bluuebunny/crossref_metadata_2025 huggingface dataset:
Revisiting the Numerical Evaluation and Visualization of the Gravity Fields of Asteroid 4769 Castalia Using Polyhedron and Harmonic Expansions Models
Authors: Hu Weiduo , Fu Tao , Liu Chang
Published: May 2024

For the convenience of comparison with previous literature, the gravity calculations are revisited for the Asteroid 4769 Castalia, but with extensions on its surface and on intersecting planes and spheres around it, using the polyhedron and harmonic expansion methods with different order and degree for different cases, especially including the gravitational accelerations inside the asteroid, which did not appear at all before. In these evaluations, a few different facts of the these methods and results are revealed, such as the fact that gravity diverges when the position radius is less than the mean radius from harmonic-expansion method, and the maximum gravity is not at the deep valley and mountain top. For a surface that intersects the asteroid, the maximum gravity on it is at the intersection lines between the asteroid surface and the spheres or planes. This means that on the sphere and the plane, the gravities inside and outside the asteroid are smaller than the gravity on the intersection, i.e., on the surface. Some analyses of these conclusions are given with many examples with different radii of the sphere and with different order and degree harmonic expansion models for the above asteroid surface and surrounding spheres. It is interesting to note that very few researchers know that the polyhedral method can also be used to calculate the gravity inside an asteroid with just some modifications of the code. Some special gravity figures on surface and planes inside the asteroid Castalia are computed and made for the first time. The calculations also include tangential gravity, potential, and gravitational slope on surface. Specifically, we find that the overall mean gravitational slope could be one kind of indicator of the density of an asteroid. The minimum overall mean slope happens when the asteroid density is about 2.9 g/cm3, which is much larger than a usually assumed value between 1.7 and 2.5 for asteroid Castalia when its period is 4.07 h, since rotation period should be a more accurate parameter than its estimated density. These conclusions about this typical prolate-like asteroid could be a benchmark for analyzing other similar asteroids.
Assessing the Opportunity Offered by Electric Vehicles in Performing Service Trips to End Consumers
Authors: Comi Antonio , Polimeni Antonio , Belcore Orlando M. , Cartisano Antonio G. , Micari Salvatore , Napoli Giuseppe
Published: May 2024

This paper proposes the assessment of the impacts of using electric vehicles for urban service trips. In particular, the focus is on trips performed for delivering and installing products, as well as for reverse logistics. Such components of commercial traffic in urban areas have not received the level of attention it deserves. In fact, recent research on commercial traffic mainly deals with shop restocking, service visits to establishments and e-commerce deliveries, and limited attention has been paid to the service sector (e.g., installation, maintenance, repairs) which can have a high impact on city sustainability in terms of pollution emissions, congestion as well as land use for parking. Furthermore, pushed by the current trend towards the promotion of electric vehicles, an assessment is developed comparing potential service patterns using real data from the inner area of Rome (Italy) when the electric fleet replaces the traditional one. Results show the opportunity to decouple the delivery operation from the installation one, and to integrate service with reverse logistics. These significant results could address the adoption of suitable integrated urban policies to make the most of the opportunities arising from the use of electric vehicles.
A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
Authors: Van Trinh Thai , Trinh-Van Son , Lee Kang-Yoon , Yang Younggoo , Hwang Keum Cheol
Published: May 2024

This paper presents the design of a two-port dual-band dual-circularly-polarized dielectric resonator antenna (DRA). The proposed DRA is formed by stacking two dielectric resonators (DRs) of different shapes, including a hexagonal DR on top and a cross-shaped DR on the bottom. It is designed to resonate at two near-degenerate orthogonal modes of TE111 and TE113, and an aperture-coupled feeding through a cross-like slot is used to achieve dual-band impedance matching simultaneously for right- and left-handed circular polarizations. Tests were conducted on a prototype working in C-band to verify the design concept. The experiment results demonstrate that the proposed DRA has exceptional performance with measured −10 dB reflection bandwidths of 24.4% and 17.4%, 3 dB axial ratio bandwidths of 21.2% and 16.3%, and maximum gains of 5.64 and 8.13 dBic for the lower and upper bands, respectively. Moreover, the measured channel isolation is more than 15.8 dB. The results obtained from the experiments show good agreement with the simulation, and hence, it can be concluded that the proposed DRA is a promising solution that can be used for various wireless communication applications.
BMFA-Net: Boundary Constraint Multi-Level Feature Aggregation Framework for Precise Polyp Segmentation
Authors: Li Qin , Zhang Tianchi , Mosharaf Parvej Md , Zhang Jing
Published: May 2024

