bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk factors for collisions attributed to microsleep-related behaviors while driving in professional truck drivers
Authors: Sawatari Hiroyuki , Kumagai Hajime , Kawaguchi Kengo , Kiyohara Yuka , Konishi Noriyuki , Arita Aki , Hayashi Mitsuo , Shiomi Toshiaki 
Published: March 2024

AbstractSleep-disordered breathing (SDB) is prevalent among professional drivers. Although SDB is a known risk factor for truck collisions attributed to microsleep-related behaviors at the wheel (TC-MRBs), the usefulness of overnight pulse oximetry for predicting TC-MRBs is debatable. This retrospective study assessed the association between overnight pulse oximetry parameters, the Epworth Sleepiness Scale (ESS), and TC-MRBs, confirmed by dashcam footage. This study included 108 matched professional truck drivers (TC-MRBs: N = 54; non-TC-MRBs: N = 54), with a mean age and body mass index of 41.9 ± 11.3 years and 23.0 ± 3.7 kg/m2, respectively. Night-time drivers, 4% oxygen desaturation index (ODI), and nadir oxygen saturation (SpO2) were associated with TC-MRBs (odds ratio [95% confidence interval]: 25.63 [5.88–111.77], p < 0.0001; 2.74 [1.02–7.33], p = 0.045; and 3.87 [1.04–14.39], p = 0.04, respectively). The area under the curve of 4% ODI and nadir SpO2 for TC-MRBs were 0.50 and 0.57, respectively. In conclusion, night-time driving, 4% ODI, and nadir SpO2 were significantly associated with TC-MRBs in professional truck drivers. However, the sensitivity of overnight pulse oximetry parameters to predict TC-MRBs in a real-world application was poor. Therefore, combining subjective and objective assessments such as dashcam video footage may be needed to achieve high accuracy for predicting TC-MRBs among professional truck drivers.
Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain
Authors: Zhang Weiwei , Hao Jianhua , Liu Fangai 
Published: March 2024

AbstractThis paper presents a novel distributed assembly permutation flowshop scheduling problem (DAPFSP) based on practical problems in automobile production. Different from the existing research on DAPFSP, this study considers that each component of the final product is composed of more than one part. Components are processed in a set of identical components manufacturing factories and are assembled into products in the assembly factory. The integration of manufacturing processes is an important objective of Industry 4.0. For solving this problem with the minimum makespan criterion, we introduce a three-level representation and a novel initialization method. To enhance the search ability of the proposed algorithms, we design three local search methods and two restart procedures according to characteristics of the problem. Then, by incorporating the problem specific knowledge with the social spider optimization algorithm (SSO), we propose three SSO variants: the SSO with hybrid local search strategies (HSSO), the HSSO with restart procedures (HSSOR), and the HSSOR with self-adaptive selection probability (HSSORP). Finally, 810 extended instances based on the famous instances are used to test the proposed algorithms. In most cases, HSSOR performs the best, with an average comparison metric value of 0.158% across three termination conditions, while the average comparison metric value for the best comparison method is 2.446%, which is 15.481 times that of HSSOR. Numerical results demonstrate that the proposed algorithms can solve the problem efficiently.
The porcine islet-derived organoid showed the characteristics as pancreatic duct
Authors: Sakata Naoaki , Yoshimatsu Gumpei , Kawakami Ryo , Nakano Kazuaki , Yamada Teppei , Yamamura Akihiro , Nagashima Hiroshi , Kodama Shohta 
Published: March 2024

