bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single Image 3D Reconstruction Algorithm Based on Parallel Double-Branch Pyramid Stacking Network
Authors: Ku Yintao , Zhang Yumei , Yang Honghong , Zhao Yanqing , Li Jinshan , Zhou Yulin , Wu Xiaojun 
Published: November 2023

Abstract
Limited visual information contained in single images and complex motion models of objects may lead to severe fragmentation and confusion of model backbone in the 3D reconstruction of objects. In order to fully extract feature information in a single image and reduce noise interference caused by environmental factors in 3D reconstruction, a Parallel Dual-Branch Pyramid Stacking Network (PDB-PSN) model is proposed. A parallel dual-branch network model is used to construct an encoder-decoder framework based on cascaded feature extraction, encode/decode high and low-resolution features in a single image, and then convert from 2D to 3D through implicit functions to achieve the 3D reconstruction of target objects in a single image. A cascaded feature extraction network is used as a low-resolution feature extraction network to extract global features of objects. In the high-resolution feature extraction branch, three concatenated hourglass networks and dilated convolutions are used in the hourglass network to increase the receptive field and obtain more global information in order to maintain the integrity of the reconstructed object limbs. A threshold processing module is set to remove irrelevant information and ensure the integrity of global information, and meanwhile, to reduce the interference of irrelevant noise information. Simulation experiments on a self-built terracotta dataset show that the PDB-PSN model can completely reconstruct the 3D model in a single image and effectively eliminate model fragmentation in the reconstruction results.
Small Object Detection Algorithm for Underwater Organisms based on Improved Transformer
Authors: Cao Ning 
Published: November 2023

Abstract
In view of the problem of difficult feature extraction and target missed detection, propose an improved transformer based underwater target detection algorithm. The proposed algorithm improves the linear embedding module of the Transformer to supplement the linear embedding information of small objects and builds the local relationship model of each layer feeling field, in order to achieve end-to-end accurate detection. An experiment on URPU underwater target detection data set is conducted. Compared with Swim Transformer, the proposed algorithm detection accuracy is 77.4%, 1. 5%, detection speed is 7 frame/s, 35.6%, the number is 30.4 MB, and compression is 84.1%. Experimental results have shown that the improved algorithm improves the performance of underwater small target detection
Variance-aware spatial decoupling attention for stereo matching
Authors: Zeng Pengcheng , Zhai Jucai , Ma Chihao , Liu Yang , Wang Xinan , Zhao Yong 
Published: November 2023

Abstract
The stereo matching algorithms which are based on feature matching build cost volume to regression disparity. Such cost volume shows a regular data distribution along the disparity dimension. The pixels with proper matching should have a single-peak distribution, while the pixels in the ill-conditioned regions have a multi-peak distribution. Using the variance of disparity dimension data, ill-conditioned regions can be identified. In this paper, an attention mechanism based on variance features is proposed, which promotes the neural network to deal with complicated regions. By decoupling the space of the image, a linear complexity attention mechanism is realized. It can be embedded into the algorithm based on feature matching with a slight increase in inference time, and a suitable trade-off is achieved in precision and speed. By embedding the module into BGNet, the accuracy of the designed model has been improved by 10 %, meanwhile, competitive performance has been achieved.
Conversion Control for Remote Sensing Stable Platforms for Transient and Continuous Disturbances
Authors: Zhou Jiaxing , Qixuan Huang , Chen Xiang , Yao Youxin 
Published: November 2023

Abstract
An airborne electro-optical stabilized platform is a device situated between the aircraft and the imaging components, which serves to isolate external disturbances and maintain imaging axis stability for the imaging components. In light of the impact of transient and continuous attitude changes of the aircraft on the axis-stabilized imaging in visible light unmanned aerial vehicle (UAV) remote sensing missions, a control method combining a proportional-integral (PI) controller and a backpropagation neural network (BPNN) PID conversion control was devised. The specific procedure involved the following steps: firstly, the transfer mechanism of external disturbances between platforms was derived using the state transition matrix through mechanistic modelling. Secondly, the multi-rigid body dynamics equations of the airborne electro-optical platform were established based on Newton’s laws of motion and Euler’s laws. Subsequently, the relationship between the stable platform rotation angle and external disturbances was obtained using Laplace transformation, and an analysis was conducted on the effects of different disturbances on the stable platform. Lastly, conversion control was incorporated into the system model for simulation analysis. The simulation results demonstrated that transient disturbances could be effectively and rapidly isolated by the PI controller, while continuous disturbances exhibited improved performance when handled by the neural network PID controller, thus validating the efficacy of the designed conversion control method.
A Mixed Safety and Reliability Evaluation Method for the Spacecraft Power Supply System
Authors: Wang Chao , Wang Heyu 
Published: November 2023