Colorectal cancer is one of the three most common cancers worldwide. Polyps are complex and have unclear boundaries, often leading to inaccurate boundary segmentation and missed detections. To address these challenges, we propose a boundary constraint multi-level feature aggregation framework called BMFA-Net to precisely segment polyps. The framework comprises four key modules. First, the parallel partial decoder is introduced to aggregate high-level features within the network to generate a globally informative semantic map serving as the initial guidance region for reverse erasing the foreground. Second, we propose an efficient atrous convolution attention module to effectively aggregate local and global contextual information over multiple levels. Additionally, a multi-level feature aggregation mechanism is designed and placed among the efficient atrous convolution attention modules to enable the network to capture a large amount of semantic structure while preserving intricate details. Finally, a boundary constraint reverse attention module is proposed to perform the boundary constraint while removing the foreground to improve the quality of boundary segmentation. Extensive experiments demonstrated the superiority and versatility of our framework compared with state-of-the-art methods; specifically, it achieved a mean Dice score of 0.922 on the CVC-300 dataset.
Analyzing the Impact of Geometrophysical Modeling on Highway Design Speeds: A Comparative Study for Mexico’s Case
Authors: Anaya Rivera Ely , Isaza Cesar , Ramirez-Gutierrez Cristian Felipe , Zavala-De Paz J. P. , Ibarra Tapia Pamela Rocío , Rizzo-Sierra Jose Amilcar
Published: May 2024

This manuscript presents an examination of the impact of geometrical and physical parameters on highway design speeds, critical for traffic safety and efficiency. Originating from a classical dynamics discussion in an undergraduate automotive technology engineering class, an exploration of the consequences of different geometrophysical considerations on a vehicle’s dynamics over pavement surfaces is developed. Considering various analytical models, an assessment of their principles and the significance of geometric and physical concepts involved in the problem is made, such as plane of motion and trajectory curvature radius, on safe (non slippage) operational speeds. The subsequent comparative study shows that one of the most accepted models in highway design regulations in México, when used as reference, yields percentage error differences respect to others of 0≲%EMax≲5, as well as a consistent trend for relatively underestimating safe highway operational speeds. A discussion of the immediate implications of these findings, emphasizing the necessity of experimental studies to validate theoretical predictions, is presented. This work contributes to the field by providing a detailed comparison of analytical models under a general applied science perspective, suggesting modifications to current highway design practices in México based on geometrophysical insights. In summary, this work’s main aim is to shed light on the intricacies of determining safe design speeds from an applied sciences point of view, while also calling for a reevaluation of the existing guidelines to enhance highway design and safety.
Feature Maps Need More Attention: A Spatial-Channel Mutual Attention-Guided Transformer Network for Face Super-Resolution
Authors: Zhang Zhe , Qi Chun
Published: May 2024

Recently, transformer-based face super-resolution (FSR) approaches have achieved promising success in restoring degraded facial details due to their high capability for capturing both local and global dependencies. However, while existing methods focus on introducing sophisticated structures, they neglect the potential feature map information, limiting FSR performance. To circumvent this problem, we carefully design a pair of guiding blocks to dig for possible feature map information to enhance features before feeding them to transformer blocks. Relying on the guiding blocks, we propose a spatial-channel mutual attention-guided transformer network for FSR, for which the backbone architecture is a multi-scale connected encoder–decoder. Specifically, we devise a novel Spatial-Channel Mutual Attention-guided Transformer Module (SCATM), which is composed of a Spatial-Channel Mutual Attention Guiding Block (SCAGB) and a Channel-wise Multi-head Transformer Block (CMTB). SCATM on the top layer (SCATM-T) aims to promote both local facial details and global facial structures, while SCATM on the bottom layer (SCATM-B) seeks to optimize the encoded features. Considering that different scale features are complementary, we further develop a Multi-scale Feature Fusion Module (MFFM), which fuses features from different scales for better restoration performance. Quantitative and qualitative experimental results on various datasets indicate that the proposed method outperforms other state-of-the-art FSR methods.
Antifungal Effects of Fermented Sophora flavescens and Eleutherococcus sessiliflorus Extract
Authors: Kim Ju Yeon , Chae Min Joo , Son Yun Gon , Jo Su Min , Kang Na Rae , Kang Seong Doo , Kim Kwang Dong , Lee Sang Won , Kim Jeong Yoon
Published: May 2024