AbstractOrganoid is a tissue-engineered organ-like structure that resemble as an organ. Porcine islet-derived organoid might be used as an alternative donor of porcine islet xenotransplantation, a promising therapy for severe diabetes. In this study, we elucidated the characteristics of porcine islet organoids derived from porcine islets as a cell source for transplantation. Isolated porcine islets were 3D-cultured using growth factor-reduced matrigel in organoid culture medium consist of advanced DMEM/F12 with Wnt-3A, R-spondin, EGF, Noggin, IGF-1, bFGF, nicotinamide, B27, and some small molecules. Morphological and functional characteristics of islet organoids were evaluated in comparison with 2D-cultured islets in advanced DMEM/F12 medium. Relatively short-term (approximately 14 days)—cultured porcine islet organoids were enlarged and proliferated, but had an attenuated insulin-releasing function. Long-term (over a month)—cultured islet organoids could be passaged and cryopreserved. However, they showed pancreatic duct characteristics, including cystic induction, strong expression of Sox9, loss of PDX1 expression, and no insulin-releasing function. These findings were seen in long-term-cultured porcine islets. In conclusion, our porcine islet organoids showed the characteristics of pancreatic ducts. Further study is necessary for producing porcine islet-derived organoids having characteristics as islets.
Influence of saturation level on the acoustic emission characteristics of gas hydrate-bearing coal
Authors: Yu Kang , Miao Yu , Jinchao Zhu 
Published: March 2024

AbstractTo study the effects of gas hydrates on the prevention and control of coal and gas protrusions, this paper reports the results of acoustic emission experiments on coal bodies containing gas hydrates with different saturation levels. The results showed that few acoustic emission events were generated in the elasticity stages of coal bodies containing gas hydrates, and the first sudden increase in the number of ringing counts generally occurred before and after the yielding point. Additionally, the acoustic emission events in the yielding stage were more active, and the cumulative number of ringing counts increased faster. The peak ringing counts appeared around the damage point, and a small number of acoustic emission events were still generated after destruction of the coal samples. The cumulative ringing counts decreased linearly with increasing saturation. The effect of saturation on the cumulative ringing counts in the elasticity stage of the gas hydrate-containing coal samples was small, but the difference between the cumulative ringing counts in the yielding stage and those in the destruction stage was larger. The total cumulative ringing counts and the cumulative ringing counts during each stage for the gas hydrate-containing coal samples decreased with increasing enclosure pressure. The energy and amplitude of the loading process were consistent with the trend for the ringing counts. The acoustic emission ringing counts of gas-containing coals were greater than those of gas hydrate-containing coals in the yielding and destructing stages.
Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes
Authors: Blake Kevin S. , Kumar Hirdesh , Loganathan Anisha , Williford Emily E. , Diorio-Toth Luke , Xue Yao-Peng , Tang Wai Kwan , Campbell Tayte P. , Chong David D. , Angtuaco Steven , Wencewicz Timothy A. , Tolia Niraj H. , Dantas Gautam 
Published: March 2024

AbstractTetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.
Time-dependent Gutzwiller simulation of Floquet topological superconductivity
Authors: Anan Takahiro , Morimoto Takahiro , Kitamura Sota 
Published: March 2024

AbstractPeriodically driven systems provide a novel route to control the topology of quantum materials. In particular, Floquet theory allows an effective band description of periodically-driven systems through the Floquet Hamiltonian. Here, we study the time evolution of d-wave superconductors irradiated with intense circularly-polarized laser light. We consider the Floquet t–J model with time-periodic interactions, and investigate its mean-field dynamics by formulating the time-dependent Gutzwiller approximation. We observe the development of the idxy-wave pairing amplitude along with the original $${d}_{{x}^{2}-{y}^{2}}$$

d

x

2

−

y

2

-wave order upon gradual increasing of the field amplitude. We further numerically construct the Floquet Hamiltonian for the steady state, with which we identify the system as the fully-gapped d + id superconducting phase with a nonzero Chern number. We explore the low-frequency regime where the perturbative approaches in the previous studies break down, and find that the topological gap of an experimentally-accessible size can be achieved at much lower laser intensities.
Locally controlled MOF growth on functionalized carbon nanotubes
Authors: Dzinnik Marvin J. , Akmaz Necmettin E. , Hannebauer Adrian , Schaate Andreas , Behrens Peter , Haug Rolf J. 
Published: March 2024