Abstract
A spacecraft power distribution system is a multi-parameter, strongly coupled complex system, and the safety of a spacecraft power distribution system directly affects the working status of the spacecraft. In order to achieve better research results in the safety analysis system of spacecraft energy systems, it is very necessary to establish an assessment method for the safety of spacecraft power distribution systems. Previous research mainly focuses on the safety assessment of power systems or aircraft power systems and has not formed a relatively complete assessment method for the safety of spacecraft power distribution systems. This paper proposes a comprehensive evaluation method for the safety of spacecraft power distribution systems. It combines objective and subjective evaluation methods to comprehensively evaluate the safety of spacecraft power distribution systems. The objective parameters of the spacecraft power distribution system are processed and objectively evaluated; Subjective evaluations are obtained using analytic hierarchy. The proposed method can reasonably evaluate the safety of spacecraft power distribution systems and assist in the selection of different spacecraft power distribution systems.
A PEDOT-Pt-TiO2 hybrid material synthesized by the casting method for photocatalytic hydrogen generation
Authors: Kobylański Marek P , Tercjak Agnieszka , Remita Hynd , Yuan Xiaojiao , Cavdar Onur , Gutierrez Junkal , Zaleska-Medynska Adriana 
Published: November 2023

Abstract
This study describes the synthesis and characterisation of a hybrid material consisting of titanium dioxide nanotube arrays (TiO2 NTs) modified by platinum nanoparticles (Pt-TiO2 NTs) via radiolysis and a conductive poly(3,4-ethylenedioxythiophene) (PEDOT) layer, for the first time. The NTs were fabricated by a two-step anodic oxidation process and exhibited different morphologies using electrolyte solutions with different water contents (2–10 vol%). The polymer layer of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) was coated on the Pt-TiO2 scaffold using the casting method. The PEDOT:PSS-PT-TiO2 NTs exhibited stability in the photocatalytic process after additional calcination which was carried out to remove the PSS part; the nanotubes with lengths of ∼3 μm exhibited the highest photocatalytic activity (∼4.5 × 10−3
μmol cm−2 of H2). Additionally, the samples obtained from electrolyte solutions containing 5 and 10 vol% water exhibited nanostructures with the highest catalytic activity.
Techno-economic optimization of renewable urea production for sustainable agriculture and CO2 utilization
Authors: Palys Matthew J , Daoutidis Prodromos 
Published: November 2023

Abstract
Urea is the most used nitrogen fertilizer due to its ease of storage, transportation, and application. It is made by combining ammonia and carbon dioxide (CO2), both of which are produced predominantly from fossil fuels at present. The recent momentum behind ammonia production using renewable-powered electrolysis offers an opportunity to both make urea in a more sustainable way and utilize CO2 from external sources. In this work, we present a techno-economic optimization model to minimize the cost of making urea in this way. The model allows for time-varying chemical production in response to renewable variability by simultaneously optimizing production facility design and hourly operation. We performed a case study for Minnesota considering the use of byproduct CO2 from bioethanol production. We found that the present-day levelized cost of renewable urea is between $268 mt−1 and $413 mt−1 at likely implementable production scales up to 250 000 mt yr−1. This is within the range of historical conventional urea prices while offering at least 78% carbon intensity reduction. Projecting to 2030, there is a clear economic case for renewable urea production with levelized cost as low as $135 mt−1 due to technology improvement and electrolysis manufacturing expansion, facilitating a urea production scale increase to 525 000 mt yr−1. Optimal facilities use wind energy, with hydrogen and ammonia production operating in a flexible, time-varying way to minimize battery and hydrogen storage capacities. Urea production operates near steady state due to the relatively low cost of intermediate ammonia buffer storage. A mix of imported methane and locally produced hydrogen are used to provide heat for steam consumed in the urea synthesis.
Computational study of shock diffraction over convex edges
Authors: Banerjee Debiprasad , Halder Pabitra 
Published: November 2023

