bluuebunny/crossref_metadata_2025 huggingface dataset:
A Contribution to a Better Understanding of the Nitella axillaris Group (Charales, Charophyceae): A Taxonomic Re-Examination of the Nitella translucens Collected in the Province of Ferrara, Italy
Authors: Abdelahad Nadia , Troia Angelo , van de Weyer Klaus , Iberite Mauro , Piccoli Filippo , Casanova Michelle
Published: November 2024

The identification of a charophyte population found in a rice field in Ferrara, North Italy, collected in 1999 and initially identified as Nitella translucens, has been reevaluated. Detailed morphological observations here reported have suggested that the specimen displays characteristics more akin to Nitella axillaris, particularly in the structure of its bicellular dactyls and axillary heads. Additional examinations, including scanning electron microscopy (SEM) of oospores and analyses of herbarium specimens—encompassing original materials of N. axillaris, N. axillaris f. tenuoir, and N. translucens f. confervoides—support this reclassification. Notably, the Ferrara specimens exhibit reticulate oospores and specific morphological traits that align well with N. axillaris. However, a syntype of N. axillaris housed at the BM Herbarium presents an anomaly, as it features granulate rather than the expected reticulate oospores. This discrepancy indicates a need for further studies, given that the lectotype of N. axillaris lacks oospores altogether. For now, the charophyte population from Ferrara can be provisionally assigned to N. axillaris “sensu Auctores”. Additionally, it appears to be an alien species introduced into the rice fields of Northern Italy, highlighting the need for further investigation into its taxonomy and distribution.
Secretory Proteins Are Involved in the Parasitism of Melon by Phelipanche aegyptiaca During the Attachment Stage
Authors: Xiao Lifeng , Zhao Qiuyue , Cao Xiaolei , Yao Zhaoqun , Zhao Sifeng
Published: November 2024

Parasitic plants represent a significant challenge in global agriculture, with Broomrape (Orobanche/Phelipanche spp.) being a notable example of a holoparasitic species that targets the roots of host plants. This study employed comparative transcriptomics to investigate the mechanisms underlying the parasitism of P. aegyptiaca on melon, focusing on both resistant and susceptible interactions. The findings indicate that the critical phase of P. aegyptiaca parasitism occurs during the post-attachment stage. It is suggested that peptidases may play a role in the development of invasive cells, while cell wall-degrading enzymes (CWDEs) are likely involved in cell wall modification and degradation, and transferases, elicitors, and effectors may play a role in immune regulation. In this study, 25 tobacco rattle virus (TRV) recombinant vectors were successfully constructed and functionally validated using a host-induced gene silencing assay to explore the functions of candidate-secreted effector proteins. The results revealed that silencing Cluster-107894.0, Cluster-11592.0, and Cluster-12482.0 significantly decreased the parasitism rate of P. aegyptiaca on Nicotiana benthamiana. Notably, Cluster-107849.0 encodes a cellulase with hydrolase activity, Cluster-11592.0 encodes a periodic-dependent kinase inhibitor with phosphoprotein activity, and Cluster-12482.0 encodes a glucan 1,3-β-glucosidase with hydrolase activity. These findings potentially offer a novel theoretical framework and justification for understanding host–parasite plant interactions, and suggest new avenues for developing crop varieties resistant to parasitic infestation.
Vinyl Chloride Distillation Process Simulation Optimization Evaluation: Optimization Based on NSGA-III Algorithm and Quantitative Risk Analysis
Authors: He Miao , Chen Fei , Wen Pengtao , Jin Yulong , Zhao Jun , Zhang Li , Gao Jinhao , Lu Xilei , Wan Lining
Published: November 2024

In response to the current issues of high energy consumption, environmental pollution, and safety hazards associated with the vinyl chloride distillation process, this study has developed a sustainable, economically energy-efficient, and safe multi-objective optimization method for the vinyl chloride distillation process. Based on the actual operation of the vinyl chloride distillation process in enterprises, this research employs the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize key parameters of the distillation operation, aiming to achieve multiple objectives such as improving product quality, reducing energy consumption, decreasing CO2 emissions, and enhancing process safety. The safety performance of the optimized scheme was comprehensively evaluated through simulation with Aspen Plus V14 software, combined with Hazard and Operability (HAZOP) qualitative risk analysis and quantitative risk analysis based on Aspen Plus. Through comparative analysis with the original design scheme, the following conclusions were drawn: all optimization plans (A, B, C, D) are superior to the original design to varying degrees. Further research revealed that as the number of iterations of the genetic algorithm increases, the optimization plans have significantly improved in terms of multi-objective performance, highlighting the importance of adequate iteration in the process of finding the optimal solution. The outcomes of this study not only provide an effective strategy for the optimization of the vinyl chloride distillation process but also offer a theoretical basis and practical guidance for the green development and safe production in the chemical industry.
Mathematical Modeling of the Transport of H2O–CH4 Steam–Gas Mixtures in Hydrodynamic Devices: The Role of Helical Screws
Authors: Mamytbekov Galymzhan , Shayakhmetov Nurlan , Aizhulov Daniar , Kurmanseiit Maksat , Tungatarova Madina , Zhakanbayev Yeldar , Danko Igor , Rakhimbayev Aisultan
Published: November 2024