In this study, a microbial strain was isolated from humus soil to ferment Sophora flavescens and Eleutherococcus sessiliflorus extracts. The isolated microbial was identified as the Bacillus genus by 16S rRNA sequence analysis. The fermented plant extracts exhibited antifungal effects against four types plant pathogen, P. carotorum, B. cinerea, C. fructicola Sau-3, and C. gloeosporioides, according to incubation time. In particular, the fermented plant extracts showed the most activity for Colletotrichum genus in inhibiting mycelium growth. Metabolite changes in fermented S. flavescens and E. sessiliflorus extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented S. flavescens and E. sessiliflorus extracts compared to their unfermented counterparts. This study suggested that isolated Bacillus microbial fermentation could be a valuable tool in improving the bioactivity of S. flavescens and E. sessiliflorus extracts, with the potential to form more environmentally friendly antifungal agents.
Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
Authors: Jin Shousong , Yu Fengyi , Wang Boyu , Zhang Min , Wang Yaliang
Published: May 2024

Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human–computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human–machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design.
Research on Seismic Performance of a Two-Story, Two-Span Underground Subway Station with Split Columns Based on the Quasi-Static Method
Authors: Xu Zigang , Xia Zongyao , Bu Xiangbo , Han Runbo
Published: May 2024

The behavior of center columns in shallow-buried underground subway station structures resembles that of high-rise buildings. In both cases, these columns experience significant vertical loads during earthquake events and are susceptible to brittle failure due to inadequate deformation capacity. In this study, the design concept of split columns, commonly employed in high-rise structures, is adapted for application in a two-story, two-span subway station. Initially, a comparative analysis was conducted using quasi-static pushover analysis to assess the horizontal deformation characteristics of traditional and split columns under high axial loads. Subsequently, a comprehensive quasi-static pushover analysis model encompassing the soil–structure interaction was formulated. This model was employed to investigate differences in seismic performance between traditional and innovative underground structures, considering internal forces, deformation capacity, and plastic damage of crucial elements. The analysis results demonstrate that the incorporation of split columns in a two-story, two-span subway station enhances the overall seismic performance of the structure. This enhancement arises from the fact that split columns mitigate excessive shear forces while effectively utilizing their vertical support and horizontal deformation capacities.
Spatial Image-Based Walkability Evaluation Using Regression Model
Authors: Hwang Jiyeon , Nam Kwangwoo , Lee Changwoo
Published: May 2024

Governments worldwide have invested considerable money and time into creating pedestrian-oriented urban environments. However, generalizing arbitrary standards for walking environments is challenging. Therefore, this study presents a method for predicting walkability scores of evaluations using five regression models, including Multiple linear, Ridge, LASSO regression, SVR, and XGBoost. The models were trained using semantic segmentation, walkability evaluations based on crowdsourcing, and image scores obtained using the TrueSkill algorithm, and their performances were compared. Feature selection was employed to improve the accuracies of the models, which were retrained using the importance of extracted features. Among the five regression models, XGBoost, a tree-based regression model, exhibited the lowest error rate, high accuracy, and greatest performance improvement after retraining. This study is expected to generalize the walking environments preferred by various people and demonstrate that objective walkability evaluations are possible through a computer system rather than through subjective human judgment.
Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm
Authors: Chen Fengyu , Xie Wei , Ma Jiachen , Chen Jun , Wang Xiaoli
Published: May 2024

To improve the workshop production efficiency of textile enterprises and balance the total operating time of all machines in each operation, this paper proposes a modified algorithm based on the combination of the ant colony optimization (ACO) algorithm and production products, which we call the product ant colony optimization (PACO) algorithm. The local pheromone update rule in the ACO algorithm is modified through the close relationship between textile machinery and production products in the textile workshop; the pheromone is then introduced into production products based on the constraints of the textile machine. A heuristic function is designed to improve the utilization rate of textile machines to increase the heuristic value of machines that are less frequently used in the algorithm iteration process. In addition, this paper combines the convergence speed and the global search ability of the algorithm with a designed variable pheromone evaporate parameter. The comparison among the initially designed PACO algorithm, the separately modified PACO algorithm, and the integratively modified PACO algorithm demonstrates that the proposed enhancement effectively addresses scheduling issues in textile flexible workshops and various workshops with similar constraint conditions.
Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis
Authors: Kim Jinah , Moon Nammee
Published: May 2024

