bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analogical environmental cost assessment of silicon flows used in solar panels by the US and China
Authors: Golroudbary Saeed Rahimpour , Lundström Mari , Wilson Benjamin P. 
Published: April 2024

AbstractAchieving carbon neutrality requires deployment of large-scale renewable energy technologies like solar photovoltaic (PV) panels. Nevertheless, methods to ascertain the overall environmental impacts PVs and further improve their sustainability are under-investigated. In an effort to provide more understanding of this crucial topic, this research focuses on silicon flows—a key element for manufacturing crystalline silicon PVs. Using system dynamics modeling, we conduct a comprehensive environmental cost assessment of the silicon flows used in PVs based on a comparative analysis between the United States and China as the leading global PV manufacturers. Despite the advancement in wafer quality, material usage reductions and overall price decreases achieved in recent decades, our results project a substantial increase in energy and water consumption in China related to Metallurgical Grade Si (MG-Si), Solar Grade Si (SoG-Si) and cell manufacturing by 2030. An approximate 6.5 times increase of energy and water consumption is observed for c-Si cell manufacturing in China between 2010 and 2020. In 2030, increases of 70% in energy consumption and 69% in water use are estimated for Chinese MG-Si and SoG-Si production. The most significant environmental impact is observed in silicon cell and module manufacturing in both countries, particularly concerning GHG, SOx and NOx emissions. This study provides valuable insights into the environmental impacts of these two major solar panel manufacturing countries by examining the silicon life cycle, from production to end-of-life.
Clinical characteristics and removal approaches of tracheal and bronchial foreign bodies in elders
Authors: Hong Ping-Yang , Wang Ling , Du Yan-Ping , Wang Miao , Chen Yi-Yuan , Huang Mao-Hong , Zhang Xiao-Bin 
Published: April 2024

AbstractThe symptoms of tracheobronchial foreign body in the elderly are not typical, so they are often missed or misdiagnosed. This study aims to depict the clinical characteristics of tracheobronchial foreign body inhalation in the elderly. We retrospectively analysed the clinical data of elder patients (age ≥ 65 years) diagnosed with tracheal and bronchial foreign bodies. The data included age, sex, clinical symptoms, type and location of foreign bodies, prehospital duration, Chest CT, bronchoscopic findings, and frequencies and tools for removing these elderly patients' tracheal and bronchial foreign bodies. All patients were followed up for a half year. Fifty-nine cases were included, of which only 32.2% had a definite aspiration history. Disease duration > 30 days accounted for 27.1% of the patients. 27.1% of the patients had a history of stroke, and 23.8% had Alzheimer's Disease. Regarding clinical symptoms, patients mainly experience cough and expectoration. The most common CT findings were abnormal density shadow (37.3%) and pulmonary infiltration (22.0%). Under bronchoscopy, purulent secretions were observed in 52.5% of patients, and granulation tissue hyperplasia was observed in 45.8%. Food (55.9%) was the most common foreign object, including seafood shells (5.1%), bones (20.3%), dentures (18.6%), and tablets (20.3%). The success rate of foreign body removal under a bronchoscope was 96.7%, 28.8% of the foreign bodies were on the left and 69.5% on the right. 5.1% of the elderly patients required rigid bronchoscopy, and 6.8% required two bronchoscopies. In elderly cohorts, tracheal foreign bodies are obscured by nonspecific clinical presentations and a paucity of aspiration history, challenging timely diagnosis. Predominantly constituted by food particles, with a notable predilection for the left bronchial tree, these cases demand skilled bronchoscopic management, occasionally requiring sophisticated approaches for successful extraction.
Sex differences in the diagnostic value of optic nerve sheath diameter for assessing intracranial pressure
Authors: Pansell Jakob , Rudberg Peter C. , Friman Ola , Bell Max , Cooray Charith 
Published: April 2024

