the bottom adherends showed to increase the power consumed during welding. An overshoot in the power needed at the onset of the welding process for increased thickness of the top adherend precluded welding beyond a threshold thickness of 4.72 mm. In the case of the thicker top adherends, there was also melting of the energy director and early fiber squeeze-out within the top adherend as a result of increased bulk heating. Increased bulk heating was hypothesized to be caused by increased hammering, as indicated by the amplitude readings for thicker adherends. Welding with a higher force, which is known to reduce hammering, corroborated this hypothesis as fiber squeeze-out within the top adherend was not observed. It is believed that hammering contributes to heating by causing an oscillatory impact excitation that is close to the natural frequencies of the system, which would result in amplification of the cyclic strain and subsequent increase in the viscoelastic heating in the adherend.
Review and New Perspectives on Non-Layered Manganese Compounds as Electrode Material for Sodium-Ion Batteries
Authors: Alcántara Ricardo , Pérez-Vicente Carlos , Lavela Pedro , Tirado José L. , Medina Alejandro , Stoyanova Radostina
Published: October 2023

After more than 30 years of delay compared to lithium-ion batteries, sodium analogs are now emerging in the market. This is a result of the concerns regarding sustainability and production costs of the former, as well as issues related to safety and toxicity. Electrode materials for the new sodium-ion batteries may contain available and sustainable elements such as sodium itself, as well as iron or manganese, while eliminating the common cobalt cathode compounds and copper anode current collectors for lithium-ion batteries. The multiple oxidation states, abundance, and availability of manganese favor its use, as it was shown early on for primary batteries. Regarding structural considerations, an extraordinarily successful group of cathode materials are layered oxides of sodium, and transition metals, with manganese being the major component. However, other technologies point towards Prussian blue analogs, NASICON-related phosphates, and fluorophosphates. The role of manganese in these structural families and other oxide or halide compounds has until now not been fully explored. In this direction, the present review paper deals with the different Mn-containing solids with a non-layered structure already evaluated. The study aims to systematize the current knowledge on this topic and highlight new possibilities for further study, such as the concept of entatic state applied to electrodes.
Eccentric Exercises on the Board with 17-Degree Decline Are Equally Effective as Eccentric Exercises on the Standard 25-Degree Decline Board in the Treatment of Patellar Tendinopathy
Authors: Knež Vladimir , Hudetz Damir
Published: October 2023

Background and Objectives: Patellar tendinopathy is one of the most significant problems in jumping and running athletes. Eccentric quadriceps exercise has been introduced into the therapy of patients with patellar tendinopathy in order to avoid weakening the tendon during rehabilitation. The use of decline boards with a decline angle of 25° has been the cornerstone of therapy over the last two decades. Biomechanical studies have suggested that an equal or potentially better outcome could be achieved with lower angles of decline (up to 16°). Materials and Methods: In this present research, we compared the effects of two various decline board angles on the clinical outcome of patients treated for patellar tendinopathy by performing eccentric quadriceps exercises. Patients were randomly allocated into two groups: patients practicing on the standard board with a 25° decline, and patients practicing on the 17° decline (n = 35 per group). Results: After 6 weeks of exercise, we found a significant improvement in all the clinical scores (VISA-P score, KOOS score, Lysholm Knee Questionnaire/Tegner Activity Scale, and VAS scale) of treated patients. However, there was no significant difference between the patients who performed eccentric quadriceps exercises on the standard 25° decline board and those exercising on the 17° decline board. A smaller additional degree of improvement was visible at the end of the follow-up period (at 12 weeks), but, again, no statistical difference could be detected between the investigated groups. We conclude that both treatment options provide similar short-term and midterm benefits regarding improvements in pain and clinical scores. The improvement in clinical scores does not depend on age, sex, BMI, or the professional sport of the patient. Conclusions: Our findings encourage changes in the decline angle of the board in the case of a patient’s discomfort in order to achieve better compliance without affecting the recovery.
Amelioration of Rheumatoid Arthritis by Fragaria nubicola (Wild Strawberry) via Attenuation of Inflammatory Mediators in Sprague Dawley Rats
Authors: Mashaal Kiran , Shabbir Arham , Shahzad Muhammad , Mobashar Aisha , Akhtar Tasleem , Fatima Tabinda , Riaz Bushra , Alharbi Rana , Fatima Afreen , Alanezi Abdulkareem A. , Ahmad Ashfaq
Published: October 2023