The pressing issue of global warming, coupled with the increasing depletion of fossil fuels, highlights the necessity for sustainable energy solutions. In this context, hydrogen stands out as a viable option, possessing the capacity to revolutionize critical industries, including fuel cells, internal combustion engines, and gas turbines. An effective approach to enhancing numerous chemical and technological processes in liquid and steam–gas mixtures is the establishment of cavitation mixing zones for the reacting components. These zones are produced in specialized reactors that operate on the principles of hydrodynamic effects applied to the reaction medium. The study focused on the design of the cavitation-jet chamber utilizing the k–ω Turbulence Model and Particle Tracing Model. As a result, the influence of the inlet velocity on cavitation formation and the uniformity of mixing was investigated. Ripley’s K-function was used to analyze the results of particle distribution. The influence of the screw on flow turbulence and the uniformity of particles was evaluated. Analysis through the K-function indicated a decrease in uniformity at lower velocities, with noticeable turbulization of the flow occurring at high velocities, which facilitated better mixing. In contrast, without the screw, the flow exhibited a high longitudinal velocity and minimal transverse velocity, limiting particle dispersion to the radius of the nozzle and resulting in inefficient mixing. It was found that the inclusion of the screw not only enhanced particle distribution but also maintained the size of the cavitation zones, thereby improving the overall efficiency of the design.
Biochar as an Enzyme Immobilization Support and Its Application for Dye Degradation
Authors: Antanasković Anja , Lopičić Zorica , Dimitrijević-Branković Suzana , Ilić Nevena , Adamović Vladimir , Šoštarić Tatjana , Milivojević Milan
Published: November 2024

Wastewaters generated by the textile industry often contain significant amounts of harmful (carcinogenic and mutagenic) cationic dyes, whose efficient removal is of crucial importance. This study investigates the laccase immobilization on biochar obtained from sour cherry stones (SCS-B), as a cost effective adsorbent, and evaluates its application for brilliant green (BG) degradation. The successful immobilization of laccase on biochar was achieved via adsorption and confirmed using scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and Fourier transform infrared spectroscopy (FTIR). An immobilization efficiency of 66% was achieved using 0.274 U/mL of laccase at pH 5 and a temperature of 40 °C. The adsorption kinetics of laccase followed a pseudo-second-order model, indicating that chemical adsorption plays a significant role in the immobilization process. The BG degradation by immobilized system was further optimized by evaluating effects of pH, temperature, dye concentration, and contact time. More than 92% of BG (50 mg/L) was removed within 4 h at pH 5 and temperature of 30 °C. These findings suggest that SCS-B can effectively be used as an enzyme carrier and be further utilized for the removal of emerging pollutants, positioning it as a sustainable solution for wastewater treatment.
Sustainable Resource Management: The End of Nickel Mining?
Authors: Nell (née Campbell) Kristy , Valenta Richard K. , Forbes Gordon , Yahyaei Mohsen , Ilyas Hafiz M. A.
Published: November 2024

As a versatile metal, nickel will experience increased demand in the coming years, with a specific focus on its importance in the battery industry and its role in achieving net-zero emissions. Recognizing the need to ensure sustainable resource management, this study analyses the flow dynamics of nickel’s supply and demand by employing a modelling approach. This is done with a focus on understanding how recycling can contribute to meeting the growing nickel demand. By considering the interaction between mining production, product applications, and recycling rates, this study contributes to a better understanding of the long-term prospects for meeting the nickel demand. It can assist policymakers, industry stakeholders, and investors in making informed decisions regarding resource management and developing sustainable practices in the nickel industry. The results revealed that mining would still play an important role in the supply of nickel for at least the next 40 years. Nickel mining and recycling practices are sufficient to meet future nickel demand if sufficient recycling practices are (rapidly) implemented. Modelling results show that nickel recycling will account for 90% of the total demand (primary nickel mining accounts for the remaining 10%) between the years 2062 and 2096.
Temple-Monasteries, Buddhist Monks, and Architectural Exchange Between India, Java, and Tibet in the Late 8th Century
Authors: Copplestone Louis
Published: November 2024

