bluuebunny/crossref_metadata_2025_split huggingface dataset:
A 17-level octuple boost switched-capacitor inverter with lower voltage stress on devices
Authors: Hosseinpour Majid , Noori Meysam , Shahparasti Mahdi 
Published: June 2024

AbstractThis paper presents a new structure for switched-capacitor multilevel inverter with octuple voltage gain capability. The proposed inverter utilizes three capacitors, 13 semiconductor switches, three diodes, and an input voltage source to achieve a 17-level output voltage. The switched capacitors naturally achieve voltage balancing without the need for sensors or additional circuits, indicating the ease of control of the proposed structure. To control the inrush current of the switched capacitors, a charge limiting inductor has been utilized in the charging path of the capacitors. This not only reduces the inrush current of the capacitors and the input source current but also enables faster capacitor charging and extends their lifetime. The switches used in the proposed structure can withstand a maximum of 4 times the input voltage value or the half of the maximum output voltage, which is a significant advantage for the proposed structure. A detailed comparison with similar structures is provided to examine the advantages and disadvantages of the suggested inverter. The procedure of self-voltage balancing of the capacitors and the functional modes of the proposed topology has been explained in detail. The proposed structure is suitable for applications such as renewable energy sources transfer to load or grid. The performance of the proposed topology under different conditions is confirmed through simulation in the Matlab\Simulink software and the implementation of the laboratory sample.
Creep level qualitative evaluating of crushed rock based on uncertainty measurement theory and hierarchical analysis
Authors: Wu Shiwei , Mou Qi , Yang Tao 
Published: June 2024

AbstractA large number of tectonically mixed rock belts and complex tectonic zones are distributed in the southwestern part of China. In these areas, high geostress and tectonic stresses have caused some underground rock layers to be crushed and broken, eventually forming crushed rock zones. Which may undergo creep deformation under long-term loads. The manuscript is based on a typical crushed rock in the southwestern China. Firstly, the factors affecting creep deformation were analysed, and the response law of each influencing factor to rock creep is demonstrated. Then, the theory of uncorroborated measures and hierarchical analysis were used to systematically correlate the factors influencing creep. Thereby, a creep level qualitative evaluating model of crushed rock is established. Finally, this model was used to qualitatively evaluate the creep level of the crushed rock in the study area. It is concluded that the creep level qualitative evaluating of this crushed rock is rated as Class II, which is characterised by a low creep level and small creep deformations (0–10 mm). The research results can provide a reference for the creep analysis of crushed rock and provide a basis for the safe construction of engineering slopes.
Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition
Authors: Moussaoui Hanae , Akkad Nabil El , Benslimane Mohamed , El-Shafai Walid , Baihan Abdullah , Hewage Chaminda , Rathore Rajkumar Singh 
Published: June 2024

AbstractVehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.
Effects of diffuse light on the gross ecosystem primary productivity of a winter wheat (Triticum aestivum L.) cropland in northern China
Authors: Bao Xueyan , Sun Xiaomin 
Published: June 2024

AbstractDiffuse light is produced by clouds and aerosols in the atmosphere. Exploring the effects of diffuse light on ecosystem productivity is important for understanding the terrestrial carbon (CO2) cycle. Here, 2 years of gross ecosystem primary productivity (GEP) from a (winter) wheat cropland in China was assessed using eddy covariance technology to explore the effects of diffuse photosynthetic active radiance (PAR) on wheat GEP. Wheat GEP increased significantly and positively along with diffuse PAR. In addition, wheat GEP was significantly affected by total PAR, air temperature, and vapor press deficit in different diffuse PAR fraction (fDIF) change stages. Because significant autocorrelations existed among the controlling factors, a path analysis was used to quantify the effects of diffuse light on GEP. Diffuse PAR was the primary and secondary importance factors affecting GEP with direct path coefficients of 0.54 and 0.48, respectively, in different fDIF change stages. A multilayer canopy model revealed that the middle and lower canopy levels intercepted more light when diffuse PAR increased. This resulted in the photosynthetic enhancement of middle and lower canopy levels, which contributed approximately 65% and 35%, respectively, to the increase in photosynthesis for the entire canopy (~ 30.5%). Overall, our study provided new evidence regarding the importance of diffuse light for CO2 uptake in agroecosystems, which is important for predicting the responses of ecosystem CO2 budgets to future climate-related light changes.
YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture
Authors: Chen Guojun , Hou Yongjie , Cui Tao , Li Huihui , Shangguan Fengyang , Cao Lei 
Published: June 2024

AbstractColor-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.
Age-related changes in the susceptibility to visual illusions of size
Authors: Mazuz Yarden , Kessler Yoav , Ganel Tzvi 
Published: June 2024