Background and Objectives: Fragaria nubicola has never been evaluated scientifically for its anti-arthritic potential despite its use in folkloric systems of medicine. The research was conducted to assess the potential of F. nubicola against rheumatoid arthritis. Materials and Methods: The current study provided scientific evidence by evaluating the effects of plants using an in vivo CFA-induced model of arthritic rats and subsequent microscopic histopathological evaluation of ankle joints along with the determination of paw edema using a digital water displacement plethysmometer. The study also gave insight by determining levels of pro-inflammatory cytokines, matrix metalloproteinase enzymes (MMPs), prostaglandin E2 (PGE2), nuclear factor kappa B (NF-κB), vascular endothelial growth factor (VEGF), and biochemical and hematological parameters. GCMS analysis was also conducted for the identification of possible anti-inflammatory plant constituents. Results: The data showed that F. nubicola-treated groups attenuated the progression of arthritis and paw edema. Microscopic histopathological evaluation validated the anti-arthritic potential by showing amelioration of bone erosion, infiltration of inflammatory cells, and pannus formation. RT-PCR analysis displayed that treatment with F. nubicola down-regulated IL1β, IL6, TNFα, NF-κB, VEGF, MMP2, MMP3, and MMP9 levels. Moreover, ELISA exhibited a reduction in levels of PGE2 levels in treatment groups. The levels of RBCs, platelets, WBCs, and Hb content were found to be nearly similar to negative control in the treated group. Statistically, a non-significant difference was found when all groups were compared for urea, creatinine, ALT, and AST analysis, indicating the safety of plant extract and fractions at test doses. GCMS analysis of extract and fractions showed the existence of many anti-inflammatory and antioxidant phytochemicals. Conclusion: In conclusion, F. nubicola possessed anti-arthritic properties that might be attributed to the amelioration of MMPs and pro-inflammatory cytokines.
Recent Advances of Graphene Quantum Dots in Chemiresistive Gas Sensors
Authors: Zhu Xiaofeng , Li Yongzhen , Cao Pei , Li Peng , Xing Xinzhu , Yu Yue , Guo Ruihua , Yang Hui
Published: October 2023

Graphene quantum dots (GQDs), as 0D graphene nanomaterials, have aroused increasing interest in chemiresistive gas sensors owing to their remarkable physicochemical properties and tunable electronic structures. Research on GQDs has been booming over the past decades, and a number of excellent review articles have been provided on various other sensing principles of GQDs, such as fluorescence-based ion-sensing, bio-sensing, bio-imaging, and electrochemical, photoelectrochemical, and electrochemiluminescence sensing, and therapeutic, energy and catalysis applications. However, so far, there is no single review article on the application of GQDs in the field of chemiresistive gas sensing. This is our primary inspiration for writing this review, with a focus on the chemiresistive gas sensors reported using GQD-based composites. In this review, the various synthesized strategies of GQDs and its composites, gas sensing enhancement mechanisms, and the resulting sensing characteristics are presented. Finally, the current challenges and future prospects of GQDs in the abovementioned application filed have been discussed for the more rational design of advanced GQDs-based gas-sensing materials and innovative gas sensors with novel functionalities.
Investigations of Nanoscale Columnar AlxGa1-xN/AlN Heterostructures Grown on Silicon Substrates with Different Modifications of the Surface
Authors: Seredin Pavel Vladimirovich , Kurilo Nikolay , Goloshchapov Dmitry L. , Kashkarov Vladimir , Lenshin Aleksandr S. , Buylov Nikita , Nesterov Dmitry , Mizerov Andrey , Kukushkin Sergey A. , Timoshnev S. , Shubina K. Yu. , Sobolev M. S.
Published: October 2023