The once-dominant view that architectural developments in mediaeval Southeast Asia closely followed Indian ‘influence’ is now largely rejected. Recent scholarship has shifted its focus onto the agency of local artists and architects in driving architectural innovations across the region. However, specific cases of transregional exchanges in architectural ideas and practices remain underexplored. This study examines three geographically distant Buddhist sites—Paharpur in northern Bangladesh, Candi Sewu in Central Java, Indonesia, and Samye Monastery in central Tibet—active in the late 8th century. I consider the significance of specific similarities and their temporal correlations within a broader range of styles, materials, and technologies. I argue that the activity at these sites reveals a shared architectural agenda transmitted over vast distances by religious experts, including Buddhist monks, in the last decades of the 8th century. Central to the network of three temple-monasteries proposed is the role that a specific architectural type was understood to play in protecting the kingdom and extending a king’s sovereignty while manifesting his spiritual aspirations. By distinguishing between architectural forms, architectural agendas, and modes of production, this study clarifies the complex nature of transregional architectural exchange in the premodern world.
Interdisciplinary Study of a 15th-Century Byzantine Embroidery Fragment from St Elisabeth’s Cathedral in Košice (Slovakia)
Authors: Hasalová Eva , Krivda Andrej , Piatrová Alena
Published: November 2024

This study investigates a Byzantine embroidery fragment found in the Cathedral of St Elisabeth in Košice, Slovakia, which is believed to come from a 15th-century epitrachelion. Through interdisciplinary research, including art historical analysis, archival studies, and material research, the embroidery was identified as a rare example of Byzantine liturgical textile art in Slovakia. The iconography includes Archangel Gabriel and four Eastern Church Fathers. Comparative studies suggest possible origins in Greek or Romanian monastic workshops. Despite the information provided by radiocarbon dating and stylistic comparisons, the embroidery’s precise provenance and historical context invite further research.
Exploring the Use of High-Resolution Satellite Images to Estimate Corn Silage Yield Within Field
Authors: Subhashree Srinivasagan N. , Marcaida Manuel , Sunoj Shajahan , Kindred Daniel R. , Thompson Laura J. , Ketterings Quirine M.
Published: November 2024

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability. However, equipment’s sensor malfunctioning can result in data loss, and yield sensor systems are expensive to purchase and maintain. In this study, we analyzed corn silage yield data from two fields and three years each for two dairy farms (Farm A and B). We aimed to explore the potential of integrating high-resolution satellite data, topography, and climate data with machine learning models to estimate missing yield data for a field or a year. Our objectives were to identify key yield-explaining features and assess the accuracy of different machine learning models in estimating silage yield. Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors. Ensemble-based models like XGBoost, Random Forest, and Extra Tree regressors exhibited superior predictive performance. However, XGBoost performed poorly when applied to unseen fields or years, whereas Extra Tree regressor, followed closely by Random Forest, emerged as a more reliable model for predicting missing data. Despite achieving reasonable accuracy, the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha currently used in state policy to evaluate if a management change resulted in a yield increase. These findings emphasize the need for higher-resolution data and extended years of yield records to better capture the trends in farm-scale yield applications.
Distribution-Based Approach for Efficient Storage and Indexing of Massive Infrared Hyperspectral Sounding Data
Authors: Li Han , Gu Mingjian , Shi Guang , Hu Yong , Xie Mengzhen
Published: November 2024

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing.
Subframe-Level Synchronization in Multi-Camera System Using Time-Calibrated Video
Authors: Zhou Xiaoshi , Dai Yanran , Qin Haidong , Qiu Shunran , Liu Xueyang , Dai Yujie , Li Jing , Yang Tao
Published: November 2024

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups.
Supervised Deep Learning for Detecting and Locating Passive Seismic Events Recorded with DAS: A Case Study
Authors: Al-Hemyari Emad , Collet Olivia , Tertyshnikov Konstantin , Pevzner Roman
Published: November 2024

Exploring shallow mineral resources requires acquiring denser seismic data, for which Distributed Acoustic Sensing is an effective enabler and relevant to mining operations monitoring. Passive seismic can be of interest in characterizing the subsurface; however, dealing with large amounts of data pushes against the limits of existing computational systems and algorithms, especially for continuous monitoring. Hence, more than ever, novel data analysis methods are needed. In this article, we investigate using synthetic seismic data, paired with real noise recordings, as part of a supervised deep-learning neural network methodology to detect and locate induced seismic sources and explore their potential use to reconstruct subsurface properties. Challenges of this methodology were identified and addressed in the context of induced seismicity applications. Data acquisition and modelling were discussed, preparation workflows were implemented, and the method was demonstrated on synthetic data and tested on relevant seismic monitoring field dataset from the Otway CO2 injection site. Conducted tests confirmed the effects of time shifts, signal-to-noise ratios, and geometry mismatches on the performance of trained models. Those promising results showed the method’s applicability and paved the way for potential application to more field data, such as seismic while drilling.
Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8
Authors: Michael Goodnews , Shahra Essa Q. , Basurra Shadi , Wu Wenyan , Jabbar Waheb A.
Published: November 2024