AbstractMetal-organic frameworks (MOFs) are highly versatile materials because of their tunable properties. However, the typically poor electrical conductivity of MOFs presents challenges for their integration into electrical devices. By adding carbon nanotubes to MOF synthesis, a highly intergrown material with increased conductivity and chemiresistive sensing properties can be obtained. Here, we present a patterning technique to control MOF growth on predefined areas of one particular carbon nanotube. We found that electron beam pretreatment of -COOH functionalized multi-walled carbon nanotubes inhibits the growth of UiO-66 MOF on these multi-walled carbon nanotubes. By irradiating individual multi-walled carbon nanotubes, we show that MOF growth can be inhibited in predefined tube areas, creating MOF-free spaces on the nanotube. In this way, our method shows a possibility to pattern MOF growth on individual nanotubes.
The complex composition of organic aerosols emitted during burning varies between Arctic and boreal peat
Authors: Schneider Eric , Rüger Christopher P. , Chacón-Patiño Martha L. , Somero Markus , Ruppel Meri M. , Ihalainen Mika , Köster Kajar , Sippula Olli , Czech Hendryk , Zimmermann Ralf 
Published: March 2024

AbstractPeatlands in the northern hemisphere are a major carbon storage but face an increased risk of wildfires due to climate change leading to large-scale smoldering fires in boreal and Arctic peatlands. Smoldering fires release organic carbon rich particulate matter, which influences the earth’s radiative balance and can cause adverse health effects for humans. Here we characterize the molecular composition of biomass burning particulate matter generated by laboratory burning experiments of peat by electrospray ionization 21 T Fourier-transform ion cyclotron resonance mass spectrometry, revealing a highly complex mixture of aromatic and aliphatic organic compounds with abundant heteroatoms including oxygen, sulfur and up to five nitrogen atoms. Primary organosulfur species are identified in the emissions of peat-smoldering, in part also containing nitrogen. Differences are observed when comparing structural motifs as well as the chemical composition of boreal and Arctic peat burning emissions, with the latter containing compounds with more nitrogen and sulfur.
Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time
Authors: Liu Fubin , Gao Kaizhou , Li Dachao , Sadollah Ali 
Published: March 2024

AbstractA distributed heterogeneous permutation flowshop scheduling problem with sequence‐dependent setup times (DHPFSP‐SDST) is addressed, which well reflects real‐world scenarios in heterogeneous factories. The objective is to minimise the maximum completion time (makespan) by assigning jobs to factories, and sequencing them within each factory. First, a mathematical model to describe the DHPFSP‐SDST is established. Second, four meta‐heuristics, including genetic algorithms, differential evolution, artificial bee colony, and iterated greedy (IG) algorithms are improved to optimally solve the concerned problem compared with the other existing optimisers in the literature. The Nawaz‐Enscore‐Ham (NEH) heuristic is employed for generating an initial solution. Then, five local search operators are designed based on the problem characteristics to enhance algorithms' performance. To choose the local search operators appropriately during iterations, Q‐learning‐based strategy is adopted. Finally, extensive numerical experiments are conducted on 72 instances using 5 optimisers. The obtained optimisation results and comparisons prove that the improved IG algorithm along with Q‐learning based local search selection strategy shows better performance with respect to its peers. The proposed algorithm exhibits higher efficiency for scheduling the concerned problems.
Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications
Authors: Sharma Amit , Sharma Ashutosh , Tselykh Alexey , Bozhenyuk Alexander , Kim Byung‐Gyu 
Published: March 2024