The growth of nanoscale columnar AlxGa1-xN/AlN heterostructures on the surface of silicon substrates using plasma-activated nitrogen molecular-beam epitaxy was investigated in this work. Silicon substrates include atomic-smooth cSi substrate, Si substrate with a transition layer of porous silicon porSi/cSi and a hybrid substrate involving a silicon carbide layer grown with matched substitution of the atoms on the surface of porous silicon SiC/porSi/cSi. A complex analysis performed using a set of structural and spectroscopic techniques demonstrated that the epitaxial growth of the nuclear AlN layer on all types of the substrates in a N-enriched environment resulted in the formation of AlxGa1-xN/AlN heterostructures with a Ga-polar surface, which was realized only on the SiC/porSi/cSi substrate. The layer of AlxGa1-xN on cSi and porSi/cSi substrates was in the state of disordered alloy with an excess of gallium atom content. It was shown that a great difference in the lattice parameters of a substrate–film pair resulted not only in the appearance of a number of various defects but also in a considerable effect on the chemical process of the formation of the alloys, in our case, the AlxGa1-xN alloy. It was shown that nanoscale columns of AlxGa1-xN formed on SiC/porSi/cSi substrate were inclined relative to the c-axis, which was connected with the features of the formation of a SiC layer by the matched substitution of the atoms on the porous Si substrate, resulting in the formation of the inclined (111) SiC facets at the boundary of the (111) Si surface and pores in Si. Optical studies of the grown samples demonstrated that the optical band-to-band transition for the AlxGa1-xN alloy with Eg = 3.99 eVB was observed only for the heterostructure grown on the SiC/porSi/cSi substrate. A qualitative model is proposed to explain the difference in the formation of AlxGa1-xN layers on the substrates of cSi, porSi/cSi and SiC/porSi/cSi. The results obtained in our work demonstrate the availability of using SiC/porSi/cSi substrates for the integration of silicon technology and that used for the synthesis of nanoscale columnar AlxGa1-xN heterostructures using plasma-activated molecular-beam epitaxy with a nitrogen source.
A Comparative Study on the Properties of Rosin-Based Epoxy Resins with Different Flexible Chains
Authors: Deng Lianli , Wang Zehua , Qu Bailu , Liu Ying , Qiu Wei , Qi Shaohe
Published: October 2023

This study aims to reveal the effects of flexible chain lengths on rosin-based epoxy resin’s properties. Two rosin-based epoxy monomers with varying chain lengths were synthesized: AR-EGDE (derived from ethylene glycol diglycidyl ether-modified acrylic acid rosin) and ARE (derived from acrylic acid rosin and epichlorohydrin). Diethylenetriamine (DETA), triethylenetetramine (TETA), and tetraethylenepentamine (TEPA) with different flexible chain lengths were used as curing agents. The adhesion, impact, pencil hardness, flexibility, water and heat resistance, and weatherability of the epoxy resins were systematically examined. It was found that when the flexible chains of rosin-based epoxy monomers were grown from ARE to AR-EGDE, due to the increased space of rosin-based fused rings, the toughness, adhesion, and water resistance of the rosin-based epoxy resins were enhanced, while the pencil hardness and heat resistance decreased. However, when the flexible chains of curing agents were lengthened, the resin’s performance did not change significantly because the space between the fused rings changed little. This indicates that the properties of the rosin-based resins can only be altered when the introduced flexible chain increases the space between the fused rings. The study also compared rosin-based resins to E20, a commercial petroleum-based epoxy of the bisphenol A type. The rosin-based resins demonstrated superior adhesion, water resistance, and weatherability compared to the E20 resins, indicating the remarkable durability of the rosin-based resin.
Study on Modification of Poplar Wood via Composite Impregnation with Silica Sol/Melamine–Glyoxal Resin
Authors: Liu Mingli , Li Xiangrui , Qin Zexiu , Liu Wenbo , Li Chunfeng , Le Lei
Published: October 2023

In order to overcome the defects of fast-growing poplar wood, such as low strength and poor toughness, this paper introduces a method of modifying poplar wood via impregnation with silica sol/melamine–glyoxal (silica sol/MG) resin and explores its effects on the physical, mechanical, and thermal properties of poplar wood. It was found via scanning electron microscopy that the composite modifier covered and filled the cell lumen, cell interstitial space, and cell wall pores of poplar wood. Further, infrared spectroscopy and X-ray photoelectron spectroscopy analyses confirmed that chemical cross-linking occurred between the silica sol/MG resin composite modifier and the internal groups of poplar wood and that the Si-O-Si flexible long chains introduced in the composite modifier formed a cross-linking network with poplar wood such as Si-O-Si and Si-O-C, which led to the improvement of the physical and mechanical properties and the enhancement of the thermal stability of poplar wood. The method provides a theoretical basis for the high-value utilization of fast-growing poplar wood.
Validity and Reliability of Inertial Measurement Units in Active Range of Motion Assessment in the Hip Joint
Authors: Stołowski Łukasz , Niedziela Maciej , Lubiatowski Bartłomiej , Lubiatowski Przemysław , Piontek Tomasz
Published: October 2023