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis.
Exploring the Influence of Tropical Cyclones on Regional Air Quality Using Multimodal Deep Learning Techniques
Authors: Younis Muhammad Waqar , Saritha  , Kallapu Bhavya , Hejamadi Rama Moorthy , Jijo Jeny , Ramesh  Raghunandan Kemmannu , Aslam Muhammad , Jilani Syeda Fizzah
Published: November 2024

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an R2 of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal.
High-Precision and Lightweight Model for Rapid Safety Helmet Detection
Authors: Jia Xuejun , Zhou Xiaoxiong , Su Chunyi , Shi Zhihan , Lv Xiaodong , Lu Chao , Zhang Guangming
Published: November 2024

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications.
Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR)
Authors: Jin Meiting , Zhang Junxing
Published: November 2024

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection.
Modeling and Evaluation of Penetration Process Based on 3D Mechanical Simulation
Authors: Chen Xiaohan , Gong Huiying , Yang Bin , Wang Zengshuo , Liu Yaowei , Zhou Lu , Zhao Xin , Sun Mingzhu
Published: November 2024

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.
A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios
Authors: Orro Alessandro , Geminiani Gian Angelo , Sicurello Francesco , Modica Marcello , Pegreffi Francesco , Neri Luca , Augello Antonio , Botteghi Matteo
Published: November 2024

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system’s reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.
Design of a Novel SiP Integrated RF Front-End Module Based on SOI Switch and SAW Filter
Authors: Wei Xuanhe , Miao Youming , Jin Xiao , Loh Tian Hong , Liu Gui
Published: November 2024

This paper proposes a novel System-in-Package (SiP) integrated architecture that incorporates Silicon-On-Insulator (SOI) switches and Surface Acoustic Wave (SAW) filters within the chip, aiming to fulfill the demands for miniaturization and multi-functionality for application in emerging wireless technologies. The proposed architecture not only reduces the integration complexity but also considers the architectural design of the integrated module, impedance matching techniques, and signal integrity for carrier aggregation (CA) technology realization. The feasibility of employing SOI switches and SAW filters based on SiP design has been validated through the trial production of a Sub-3GHz radio frequency (RF) front-end diversity receiver module. The resulting RF front-end module demonstrates exceptionally high packaging density and enhanced communication reliability, rendering it suitable for diverse applications in miniaturized RF systems.
Two-Dimensional Scattering Center Estimation for Radar Target Recognition Based on Multiple High-Resolution Range Profiles
Authors: Lee Kang-In , Kim Jin-Hyeok , Chung Young-Seek
Published: November 2024

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance.
Near-Infrared Spectroscopy for Neonatal Sleep Classification
Authors: Hakimi Naser , Arasteh Emad , Zahn Maren , Horschig Jörn M. , Colier Willy N. J. M. , Dudink Jeroen , Alderliesten Thomas
Published: November 2024

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model’s superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.
Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning
Authors: Kavak Fulya , Bora Sebnem , Kantarci Aylin , Uğur Aybars , Cagaptay Sumru , Gokcay Deniz , Aysal Anıl , Pehlivanoglu Burcin , Sagol Ozgul
Published: November 2024

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis.
Evaluation of Diffuse Reflectance Spectroscopy Vegetal Phantoms for Human Pigmented Skin Lesions
Authors: Buendia-Aviles Sonia , Cunill-Rodríguez Margarita , Delgado-Atencio José A. , González-Gutiérrez Enrique , Arce-Diego José L. , Fanjul-Vélez Félix
Published: November 2024