Heterogeneous data have been used to enhance behavior prediction performance; however, it involves issues such as missing data, which need to be addressed. This paper proposes enhanced pet behavior prediction via Sensor to Skeleton Generative Adversarial Networks (S2GAN)-based heterogeneous data synthesis. The S2GAN model synthesizes the key features of video skeletons based on collected nine-axis sensor data and replaces missing data, thereby enhancing the accuracy of behavior prediction. In this study, data collected from 10 pets in a real-life-like environment were used to conduct recognition experiments on 9 commonly occurring types of indoor behavior. Experimental results confirmed that the proposed S2GAN-based synthesis method effectively resolves possible missing data issues in real environments and significantly improves the performance of the pet behavior prediction model. Additionally, by utilizing data collected under conditions similar to the real environment, the method enables more accurate and reliable behavior prediction. This research demonstrates the importance and utility of synthesizing heterogeneous data in behavior prediction, laying the groundwork for applications in various fields such as abnormal behavior detection and monitoring.
Sexual Maturation of Fan Mussel Pinna nobilis (Linnaeus, 1758) (Mollusca: Bivalvia) in Experimental Cages in the Mali Ston Bay (South Adriatic Sea)
Authors: Kožul Valter , Bolotin Jakša , Glavić Nikša , Antolović Nenad
Published: May 2024

The research included 120 specimens of Pinna nobilis cultivated at a commercial park for oyster (Ostrea edulis) and black mussel (Mytilus galloprovincialis) cultivation. Research was carried out from 2012 to 2016, prior to the start of the mass extinction of fan mussel in the Mediterranean During the study period, the average sea temperature at a depth of 3 m was 14.87 ± 4.22 °C, ranging from 7.83 to 24.90 °C. The age of the specimens at the beginning of sexual maturity was approximately three years. The average length of the specimens throughout the study was 293.01 ± 16.58 mm. Gonad status was monitored monthly by sampling. The gender ratio in the study was 46 females (38.4%), 50 males (41.6%), and 24 undetermined individuals (20%). In our study, the results showed that males mature slightly earlier than females. The main spawning season occurred during the warmer part of the year when seawater exceeded 18 °C, starting in May and lasting until November. During the study, the GSI varied as expected and peaked in June (12.1), increasing from May to September.
Artificial Intelligence in Planning Oral Rehabilitations: Current Status
Authors: Cortes Arthur Rodriguez Gonzalez
Published: May 2024

Diagnostic digital 3D images, such as cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI), have been widely evaluated quantitatively and qualitatively regarding their diagnostic performance for identify various alterations [...]
A Novel, Soft, Cable-Driven Parallel Robot for Minimally Invasive Surgeries Based on Folded Pouch Actuators
Authors: Yang Jianlin , Li Xinxin , Runciman Mark , Avery James , Zhou Zhangxi , Sun Zhijun , Mylonas George
Published: May 2024

This paper introduces a soft, cable-driven parallel robot for minimally invasive surgeries. The robot comprises a pneumatic inflatable scaffold, six hydraulic, folded pouch actuators, and a hollow, cylindrical end-effector offering five degrees of freedom. A key development is the design of the pouch actuators, which are small, low-profile, simple structures, capable of a high stroke of 180° angular displacement. The scaffold, actuators, and plastic cables are economically and rapidly fabricated using laser cutting and welding techniques. Constructed primarily from soft plastic materials, the robot can be compactly folded into a cylinder measuring 110 mm in length and 14 mm in diameter. Upon inflation, the scaffold transforms into a hexagonal prism structure with side lengths of 34 mm and edge lengths of 100 mm. The kinematic model of the robot has been developed for workspace calculation and control purposes. A series of tests have been conducted to evaluate the performance of the actuator and the robot. Repeatability tests demonstrate the robot’s high repeatability, with mean and root mean square errors of 0.3645 mm and 0.4186 mm, respectively. The direct connection between the end-effector and the actuators theoretically eliminates cable friction, resulting in a hysteresis angle of less than 2°, as confirmed by the tracking results. In addition, simulated surgical tasks have been performed to further demonstrate the robot’s performance.
Assessing the Seasonal Water Requirement of Fully Mature Japanese Plum Orchards: A Systematic Review
Authors: Mashabatu Munashe , Motsei Nonofo , Jovanović Nebojša , Dube Timothy , Mathews Ubaidullah , Nqumkana Yolanda
Published: May 2024