Measuring joint range of motion has traditionally occurred with a universal goniometer, inclinometer, or expensive laboratory systems. The popularity of the inertial measurement unit (IMU) is growing, enabling the small and even single portable device to measure the range of motion. Until now, they were not used to assess hip joint range of motion. Our study aimed to check the validity of IMUs in assessing hip range of motion and compare them to other measurement devices—universal goniometer and inclinometer. Twenty participants completed three hip movements (flexion in standing and prone internal and external rotation) on both hips. Two testers independently assessed each movement with a goniometer, digital inclinometer, and IMU at different time points. To assess the agreement of active hip ROM between devices, Intraclass Correlation Coefficient (ICC) and Bland–Altman analysis were used. Furthermore, inter-rater and intra-rater reliability were also assessed by using ICC and Bland–Altman analysis. Limits of agreement (LOA) were calculated using Bland–Altman plots. The IMU demonstrated good to excellent validity (ICC: 0.87–0.99) compared to the goniometer and digital inclinometer, with LOAs < 9°, across all tested movements. Intra-rater reliability was excellent for all devices (ICC: 0.87–0.99) with LOAs < 7°. However, inter-rater reliability was moderate for flexion (ICC: 0.58–0.59, LOAs < 22.4) and poor for rotations (ICC: −0.33–0.04, LOAs < 7.8°). The present study shows that a single inertial measurement unit (RSQ Motion, RSQ Technologies, Poznan, Poland) could be successfully used to assess the active hip range of motion in healthy subjects, comparable to other methods accuracy.
Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
Authors: Khan Mohd Jawed , Singh Pankaj Pratap , Pradhan Biswajeet , Alamri Abdullah , Lee Chang-Wook
Published: October 2023

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.
Recent Advances in Machine Learning for Network Automation in the O-RAN
Authors: Hamdan Mutasem Q. , Lee Haeyoung , Triantafyllopoulou Dionysia , Borralho Rúben , Kose Abdulkadir , Amiri Esmaeil , Mulvey David , Yu Wenjuan , Zitouni Rafik , Pozza Riccardo , Hunt Bernie , Bagheri Hamidreza , Foh Chuan Heng , Heliot Fabien , Chen Gaojie , Xiao Pei , Wang Ning , Tafazolli Rahim
Published: October 2023

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.
Study on the Recognition of Coal Miners’ Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision
Authors: Yao Wei , Wang Aiming , Nie Yifan , Lv Zhengyan , Nie Shuai , Huang Congwei , Liu Zhenyu
Published: October 2023

The hoist cage is used to lift miners in a coal mine’s auxiliary shaft. Monitoring miners’ unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners’ statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.
Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree
Authors: Palacín Jordi , Rubies Elena , Bitriá Ricard , Clotet Eduard
Published: October 2023

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building’s floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra’s algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building.
Effect of Robotic-Assisted Gait at Different Levels of Guidance and Body Weight Support on Lower Limb Joint Kinematics and Coordination
Authors: Cherni Yosra , Blache Yoann , Begon Mickael , Ballaz Laurent , Dal Maso Fabien
Published: October 2023

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip–knee (r = 0.67–0.91), hip–ankle (r = 0.66–0.85), and knee–ankle (r = 0.90–0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy.
Characterizing the Internal Structure of Chinese Steamed Bread during Storage for Quality Evaluation Using X-ray Computer Tomography
Authors: Yu Yonghui , Jia Chanchan , Wang Jiahua , Pi Fuwei , Dai Huang , Liu Xiaodan
Published: October 2023