AbstractThe optic nerve sheath diameter (ONSD) can predict elevated intracranial pressure (ICP) but it is not known whether diagnostic characteristics differ between men and women. This observational study was performed at the Karolinska University Hospital in Sweden to assess sex differences in diagnostic accuracy for ONSD. We included 139 patients (65 women), unconscious and/or sedated, with invasive ICP monitoring. Commonly used ONSD derived measurements and associated ICP measurements were collected. Linear regression analyses were performed with ICP as dependent variable and ONSD as independent variable. Area under the receiver operator characteristics curve (AUROC) analyses were performed with a threshold for elevated ICP ≥ 20 mmHg. Analyses were stratified by sex. Optimal cut-offs and diagnostic characteristics were estimated. The ONSD was associated with ICP in women. The AUROCs in women ranged from 0.70 to 0.83. In men, the ONSD was not associated with ICP and none of the AUROCs were significantly larger than 0.5. This study suggests that ONSD is a useful predictor of ICP in women but may not be so in men. If this finding is verified in further studies, this would call for a re-evaluation of the usage and interpretation of ONSD to estimate ICP.
The dual coding of a single sex pheromone receptor in Asian honeybee Apis cerana
Authors: Ke Haoqin , D. Bohbot Jonathan , Chi Yongjuan , Duan Shiwen , Ma Xiaomei , Ren Bingzhong , Wang Yinliang 
Published: April 2024

AbstractIn Asian honeybees, virgin queens typically only mate during a single nuptial flight before founding a colony. This behavior is controlled by the queen-released mandibular pheromone (QMP). 9-oxo-(E)-2-decenoic acid (9-ODA), a key QMP component, acts as sex pheromone and attracts drones. However, how the queens prevent additional mating remains elusive. Here, we show that the secondary QMP component methyl p-hydroxybenzoate (HOB) released by mated queens inhibits male attraction to 9-ODA. Results from electrophysiology and in situ hybridization assay indicated that HOB alone significantly reduces the spontaneous spike activity of 9-ODA-sensitive neurons, and AcerOr11 is specifically expressed in sensilla placodea from the drone’s antennae, which are the sensilla that narrowly respond to both 9-ODA and HOB. Deorphanization of AcerOr11 in Xenopus oocyte system showed 9-ODA induces robust inward (regular) currents, while HOB induces inverse currents in a dose-dependent manner. This suggests that HOB potentially acts as an inverse agonist against AcerOr11.
Atmospheric river activity during the late Holocene exceeds modern range of variability in California
Authors: Knight Clarke A. , Anderson Lysanna , Presnetsova Liubov , Champagne Marie , Wahl David 
Published: April 2024

AbstractAtmospheric rivers are associated with some of the largest flood-producing precipitation events in western North America, particularly California. Insight into past extreme precipitation can be reconstructed from sedimentary archives on millennial timescales. Here we document atmospheric river activity near Leonard Lake, California, over 3,200 years, using a key metric of atmospheric river intensity, that is silicon/aluminum enriched layers that are highly correlated with modern records of integrated vapor transport. The late twentieth century had the highest median integrated vapor transport since the onset of the Medieval Climate Anomaly, with integrated vapor transport increasing during the Little Ice Age. The reconstruction suggests California has experienced pluvial episodes that exceeded any in the meteorologic instrumental era, with the largest episodes occurring two and three millennia ago. These results provide critical data to help avoid underestimation of potential risks and aid future planning scenarios.
Does Native Language Matter in Perceptual Ratings of Dysarthria?
Authors: Kim Yunjung , Thompson Austin , Lee Seung Jin 
Published: April 2024

Purpose:
Despite the general agreement that dysarthria characteristics are largely language-independent, few efforts have attempted a systematic comparison across languages. To examine the role of native languages in the perception of speech characteristics of dysarthria secondary to Parkinson's disease (PD), auditory-perceptual ratings of dysarthria, and confidence level of the judgments were compared between two listener groups: language-matched and language-crossed.

Method:
A total of 60 listeners (35 native speakers of Korean and 25 native speakers of American English) estimated speech abnormality for 20 speech dimensions using a visual analog scale method for both language-matched and language-crossed speech stimuli. Speech stimuli were passage readings of the respective languages obtained from individuals with and without PD.

Results:
For speech dimension ratings, eight of 20 speech dimensions revealed significant differences in response to PD speech between the two listener groups, for most of which, language-crossed listeners' estimation was lower (i.e., more impaired) than language-matched listeners. For confidence-level ratings, language-matched listeners were less confident in the ratings of speakers with PD compared to the language-crossed listeners.