Japanese plums have relatively high water requirements, which depend on supplementing rainfall volumes with accurately quantified irrigation water. There is a lack of knowledge on the seasonal water requirements of plum orchards. This gap in the literature poses an imminent threat to the long-term sustainability of the South African plum industry, which is particularly plagued by climate change and diminishing water resources. The systematic literature review conducted in this study aimed to provide a foundation for supporting water management in irrigated Japanese plum [Prunus salicina Lindl.] orchards. Seventeen peer-reviewed articles obtained from the literature were analyzed. Approximately 66% of the cultivars were cultivated under different regulated deficit irrigation regimes for water-saving purposes and to increase fruit quality. This review of our knowledge provided benchmark figures on the annual water requirements of Japanese plums. The full-year plum crop water requirements obtained from the literature ranged between 921 and 1211 mm a−1. Canopy growth, pruning and growing season length were the most common causes of differences in the water requirement estimates. Further research is required to measure the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, especially in the South African context.
Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
Authors: Zhou Hongmei , Zhang Yixiang , Zhu Wanxu , Zhong Qingyu , Huang Xinyu
Published: May 2024

During shield construction in underground spaces, synchronous grouting slurry is poured between the surrounding rock and tunnel lining to ensure stability. For synchronous grouting slurries, few studies have investigated the relationship between the rheological parameters and physical properties, grout-segregation mechanism, and anti-segregation performance. Therefore, we explored the relationships between the slurry rheological parameters, segregation rate, and bleeding rate. Cement, sand, fly ash, and bentonite were used to prepare the slurry, and the effects of different polycarboxylate water-reducing agents and dispersible latex powder dosages were studied. The rheological parameters of 16 groups of uniformly designed slurries were tested, and the data were fit using the Herschel–Bulkley model. The optimal mix ratio lowered the slurry segregation rate, and its rheological behaviour was consistent with the Herschel–Bulkley fluid characteristics. High-yield-shear-stress synchronous grouting slurries with high and low viscosity coefficients were less likely to bleed and segregate, respectively. The optimised slurry fluidity, 3 h bleeding rate, 24 h bleeding rate, segregation rate, coagulation time, and 28 days compressive strength were 257.5 mm, 0.71%, 0.36%, 3.1%, 6.7 h, and 2.61 MPa, respectively, which meet the requirements of a synchronous grouting slurry of shield tunnels for sufficiently preventing soil disturbance and deformation in areas surrounding underground construction sites.
Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
Authors: Zhang Ran , Chen Xiaohui , Ye Lin , Yu Wentao , Zhang Bing , Liu Junnan
Published: May 2024

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.
Enhancing Furniture Manufacturing with 3D Scanning
Authors: Muminović Adis J. , Gierz Łukasz , Rebihić Hasan , Smajić Jasmin , Pervan Nedim , Hadžiabdić Vahidin , Trobradović Mirsad , Warguła Łukasz , Wieczorek Bartosz , Łykowski Wiktor , Sydor Maciej
Published: May 2024

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering).
The Impact of Large Language Models on Programming Education and Student Learning Outcomes
Authors: Jošt Gregor , Taneski Viktor , Karakatič Sašo
Published: May 2024

Recent advancements in Large Language Models (LLMs) like ChatGPT and Copilot have led to their integration into various educational domains, including software development education. Regular use of LLMs in the learning process is still not well-researched; thus, this paper intends to fill this gap. The paper explores the nuanced impact of informal LLM usage on undergraduate students’ learning outcomes in software development education, focusing on React applications. We carefully designed an experiment involving thirty-two participants over ten weeks where we examined unrestricted but not specifically encouraged LLM use and their correlation with student performance. Our results reveal a significant negative correlation between increased LLM reliance for critical thinking-intensive tasks such as code generation and debugging and lower final grades. Furthermore, a downward trend in final grades is observed with increased average LLM use across all tasks. However, the correlation between the use of LLMs for seeking additional explanations and final grades was not as strong, indicating that LLMs may serve better as a supplementary learning tool. These findings highlight the importance of balancing LLM integration with the cultivation of independent problem-solving skills in programming education.
Wintertime Diurnal Variation in Absorption Coefficient of Brown Carbon Associated with the Molecular Marker of Levoglucosan
Authors: Yu Geun-Hye , Song Myoungki , Oh Sea-Ho , Choe Seoyeong , Jeon Hajeong , Ko Dong-Hoon , Bae Min-Suk
Published: May 2024