Chinese steamed bread (CSB) is a traditional food of the Chinese nation, and the preservation of its quality and freshness during storage is very important for its industrial production. Therefore, it is necessary to study the storage characteristics of CSB. Non-destructive CT technology was utilized to characterize and visualize the microstructure of CSB during storage, and also to further study of quality changes. Two-dimensional and three-dimensional images of CSBs were obtained through X-ray scanning and 3D reconstruction. Morphological parameters of the microstructure of CSBs were acquired based on CT image using image processing methods. Additionally, commonly used physicochemical indexes (hardness, flexibility, moisture content) for the quality evaluation of CSBs were analyzed. Moreover, a correlation analysis was conducted based on the three-dimensional morphological parameters and physicochemical indexes of CSBs. The results showed that three-dimensional morphological parameters of CSBs were negatively correlated with moisture content (Pearson correlation coefficient range−0.86~−0.97) and positively correlated with hardness (Pearson correlation coefficient range−0.87~0.99). The results indicate the inspiring capability of CT in the storage quality evaluation of CSB, providing a potential analytical method for the detection of quality and freshness in the industrial production of CSB.
BRT: An Efficient and Scalable Blockchain-Based Revocation Transparency System for TLS Connections
Authors: Xing Qianqian , Wang Xiaofeng , Xu Xinyue , Lin Jiaqi , Wang Fei , Li Cui , Wang Baosheng
Published: October 2023

Log-based public key infrastructure(PKI) refers to a robust class of CA-attack-resilient PKI that enhance transparency and accountability in the certificate revocation and issuance process by compelling certificate authorities (CAs) to submit revocations to publicly and verifiably accessible logs. However, log-based PKIs suffer from a reliance on centralized and consistent sources of information, rendering them susceptible to split-world attacks, and they regrettably fail to provide adequate incentives for recording or monitoring CA behavior. Blockchain-based PKIs address these limitations by enabling decentralized log audits through automated financial incentives. However, they continue to face challenges in developing a scalable revocation mechanism suited for lightweight clients. In this paper, we introduce BRT, a scalable blockchain-based system for certificate and revocation transparency. It serves to log, audit, and validate the status of certificates within the transport layer security (TLS)/secure sockets layer(SSL) PKI domain. We designed an audit-on-chain framework, coupled with an off-chain storage/computation system, to enhance the efficiency of BRT when operating in a blockchain environment. By implementing a blockchain-based prototype, we demonstrate that BRT achieves storage-efficient log recording with a peak compression rate reaching 8%, cost-effective log updates for large-scale certificates, and near-instantaneous revocation checks for users.
Cooperative Positioning Method of a Multi-UAV Based on an Adaptive Fault-Tolerant Federated Filter
Authors: Zhang Pengfei , Ma Zhenhua , He Yin , Li Yawen , Cheng Wenzheng
Published: October 2023

Aiming at the problem of the low cooperative positioning accuracy and robustness of multi-UAV formation, a cooperative positioning method of a multi-UAV based on an adaptive fault-tolerant federated filter is proposed. Combined with the position of the follower UAV and leader UAV, and the relative range between them, a cooperative positioning model of the follower UAV is established. On this basis, an adaptive fault-tolerant federated filter is designed. Fault detection and isolation technology are added to improve the positioning accuracy of the follower UAV and the fault tolerance performance of the filter. Meanwhile, the measurement noise matrix is adjusted by the adaptive information allocation coefficient to reduce the impact of undetected fault information on the sub-filter and global estimation accuracy. The simulation results show that the adaptive fault-tolerant federated algorithm can greatly improve the positioning accuracy, which is 83.4% higher than that of the absolute positioning accuracy of a single UAV. In the case of a gradual fault, the method has a stronger fault-tolerant performance and reconstruction performance.
Design of Zeolitic Imidazolate Framework-8-Functionalized Capacitive Micromachined Ultrasound Transducer Gravimetric Sensors for Gas and Hydrocarbon Vapor Detection
Authors: Dzikaras Mindaugas , Barauskas Dovydas , Pelenis Donatas , Vanagas Gailius , Mikolajūnas Marius , Shi Jingming , Baltrusaitis Jonas , Viržonis Darius
Published: October 2023