Conclusions:
The data support both language-universal and language-specific aspects in perceiving dysarthria characteristics, such that native language plays a role, especially when rating articulatory- and rhythmic-related characteristics. The findings are discussed with respect to the role of linguistic information, such as phonetic inventories and prosodic structures, in perceiving dysarthria characteristics.
Sequential selection and calibration of video frames for 3D outdoor scene reconstruction
Authors: Sun Weilin , Li Manyi , Li Peng , Cao Xiao , Meng Xiangxu , Meng Lei 
Published: April 2024

Abstract3D scene understanding and reconstruction aims to obtain a concise scene representation from images and reconstruct the complete scene, including the scene layout, objects bounding boxes and shapes. Existing holistic scene understanding methods primarily recover scenes from single images, with a focus on indoor scenes. Due to the complexity of real‐world, the information provided by a single image is limited, resulting in issues such as object occlusion and omission. Furthermore, captured data from outdoor scenes exhibits characteristics of sparsity, strong temporal dependencies and a lack of annotations. Consequently, the task of understanding and reconstructing outdoor scenes is highly challenging. The authors propose a sparse multi‐view images‐based 3D scene reconstruction framework (SMSR). It divides the scene reconstruction task into three stages: initial prediction, refinement, and fusion stage. The first two stages extract 3D scene representations from each viewpoint, while the final stage involves selection, calibration and fusion of object positions and orientations across different viewpoints. SMSR effectively address the issue of object omission by utilizing small‐scale sequential scene information. Experimental results on the general outdoor scene dataset UrbanScene3D‐Art Sci and our proprietary dataset Software College Aerial Time‐series Images, demonstrate that SMSR achieves superior performance in the scene understanding and reconstruction.
Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements
Authors: Zhang Heng , Zhao Yangyang , Wang Yang , Liu Lin 
Published: April 2024

AbstractThis paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.
QoE‐driven multi‐UAV deployment scheme for emergency communication networks
Authors: Dai Haibo , Ju Yinghui , Liang Yiqun , Zhang Zhe , Xu Hao , Wang Baoyun 
Published: April 2024

AbstractWhen ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)‐mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line‐of‐sight and non‐line‐of‐sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs.
Optimization method for underwater sensor networks based on a virtual force‐oriented enhanced whale optimization algorithm
Authors: Tian Wei , Guo Jirui , Hou Rui 
Published: April 2024

AbstractThis paper presents an enhanced whale optimization algorithm based on virtual force to optimize coverage and address the problem of uneven coverage during the deployment of underwater sensor networks. This method is guided by the Lévy flight and virtual force algorithms and adopts a differential mutation strategy based on random walks. This improves the fitness of the initial population and population richness of the algorithm. The effectiveness of this method was verified through simulation.
DeepKalPose: An enhanced deep‐learning Kalman filter for temporally consistent monocular vehicle pose estimation
Authors: Di Bella Leandro , Lyu Yangxintong , Munteanu Adrian 
Published: April 2024

AbstractThis paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep‐learning‐based Kalman filter. By integrating a bi‐directional Kalman filter strategy utilizing forward and backward time‐series processing, combined with a learnable motion model to represent complex motion patterns, the method significantly improves pose accuracy and robustness across various conditions, particularly for occluded or distant vehicles. Experimental validation on the KITTI dataset confirms that DeepKalPose outperforms existing methods in both pose accuracy and temporal consistency.
A high gain and high radiation efficiency optical transparent Fabry–Perot cavity antenna
Authors: An Kang , Sun Peng , Chen Aixin 
Published: April 2024

AbstractThis letter proposes a novel high gain, high efficiency optically transparent Fabry–Perot Cavity antenna operating in millimetre‐wave. The antenna employs dual layers of transparent partially reflective surface and a transparent artificial magnetic conductor structure to enhance the gain and gain bandwidth. The reflection amplitude and phase have a low sensitivity to the sheet resistance of the transparent materials, which greatly reduce the requirement for the conductivity of transparent material and simplify the manufacturing process. The size of the antenna is 3.47λ0 × 3.47λ0 × 0.68λ0. A peak gain of 14.2 dBi, peak radiation efficiency of 67.1% and optical transparency of 81% are achieved.
Multistage AC transmission expansion planning including fault current‐limiting high‐temperature superconducting cables and multiple distributed generations to improve short‐circuit level and grid‐scale flexibility
Authors: Shivaie Mojtaba , Artis Reza , Padmanaban Sanjeevikumar 
Published: April 2024