AbstractGraph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification. The authors explore in depth the applications of GNNs in computer vision, including their design considerations, architectural challenges, applications, and implementation concerns. While conventional convolutional neural networks (CNNs) excel at object recognition in images and videos, GNN architectures offer a novel method for addressing various image and video comprehension challenges. A novel deep neural network‐based model for image and video analysis is proposed, which combines a neural network with fully connected layers on a graph. The proposed architecture extracts highly discriminative information from images and videos by leveraging the graph structure. Also, the investigation focuses on the enhancement of underlying connection network estimation using cutting‐edge graph learning algorithms. Experimental results on real‐world datasets demonstrate that the proposed GNN model is preferable to existing state‐of‐the‐art methods. It obtains a remarkable 96.63% accuracy on the ImageNet dataset, outperforming heuristic approaches, artificial neural networks, and conventional CNN techniques. From the results, we can see that GNNs are a potent instrument for graph data analysis and pave the way for machines to achieve human‐level visual intuition.
Learnable fusion mechanisms for multimodal object detection in autonomous vehicles
Authors: Massoud Yahya , Laganiere Robert 
Published: March 2024

AbstractPerception systems in autonomous vehicles need to accurately detect and classify objects within their surrounding environments. Numerous types of sensors are deployed on these vehicles, and the combination of such multimodal data streams can significantly boost performance. The authors introduce a novel sensor fusion framework using deep convolutional neural networks. The framework employs both camera and LiDAR sensors in a multimodal, multiview configuration. The authors leverage both data types by introducing two new innovative fusion mechanisms: element‐wise multiplication and multimodal factorised bilinear pooling. The methods improve the bird's eye view moderate average precision score by +4.97% and +8.35% on the KITTI dataset when compared to traditional fusion operators like element‐wise addition and feature map concatenation. An in‐depth analysis of key design choices impacting performance, such as data augmentation, multi‐task learning, and convolutional architecture design is offered. The study aims to pave the way for the development of more robust multimodal machine vision systems. The authors conclude the paper with qualitative results, discussing both successful and problematic cases, along with potential ways to mitigate the latter.
Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system
Authors: Dini Pierpaolo , Basso Giovanni , Saponara Sergio , Romano Claudio 
Published: March 2024

AbstractThe article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.
Grid‐forming converters in interconnected power systems: Requirements, testing aspects, and system impact
Authors: Rogalla Soenke , Ernst Philipp , Lens Hendrik , Schaupp Thomas , Schöll Christian , Singer Roland , Ungerland Jakob 
Published: March 2024

AbstractIn this study, the integration of grid‐forming (GFM) converters in power systems is discussed in terms of both the fundamental aspects of system stability and the technical possibilities of converter‐based resources. The paper provides a survey and comparison of various GFM control concepts with respect to their transient and stationary behavior. A method for black box characterization and conformity testing of GFM converters is presented and relevant results of lab and field tests with GFM converters are discussed. Finally, important system aspects are addressed. This includes an approach to consider the behavior of GFM converters in distribution grid models and investigations on the minimum share of GFM units required to ensure system stability. The findings of this paper provide valuable insights for the design and implementation of GFM converters in modern converter‐based power systems.
Predicting the coupling of HPEM waveform with a power amplifier in perforated conductive enclosure using FDDM/VF
Authors: Kalantarnia Ali , Mirzabeigi Abdollah 
Published: March 2024

AbstractOne of the most common methods to protect electronic and telecommunication systems in a high‐power electromagnetic (HPEM) environment is using a conductive enclosure. Amplifiers are the vital parts in transmitter and receiver systems as the main components of a telecommunication system, so investigating the effects of HPEM waveform on their performance is very important. The Presence of apertures in the body of the conductive enclosure, as well as non‐linear elements such as transistors in amplifiers, adds to the complexity of examining the coupling of electromagnetic fields on the performance of amplifiers. Considering the importance of the topic, in this study, the impact of the HPEM waveform on the power amplifier inside the conductive enclosure has been investigated. By using the finite difference delay modelling (FDDM) numerical method as a stable method followed by the vector fitting (VF) method, the electromagnetic problem has become a circuit problem, which allows examining the response of the power amplifier and any circuit, including non‐linear elements against the interference field. Responses of bipolar linear and non‐linear components in the perforated conductive enclosure by the proposed method have been compared with the results obtained by computer simulation technology microwave studio (CST‐MWS) for verification.