This study investigated the aerosol particle properties and light absorption properties of brown carbon (BrC) by utilizing a seven-wavelength aethalometer, and analyzed NH4+, NO3−, SO42−, K+, K, organic carbon, elemental carbon, levoglucosan, and mannosan in PM2.5. The research was conducted in a rural area of Jeonnam, South Korea, during the winter season. In addition, the dithiothreitol assay-oxidative potential normalized to 9,10-phenanthrenequinone (QDTT-OP) was investigated throughout the study period. The absorption coefficient was found to be 2.6 to 5.6 times higher at 370 nm compared to 880 nm, suggesting the presence of light-absorbing substances in addition to black carbon (BC) particles. The estimated absorption coefficient of BrC370 was 29.9% of the total light absorption coefficient at 370 nm. Furthermore, BrC370 exhibited a strong affinity with levoglucosan while showing a weak correlation with K+, confirming the suitability of levoglucosan as a tracer for biomass burning. The QDTT-OP was 5.3 nM m−3, and highly correlated with the carbonaceous components levoglucosan and mannosan, suggesting a relatively high contribution of biomass combustion emissions to oxidative potential. Further research should be conducted to assess the health risks associated with future PM2.5 exposure related to biomass burning in the atmosphere.
Towards a Cognition-Based Framework Describing Interdisciplinary Expert Team Processes for Cognitive Robotics in Industry 5.0 Technologies
Authors: Morgenstern Tina , Klichowicz Anja , Bengler Philip , Todtermuschke Marcel , Bocklisch Franziska
Published: May 2024

With the evolution of traditional production towards smart manufacturing, humans and machines interact dynamically to handle complex production systems in semi-automated environments when full automation is not possible. To avoid undesirable side effects, and to exploit the full performance potential of experts, it is crucial to consider the human perspective when developing new technologies. Specifically, human sub-tasks during machine operation must be described to gain insights into cognitive processes. This research proposes a cognition-based framework by integrating a number of known psychological concepts. The focus is on the description of cognitive (team) processes in the resolution of anomalies within a manufacturing process with interdisciplinary experts working together. An observational eye tracking study with retrospective think-aloud interviews (N = 3) provides empirical evidence for all cognitive processes proposed in the framework, such as regular process monitoring and—in case of a detected anomaly—diagnosis, problem solving, and resolution. Moreover, the role of situation awareness, individual expertise and (cognitive) team processes is analyzed and described. Further, implications regarding a human-centered development of future production systems are discussed. The present research provides a starting point for understanding and supporting cognitive (team) processes during intelligent manufacturing that will dominate the production landscape within Industry 5.0.
An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes
Authors: Caiza Gustavo , Sanz Ricardo
Published: May 2024

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.
Effect of Nanohydroxyapatite on Silk Fibroin–Chitosan Interactions—Molecular Dynamics Study
Authors: Przybyłek Maciej , Tuwalska Anna , Ledziński Damian , Śmigiel Sandra , Sionkowska Alina , Białas Iwona , Bełdowski Piotr
Published: May 2024

Fibroin–chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca2+) and hydrogen phosphate (HPO₄2−) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions, including hydrogen bonds, ionic contacts, and hydrophobic contacts, along with structural and energetical features, were examined. The calculated binding energy values for the fibroin–chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between the considered biopolymers. These interactions were found to primarily be hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of a significant number of hydrogen bonds and ionic contacts with chitosan.
The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis
Authors: Makkaoui Mohamed , Azzouz Omar , Tendero-Salmeron Víctor , Belhadj Kamal , Galindo-Zaldivar Jesus
Published: May 2024