AbstractAs the global population ages, understanding of the effect of aging on visual perception is of growing importance. This study investigates age-related changes in adulthood along size perception through the lens of three visual illusions: the Ponzo, Ebbinghaus, and Height-width illusions. Utilizing the Bayesian conceptualization of the aging brain, which posits increased reliance on prior knowledge with age, we explored potential differences in the susceptibility to visual illusions across different age groups in adults (ages 20–85 years). To this end, we used the BTPI (Ben-Gurion University Test for Perceptual Illusions), an online validated battery of visual illusions developed in our lab. The findings revealed distinct patterns of age-related changes for each of the illusions, challenging the idea of a generalized increase in reliance on prior knowledge with age. Specifically, we observed a systematic reduction in susceptibility to the Ebbinghaus illusion with age, while susceptibility to the Height-width illusion increased with age. As for the Ponzo illusion, there were no significant changes with age. These results underscore the complexity of age-related changes in visual perception and converge with previous findings to support the idea that different visual illusions of size are mediated by distinct perceptual mechanisms.
Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study
Authors: Tan Huai-Cheng , Zeng Li-Jun , Yang Shu-Juan , Hou Li-Sha , Wu Jin-Hui , Cai Xin-Hui , Heng Fei , Gu Xu-Yu , Zhong Yue , Dong Bi-Rong , Dou Qing-Yu 
Published: June 2024

AbstractThis study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.
Navigating latency hurdles: an in-depth examination of a cloud-powered GNSS real-time positioning application on mobile devices
Authors: Hernández Olcina Jorge , Anquela Julián Ana B. , Martín Furones Ángel E. 
Published: June 2024

AbstractA growing dependence on real-time positioning apps for navigation, safety, and location-based services necessitates a deep understanding of latency challenges within cloud-based Global Navigation Satellite System (GNSS) solutions. This study analyses a GNSS real-time positioning app on smartphones that utilizes cloud computing for positioning data delivery. The study investigates and quantifies diverse latency contributors throughout the system architecture, including GNSS signal acquisition, data transmission, cloud processing, and result dissemination. Controlled experiments and real-world scenarios are employed to assess the influence of network conditions, device capabilities, and cloud server load on overall positioning latency. Findings highlight system bottlenecks and their relative contributions to latency. Additionally, practical recommendations are presented for developers and cloud service providers to mitigate these challenges and guarantee an optimal user experience for real-time positioning applications. This study not only elucidates the complex interplay of factors affecting GNSS app latency, but also paves the way for future advancements in cloud-based positioning solutions, ensuring the accuracy and timeliness critical for safety–critical and emerging applications.
5G wavelength-division-multiplexing-based bidirectional optical wireless communication system with signal remodulation employing cascaded reflective semiconductor optical amplifiers
Authors: Lu Hai-Han , Li Chung-Yi , Huang Xu-Hong , Bai Yu-Yao , Hsu Wei-Wen , Chung Yu-Chen , Lu Jia-Ming , Okram Kelper 
Published: June 2024

AbstractCompared with previous generations, fifth-generation communications can provide faster download and upload speeds and support a greater number of connected devices. Integrating fifth-generation signals with optical wireless communication systems provides promising ways to afford higher transmission rates and faster wireless connectivity. Here we report a fifth-generation wavelength-division-multiplexing-based bidirectional optical wireless communication system with signal remodulation employing cascaded reflective semiconductor optical amplifiers to effectively remove the downstream data for uplink transmission. It shows a fifth-generation wavelength-division-multiplexing-based bidirectional optical wireless communication system using four wavelengths for communication. The uplink performance is substantially enhanced by using two reflective semiconductor optical amplifiers to remove the downstream data. The system achieves an aggregate transmission rate of 36.4 Gbit/s for both downlink and uplink transmissions over a 100-m optical wireless link. This demonstrated fifth-generation wavelength-division-multiplexing-based bidirectional optical wireless communication system employing cascaded reflective semiconductor optical amplifiers holds great potential for enhancing fifth-generation advanced communication capabilities.
UAV‐aided distribution line inspection using double‐layer offloading mechanism
Authors: Duo Chunhong , Li Yongqian , Gong Wenwen , Li Baogang , Qi Guoliang , Zhang Ji 
Published: June 2024

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.
Comprehensive study on tree‐contact single‐phase‐to‐ground faults: Modelling, risk analysis, and detection recommendations
Authors: Liang Dong , Xu Bingyin , Wang Pengwei , Wang Wei 
Published: June 2024

AbstractTree‐contact single‐phase‐to‐ground faults (TSFs) present a significant fire hazard in electrical distribution systems, and research on the development, modelling, and monitoring is insufficient. This article investigates the electro‐thermal coupling mechanisms during the current rise of TSFs, constructing a simplified power–frequency model that leverages actual physical parameters like tree size for prolonged simulations of the first TSF stage zero‐sequence current. Validated through full‐scale testing, the model enables digital simulations across various tree sizes, concluding that the detection sensitivity for grounding faults should not exceed 0.5 A to mitigate fire risks effectively. This approach surpasses the limitations of traditional high‐impedance fault models reliant on empirical parameters and the shortcomings of generalised conclusions from experimental inductions, offering a new paradigm for TSF research that enhances the predictive accuracy and applicability of fault analysis in electrical distribution networks.
B‐splines image approximation using resampled chordal parameterization
Authors: Świta Robert , Suszyński Zbigniew 
Published: June 2024