A capacitive micromachined ultrasound transducer (CMUT) was engineered and functionalized with zeolitic imidazolate framework-8 (ZIF-8) dispersed in a photoresist AZ1512HS (AZ) matrix to function as a gravimetric gas sensor. The sensor response was recorded in the presence of nitrogen, argon, carbon dioxide, and methane gases as well as water, acetylene, a propane/butane mixture, n-hexane, gasoline, and diesel vapors. The photoresist matrix alone was found to have a negligible response to all the gases and vapors, except for water vapor. No visible difference in sensor response was detected when switching from nitrogen to methane gas. However, a strong shift in the sensor resonance frequency was observed when exposed to higher hydrocarbons, ranging from 1 kHz for acetylene to 7.5 kHz for gasoline. Even longer-chain hydrocarbons, specifically kerosene and more so diesel, had a significantly reduced sensor frequency shift compared with gasoline. Sensors functionalized with a thin film of AZ+ZIF-8 demonstrated higher sensitivity in their response to a hydrocarbon molecular mass than without functionalization.
Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers
Authors: Carvalho Gustavo S. , Silva Felipe O. , Pacheco Marcus Vinicius O. , Campos Gleydson A. O.
Published: October 2023

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.
A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points
Authors: Lomas-Barrie Victor , Suarez-Espinoza Mario , Hernandez-Chavez Gerardo , Neme Antonio
Published: October 2023

Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.
Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies
Authors: Nankya Mary , Chataut Robin , Akl Robert
Published: October 2023

Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects. It also highlights the typical threats and vulnerabilities faced by these systems. Moreover, the article identifies key factors that influence the design decisions concerning control, communication, reliability, and redundancy properties of ICS, as these are critical in determining the security needs of the system. The article outlines existing security countermeasures, including network segmentation, access control, patch management, and security monitoring. Furthermore, the article explores the integration of machine learning techniques to enhance the cybersecurity of ICS. Machine learning offers several advantages, such as anomaly detection, threat intelligence analysis, and predictive maintenance. However, combining machine learning with other security measures is essential to establish a comprehensive defense strategy for ICS. The article also addresses the challenges associated with existing measures and provides recommendations for improving ICS security. This paper becomes a valuable reference for researchers aiming to make meaningful contributions within the constantly evolving ICS domain by providing an in-depth examination of the present state, challenges, and potential future advancements.
Is Consumption of Ginger in Daily Life Associated with Sexual Response?
Authors: Wen Guangju , Zhang Yikang , Nyman Thomas J. , Jern Patrick , Santtila Pekka
Published: October 2023

Sexual dysfunction, a globally widespread concern, is associated with numerous negative outcomes. While some evidence indicates the potential of ginger supplementation to enhance sexual function, comprehensive research in this area remains scarce and the potential mechanism behind is unclear. The current study aimed to explore if and how consumption of ginger as part of a daily diet is associated with sexual desire, frequency of sexual behavior, as well as sexual arousal and disgust experienced during these behaviors. Chinese participants (N = 499; 250 women) from an online sample service (Credamo) answered a cross-sectional survey regarding their consumption of ginger, sexual behavior, and sexual desire. Ginger consumption was associated with higher sexual desire, and more frequent sexual behaviors, as well as feelings of sexual arousal during them. Sexual desire and feelings of sexual arousal during specific sexual activities mediated the association between the ginger consumption and the frequency of sexual behaviors, indicating potential pathways of the effects of ginger on sexual behavior. Interestingly, while ginger consumption showed no direct relationship with feelings of disgust during sexual behavior, sexual arousal during these behaviors did positively mediate the association between ginger consumption and feelings of disgust. The findings imply that ginger shows potential as an intervention for enhancing sexual behavior, but further experimental and longitudinal research is needed to fully understand the causal mechanisms underlying the effects of ginger on sexual health.
A Need-Based Approach for Modeling Recurrent Discretionary Activity Participation Patterns for Travel Demand Analysis
Authors: Cho WooKeol , Kim Jinhee , Chung Jin-Hyuk
Published: October 2023