Pigmented skin lesions have increased considerably worldwide in the last years, with melanoma being responsible for 75% of deaths and low survival rates. The development and refining of more efficient non-invasive optical techniques such as diffuse reflectance spectroscopy (DRS) is crucial for the diagnosis of melanoma skin cancer. The development of novel diagnostic approaches requires a sufficient number of test samples. Hence, the similarities between banana brown spots (BBSs) and human skin pigmented lesions (HSPLs) could be exploited by employing the former as an optical phantom for validating these techniques. This work analyses the potential similarity of BBSs to HSPLs of volunteers with different skin phototypes by means of several characteristics, such as symmetry, color RGB tonality, and principal component analysis (PCA) of spectra. The findings demonstrate a notable resemblance between the attributes concerning spectrum, area, and color of HSPLs and BBSs at specific ripening stages. Furthermore, the spectral similarity is increased when a fiber-optic probe with a shorter distance (240 µm) between the source fiber and the detector fiber is utilized, in comparison to a probe with a greater distance (2500 µm) for this parameter. A Monte Carlo simulation of sampling volume was used to clarify spectral similarities.
Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT
Authors: Guo Dudu , Li Zhuzhou , Shuai Hongbo , Zhou Fei
Published: November 2024

In this paper, we address the issues of insufficient accuracy and frequent identity switching in the multi-target tracking algorithm DeepSORT by proposing two improvement strategies. First, we optimize the appearance feature extraction process by training a lightweight appearance extraction network (OSNet) on a vehicle re-identification dataset. This makes the appearance features better suited for the vehicle tracking model required in our paper. Second, we improve the metric of motion features by using the original IOU distance metric or GIOU metrics. The optimized tracking algorithm using GIOU achieves effective improvements in tracking precision and accuracy. The experimental results show that the improved vehicle tracking models MOTA and IDF1 are enhanced by 4.6% and 5.9%, respectively. This allows for the stable tracking of vehicles and reduces the occurrence of identity switching phenomenon to a certain extent.
ChartLine: Automatic Detection and Tracing of Curves in Scientific Line Charts Using Spatial-Sequence Feature Pyramid Network
Authors: Yang Wenjin , He Jie , Li Qian
Published: November 2024

Line charts are prevalent in scientific documents and commercial data visualization, serving as essential tools for conveying data trends. Automatic detection and tracing of line paths in these charts is crucial for downstream tasks such as data extraction, chart quality assessment, plagiarism detection, and visual question answering. However, line graphs present unique challenges due to their complex backgrounds and diverse curve styles, including solid, dashed, and dotted lines. Existing curve detection algorithms struggle to address these challenges effectively. In this paper, we propose ChartLine, a novel network designed for detecting and tracing curves in line graphs. Our approach integrates a Spatial-Sequence Attention Feature Pyramid Network (SSA-FPN) in both the encoder and decoder to capture rich hierarchical representations of curve structures and boundary features. The model incorporates a Spatial-Sequence Fusion (SSF) module and a Channel Multi-Head Attention (CMA) module to enhance intra-class consistency and inter-class distinction. We evaluate ChartLine on four line chart datasets and compare its performance against state-of-the-art curve detection, edge detection, and semantic segmentation methods. Extensive experiments demonstrate that our method significantly outperforms existing algorithms, achieving an F-measure of 94% on a synthetic dataset.
Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs
Authors: Wei Pengna , Chen Tong , Zhang Jinhua , Li Jiandong , Hong Jun , Zhang Lin
Published: November 2024

Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain–computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain–body imaging dataset recorded during treadmill walking with a brain–computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time–frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal–occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.
Evaluating MIR and NIR Spectroscopy Coupled with Multivariate Analysis for Detection and Quantification of Additives in Tobacco Products
Authors: Akhtar Zeb , Canfyn Michaël , Vanhee Céline , Delporte Cédric , Adams Erwin , Deconinck Eric
Published: November 2024

The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87–100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation.
A Stacked Neural Network Model for Damage Localization
Authors: Rusu Catalin V. , Gillich Gilbert-Rainer , Tufisi Cristian , Gillich Nicoleta , Bui Thu Hang , Ionut Cosmina
Published: November 2024

Traditional vibration-based damage detection methods often involve human intervention in decision-making, therefore being time-consuming and error-prone. In this study, we propose using Artificial Neural Networks (ANNs) to detect patterns in the structural response and create accurate predictions. The features extracted from the response signal are the Relative Frequency Shifts (RFSs) of the first eight weak-axis bending vibration modes, and the predictions refer to the damage location. To increase the accuracy of the predictions, we propose a novel stacked neural network approach, capable of detecting damage locations with high accuracy. The dataset used for training involves, as input data, the RFSs calculated with an original method for numerous damage locations and severities. The following models were used as building blocks for our stacked approach: Multilayer Perceptron, Recurrent Neural Network, Long Short-term Memory, and Gated Recurrent Units. The entire beam was thus split into segments and each network was trained in this stacked model on one beam segment. All results obtained with the models are also compared to a standard neural network trained on the entire beam. The results obtained show that the model that performs the best contains 14 stacked two-layer feedforward networks.