AbstractThis paper proposes a new multistage AC model for transmission expansion planning that finds an optimal combination of transmission lines, fault current‐limiting high‐temperature superconducting cables, and multiple distributed generations (DGs). On this basis, the proposed model, from a new perspective, allows for simultaneous improvement of the short‐circuit level and grid‐scale flexibility (GFLX) under both normal and fault conditions. The objective function to be minimized includes not only the net present worth of the total investment and operation costs but also the congestion‐induced GFLX degradation measure. This model also takes the AC power balance and flow relationships, equipment capacity limits, nodal voltage bounds, DG penetration level limit, as well as discrete logical and financial restrictions together into account with the short‐circuit level constraint. To overcome the complexity of solving the resultant non‐convex mixed‐integer non‐linear optimization problem, a multi‐objective integer‐coded melody search algorithm is employed, followed by a fuzzy satisfying decision‐making mechanism to obtain the final optimal solution. The exhaustive case studies conducted on the IEEE 24‐ and 118‐bus test systems verify the efficacy of the newly developed model in terms of cost‐effectiveness, flexibility, and short‐circuit level suppression when facing different normal and fault conditions.
Photodetector array for measuring the spatial–temporal distribution characteristics of electric field in oil–pressboard insulation based on the Kerr electro‐optic effect
Authors: Gao Chunjia , Qi Bo , Li Chengrong , Zheng Yukung 
Published: April 2024

AbstractAccurate measurement and effective analysis of the space electric field and charge characteristics within the oil–pressboard insulation structure of converter transformer are essential for achieving precise insulation structure design. Presently, most electric field measurement methods relying on the Kerr electro‐optical effect are limited to single‐point measurements. Given the complexity of converter transformer insulation structures, including their large scale and the use of various materials, existing technologies and findings struggle to provide comprehensive electric field observations within the oil–pressboard region. After leveraging the Kerr electro‐optic effect, a high‐precision 32‐unit photodetector array has been developed to achieve regional measurements of spatial‐temporal electric field in oil–pressboard insulation. The array boasts a measurement spatial resolution of 1.4 mm2 and a sensitivity of 0.15 kV/mm, ensuring measurement accuracy exceeding 96.50%. Through practical measurements of the spatial electric field distribution within the oil–pressboard insulation under direct current voltage, the non‐uniform distribution of the electric field is effectively captured in oil. Notably, the maximum deviation in field strength at different positions within the transformer oil can reach 18.5%. Moreover, as the applied voltage increases, the unevenness coefficient of the field strength gradually rises, peaking at 1.19, which signifies a progressive expansion in the area affected by electric field distortion. By conducting tests to assess the dispersion of volume resistivity in samples from various positions within large sheets of pressboard, the variation in volume resistivity of materials is posited as one of the contributing factors to the non‐uniform distribution of electric field within the oil. The detailed accomplishments aim to provide essential technical and theoretical support for optimising insulation structure design of converter transformers.
Magnification factor for pile internal forces of pile‐supported wharves under bi‐directional ground motions
Authors: Feng Yunfen , Gao Shufei , Jing Fanli 
Published: April 2024

AbstractIn order to determine the pile internal forces of wharf under simultaneous seismic excitation in orthogonal horizontal directions with ease, the total internal forces can be obtained by multiplying the pile forces of two‐dimensional model under transverse earthquake loading with a magnification factor. The factors were computed from the seismic response of wharves having various aspect ratios (length to width) and eccentricity ratios when subjected to 30 pairs of bi‐directional earthquake records. Mean magnification factors and their corresponding dispersion are presented for all ground motion ensembles. The influence of aspect ratio and eccentricity ratio is evaluated and discussed. It is concluded that the magnification factor can be expressed as the function of aspect ratio and eccentricity ratio, and follows lognormal distribution. The proposed equation of magnification factor can be an alternative for internal force analysis of wharves under bi‐directional ground motion.
The design approach of an artificial intelligent (AI) medical system based on electronical health records (EHR) and priority segmentations
Authors: Akhtar Zarif Bin 
Published: April 2024

AbstractIn the rapidly evolving landscape of digital systems and modern technology, including computing and device peripherals, a paradigm shift is evident across various engineering fields. This transformation is characterized by the integration of cutting‐edge concepts such as Artificial Intelligence (AI), Machine Learning, Deep Learning, Cloud Computing, and Smart Digital Systems. Notably, these advancements hold significant promise in Biomedical Engineering, where they play crucial roles in comprehending human conditions and anatomy. Given the paramount importance of health, especially amidst rising diseases and health‐related challenges, there arises a pressing need for optimal solutions. This research focuses on the integration of Electronic Health Record (EHR) systems with AI to deliver efficient solutions. Specifically, the research targets issues related to prioritization and segment queue management, with the aim of enhancing overall proficiency and efficiency in healthcare operations. The research presents a prototype version developed, deployed, and experimentally evaluated to assess its performance in achieving the stated objectives. Through this investigation, the research seeks to contribute to the advancement of EHR systems and AI integration in healthcare, ultimately aiming to enhance patient care and healthcare delivery processes.