As society advances and various technologies like AI and LLMs are further developed, the proportion of human labor contributing to the productivity of nations and societies is gradually decreasing. This has led to increased attention to the quality of life of individuals, and cases of implementing policies such as a four-day work week are on the rise. Therefore, the objective of this study was to analyze the patterns of how people are spending their increased leisure time amid this social trend and to identify the factors influencing these patterns. Building upon the need-based theory proposed in previous studies, this research analyzed people’s recurrent discretionary activity patterns. Multiday analysis was conducted considering the characteristics of leisure activity patterns, and a hazard-based duration model was estimated for statistical analysis. The research results revealed that people’s patterns of consecutive activities are influenced by various factors, such as socio-economic attributes, time–space budgets, previous activity experiences, and preferences for specific days of the week. Through this, we were able to confirm that socio-demographic and household characteristics, as well as attributes of time/space budgets, influence the growth speed and threshold of needs as suggested in need-based theory. Additionally, we observed a preference for specific days of the week for different types of activities. As a result, people tend to either postpone activities until specific days even when their need has accumulated sufficiently or engage in activities on specific days even when the need has not yet accumulated to the desired level. This study demonstrates novelty in that it utilizes the need-based theory proposed in prior research to identify factors influencing multiday activity participation patterns. Additionally, it presents the first study providing model estimation results from the perspective of need-based theory. The correlation between the time–space budget and discretionary activity patterns identified in this study is expected to serve as a guideline for future transportation-related policies, including regional balanced development. This study demonstrates a unique contribution compared to existing research in that it established that, with improvements in activity/travel conditions, there can be an induced demand for activities. This finding can contribute to the feasibility study of transportation projects and the establishment of policies related to regional balanced development.
Digital Technology, Factor Allocation and Environmental Efficiency of Dairy Farms in China: Based on Carbon Emission Constraint Perspective
Authors: Liu Chenyang , Shi Xiuyi , Li Cuixia
Published: October 2023

The emission of carbon pollutants stemming from dairy farms has emerged as a significant obstacle in mitigating the effects of global warming. China, being a prominent nation in the field of dairy farming, encounters significant challenges related to excessive component input and elevated environmental pollution. Digital technology presents an opportunity to enhance the factor allocation of dairy farms and thus increase their environmental efficiency. This study utilizes survey data from 278 dairy farms in China to examine the effect of digital technology on the allocation of land, labor, and capital variables in dairy farms. The IV-Probit model, IV-Tobit model, treatment effect model, and two-stage least square technique are employed to empirically analyze these impacts. Simultaneously, the intermediate effect model was employed to examine the mediating function of factor allocation in the effect of digital technology on environmental efficiency. The findings indicate that digital technology has the potential to greatly enhance land transfer and land utilization rates in dairy farms. Additionally, it has been observed that digital technology may lead to a decrease in both the proportion and time of labor input. Furthermore, digital technology has the potential to decrease short-term productive input while simultaneously enhancing long-term productive input within dairy farming operations. Digital technology has been found to have an indirect yet beneficial influence on environmental efficiency. This is mostly achieved through the facilitation of resource allocation, specifically in terms of land, labor, and capital aspects. The article presents a set of policy recommendations, including the promotion of extensive integration of digital technology within dairy farms, the facilitation of optimal allocation of production factors in dairy farms, and the implementation of specialized training programs focused on digital technology.
Learning by Autonomous Manifold Deformation with an Intrinsic Deforming Field
Authors: Zhuang Xiaodong , Mastorakis Nikos
Published: October 2023

A self-organized geometric model is proposed for data dimension reduction to improve the robustness of manifold learning. In the model, a novel mechanism for dimension reduction is presented by the autonomous deforming of data manifolds. The autonomous deforming vector field is proposed to guide the deformation of the data manifold. The flattening of the data manifold is achieved as an emergent behavior under the virtual elastic and repulsive interaction between the data points. The manifold’s topological structure is preserved when it evolves to the shape of lower dimension. The soft neighborhood is proposed to overcome the uneven sampling and neighbor point misjudging problems. The simulation experiment results of data sets prove its effectiveness and also indicate that implicit features of data sets can be revealed. In the comparison experiments, the proposed method shows its advantage in robustness.
Enhancing Self-Learning in Higher Education with Virtual and Augmented Reality Role Games: Students’ Perceptions
Authors: Valladares Ríos Luis , Acosta-Diaz Ricardo , Santana-Mancilla Pedro C.
Published: October 2023

This study investigates how virtual and augmented reality role games impact self-learning in higher education settings. A qualitative research–action approach that involved creating augmented reality micro-stories to encourage creativity and critical thinking was used. Through role-playing, students collaborated and gained a deeper understanding of the course, improving their self-learning abilities. The findings indicate that incorporating virtual and augmented reality into higher education positively affects self-learning, promoting active student engagement and meaningful learning experiences. Additionally, students perceive these immersive educational methods as bridging the gap between virtual and in-person learning environments, ultimately leading to enhanced educational results.