The Rif Cordillera, an Alpine orogen in the Western Mediterranean, was developed by the interaction of Eurasian and African (Nubia) plates. Neotectonic deformations of the Rif foreland influence the relief, especially in post-nappe basins and their boundaries with Jurassic and Cretaceous carbonate mountain massifs, and they contribute to highlighting the recent evolution of the Cordillera. The topographic and hydrological lineaments of these basins were characterised on the basis of multi-scale morphostructural data analysis, supported by digital mapping and GIS. They were correlated with geological structures, essentially with fractures. The outcrops of the Upper Tortonian and Messinian deposits depict well-defined geometric shapes with roughly rectilinear boundaries, as defined by their contacts with the massive and rigid rocks of the Jurassic and Cretaceous series. Upper Tortonian deposits evidence major regional N70°E and N40°E lineaments, which are obliquely intersected by late structures. Messinian N120°E and N25°E lineaments, associated with N140°E lineaments, are also recognised. The interpretation of these lineaments as faults indicates the activity of two systems of transtensive sinistral and then dextral brittle shearing that correspond to two episodes of neotectonic deformation that played a decisive role in shaping the reliefs of the Eastern Rif. These deformations are particularly relevant to isolate basins and likely have a key role during the closure of the South Rifian corridor during the Mediterranean Messinian Salinity crisis.
Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
Authors: Zhang Hao , Huang Dawei , Wang Kaifu
Published: May 2024

Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details.
Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population
Authors: Stan Emanuela , Muresan Camelia-Oana , Dumache Raluca , Ciocan Veronica , Ungureanu Stefania , Costachescu Dan , Enache Alexandra
Published: May 2024

This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, only one misclassification occurred. Regarding accuracy, the overall rate remained notably high at 98.24%. All female specimens that could be estimated were correctly classified (100% accuracy), while for males, the accuracy rate was 95.65%. Undetermined cases were noted to potentially impact the accuracy of sex classification, underscoring the critical role of precision in forensic contexts. In conclusion, the study highlights the importance of accuracy in forensic sex estimation. It emphasizes the confidence with which DSP software can be utilized, if not the only method, at least as a preliminary or adjuvantly accurate technique for sex estimation in forensic anthropology.
A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection
Authors: Papavasileiou Apostolis , Nikoladakis Stelios , Basamakis Fotios Panagiotis , Aivaliotis Sotiris , Michalos George , Makris Sotiris
Published: May 2024

Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.
Bayes-Optimized Adaptive Growing Neural Gas Method for Online Anomaly Detection of Industrial Streaming Data
Authors: Zhang Jian , Guo Lili , Gao Song , Li Mingwei , Hao Chuanzhu , Li Xuzhi , Song Lei
Published: May 2024

Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall short of such challenges, and these models may be disabled, suffering from the evolving data distribution. The paper presents a framework for online anomaly detection of data streams, of which the baseline algorithm is the incremental learning method of Growing Neural Gas (GNG). It handles complex and evolving data streams via the proposed model Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG). Firstly, novel learning rate adjustment and neuron addition strategies are designed to enhance the model convergence and data presentation capability. Then, the Bayesian algorithm is adopted to realize the fine-grained search of BOA-GNG-based hyperparameters. Finally, comprehensive studies with six data sets verify the superiority of BOA-GNG in terms of detection accuracy and computational efficiency.
Accurate Classification of Tunnel Lining Cracks Using Lightweight ShuffleNetV2-1.0-SE Model with DCGAN-Based Data Augmentation and Transfer Learning
Authors: Zhao Ningyu , Song Yi , Yang Ailin , Lv Kangping , Jiang Haifei , Dong Chao
Published: May 2024

Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack of sufficient crack images, which limits the potential detection performance. In this paper, transfer learning was used to optimize deep convolutional generative adversarial networks (DCGANs) for crack image synthesis to significantly improve the accuracy of LCNNs. In addition, an improved LCNN model named ShuffleNetV2-1.0-SE was proposed, incorporating the squeeze–excitation (SE) attention mechanism into ShuffleNetV2-1.0 and realizing highly accurate classification results while maintaining lightness. The results show that the DCGAN-based data enhancement method can significantly improve the classification accuracy of ShuffleNetV2-1.0-SE for tunnel lining cracks. ShuffleNetV2-1.0-SE achieves an accuracy of 98.14% on the enhanced dataset, which is superior to multiple advanced LCNN models.