AbstractImage processing often requires filtering, which can be effectively performed by using B‐spline surface approximation. This article presents a fast method of approximating data in rectangular (image) form using such surfaces. The method considers dynamic changes of data by representing rows and columns in chordal parameterized form. To improve performance, after parametrization, lines are uniformly resampled using linear interpolation. It allows using the same basis functions with uniform parameterization for approximation of all processed lines. After approximation, reconstruction of original parameterization is required, but its computational complexity is also linear.
Towards high‐definition vector map construction based on multi‐sensor integration for intelligent vehicles: Systems and error quantification
Authors: Hu Runzhi , Bai Shiyu , Wen Weisong , Xia Xin , Hsu Li‐Ta 
Published: June 2024

AbstractA lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open‐source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR‐camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios.
Localizing the non-thermal X-ray emission of PSR J2229+6114 from its multi-wavelength pulse profiles
Authors: Pétri Jérôme , Guillot Sebastien , Guillemot Lucas , Mitra Dipanjan , Kerr Matthew , Kuiper Lucien , Cognard Ismaël , Theureau Gilles 
Published: June 2024

Context. Pulsars are detected over the whole electromagnetic spectrum, from radio wavelengths up to very high energies, in the GeV-TeV range. While the radio emission site for young pulsars is well constrained to occur at altitudes about several percent of the light-cylinder radius and γ-ray emission is believed to be produced in the striped wind, outside the light cylinder, the non-thermal X-ray production site remains unknown.
Aims. The aim of this Letter is to localize the non-thermal X-ray emission region based on a multi-wavelength pulse profile fitting for PSR J2229+6114, which stands as a particularly good candidate given its high X-ray brightness.
Methods. Based on the geometry deduced from the joint radio and γ-ray pulse profiles, we fixed the magnetic axis inclination angle and the line-of-sight (LoS) inclination angle. However, we left the region of X-ray emission unlocalized, setting it somewhere between the surface and the light cylinder. We localized this region and its extension by fitting the X-ray pulse profile as observed by the NICER, NuSTAR, and RXTE telescopes in the ranges of 2–7 keV, 3–10 keV, and 9.4–22.4 keV, respectively.
Results. We constrained the non-thermal X-ray emission to arise from altitudes between 0.2 rL and 0.55 rL where rL is the light-cylinder radius. The magnetic obliquity is approximately α ≈ 45° −50° and the LoS inclination angle is ζ ≈ 32° −48°.
Conclusions. This Letter is among the first works to offer a tight constraint on the location of non-thermal X-ray emission from pulsars. We plan to apply this procedure to several other promising candidates to confirm this new result.
Nierenersatztherapie in der Intensivmedizin
Authors: Enghard Philipp , Krüger Anne , Jörres Achim 
Published: June 2024

ZUSAMMENFASSUNGNeben den klassischen Indikationen für die Nierenersatztherapie (NET) gibt es eine Reihe von Spezialindikationen sowie die Möglichkeit, mit Adsorbern oder speziellen Verfahren gezielt die Homöostase in verschiedenen Krankheitsbildern wiederherzustellen. Das macht die NET auf Intensivstation zu einem spannenden und dynamischen Feld mit vielfältigen Einsatzmöglichkeiten. Aktuell fehlt für die meisten Indikationen allerdings noch eine gute Datenbasis für die Anwendung der entsprechenden Verfahren, sodass derzeit kein Einsatz in der klinischen Routine (außerhalb klinischer Studien) empfohlen werden kann.
Extrakorporale Eliminationsverfahren bei Vergiftungen
Authors: Steindl David , Greb Ingo , Hüser Christoph 
Published: June 2024

ZUSAMMENFASSUNGDialyseverfahren können über die etablierte Indikation bei vergiftungsbedingter Nieren- und seltener auch Leberfunktionseinschränkung hinaus bei bestimmten Vergiftungen auch zur extrakorporalen Elimination der auslösenden Noxe eingesetzt werden. Die Indikationsstellung kann mit der dargestellten abgestuften nephrologischen Einschätzung, unterstützt durch die toxikologische Beratung der Giftnotrufzentralen und die Empfehlungen der EXTRIP-Workgroup, getroffen werden. Dialyseverfahren bieten hier in einigen Fällen evidenzbasierte Therapieoptionen, während die Anwendung von Adsorptionsverfahren, Albumindialyse oder Plasmapharese eine Einzelfallentscheidung bleibt.