Abstract
Numerical analysis of normal shock diffraction over various step corners is presented using an implicit second-order upwind least squares cell-based method. Slipstream angle calculation for various corner angles and Mach numbers, variation of pressure load and specific heat flux with incident shock Mach numbers and different step corners along the step wall, separation point movement, and reattachment of the streamlines are some of the key features of the present study. Detailed studies have been conducted about the perturbed region enclosed within the diffracted shock wave and the last running expansion wave with the increase in the incident shock Mach numbers. The finite Volume Method is used to solve the governing equations numerically for the simulation of the moving shock. Various flow characteristics such as secondary shock, contact surface, expansion wave, slipstream, vortex, etc are well captured. Apart from isopycnics; Mach contours, isobars, and isotherms are plotted as well. The reattachment region formed after the flow separation near the step edge is mentioned and corresponding lengths over various steps are presented. Separation point movement along the step wall is highlighted and a relevant increase in reattachment length is reported. The slipstream angles get increased with incident shock Mach numbers and with corner angles. However, the rate of increment of slipstream angle decreases gradually. The core of the vortices generated and the reattachment of the separated streamlines are indicated by the two downfalls in the heat flux values.
XHap: haplotype assembly using long-distance read correlations learned by transformers
Authors: Consul Shorya , Ke Ziqi , Vikalo Haris 
Published: November 2023

Summary
Reconstructing haplotypes of an organism from a set of sequencing reads is a computationally challenging (NP-hard) problem. In reference-guided settings, at the core of haplotype assembly is the task of clustering reads according to their origin, i.e. grouping together reads that sample the same haplotype. Read length limitations and sequencing errors render this problem difficult even for diploids; the complexity of the problem grows with the ploidy of the organism. We present XHap, a novel method for haplotype assembly that aims to learn correlations between pairs of sequencing reads, including those that do not overlap but may be separated by large genomic distances, and utilize the learned correlations to assemble the haplotypes. This is accomplished by leveraging transformers, a powerful deep-learning technique that relies on the attention mechanism to discover dependencies between non-overlapping reads. Experiments on semi-experimental and real data demonstrate that the proposed method significantly outperforms state-of-the-art techniques in diploid and polyploid haplotype assembly tasks on both short and long sequencing reads.

Availability and implementation
The code for XHap and the included experiments is available at https://github.com/shoryaconsul/XHap.
Joint analysis of multiple phenotypes for extremely unbalanced case–control association studies using multi-layer network
Authors: Xie Hongjing , Cao Xuewei , Zhang Shuanglin , Sha Qiuying 
Published: November 2023

Abstract

Motivation
Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP.

Results
We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with.

Availability and implementation
https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.
CSF 14-3-3β is associated with progressive cognitive decline in Alzheimer’s disease
Authors: Qiang Qiang , Skudder-Hill Loren , Toyota Tomoko , Huang Zhe , Wei Wenshi , Adachi Hiroaki ,   Alzheimer’s Disease Neuroimaging Initiative
Published: November 2023

Abstract
Alzheimer’s disease is a neurodegenerative disorder characterized pathologically by amyloid-beta plaques, tau tangles and neuronal loss. In clinical practice, the 14-3-3 isoform beta (β) is a biomarker that aids in the diagnosis of sporadic Creutzfeldt–Jakob disease. Recently, a proteomics study found increased CSF 14-3-3β levels in Alzheimer’s disease patients, suggesting a potential link between CSF 14-3-3β and Alzheimer’s disease. Our present study aimed to further investigate the role of CSF 14-3-3β in Alzheimer’s disease by analysing the data of 719 participants with available CSF 14-3-3β measurements from the Alzheimer’s Disease Neuroimaging Initiative. Higher CSF 14-3-3β levels were observed in the mild cognitive impairment group compared to the cognitively normal group, with the highest CSF 14-3-3β levels in the Alzheimer’s disease dementia group. This study also found significant associations between CSF 14-3-3β levels and CSF biomarkers of p-tau, t-tau, pTau/Aβ42 ratios and GAP-43, as well as other Alzheimer’s disease biomarkers such as Aβ-PET. An early increase in CSF 14-3-3β levels was observed prior to Aβ-PET–positive status, and CSF 14-3-3β levels continued to rise after crossing the Aβ-PET positivity threshold before reaching a plateau. The diagnostic accuracy of CSF 14-3-3β (area under the receiver operating characteristic curve = 0.819) was moderate compared to other established Alzheimer’s disease biomarkers in distinguishing cognitively normal Aβ pathology–negative individuals from Alzheimer’s disease Aβ pathology–positive individuals. Higher baseline CSF 14-3-3β levels were associated with accelerated cognitive decline, reduced hippocampus volumes and declining fluorodeoxyglucose-PET values over a 4-year follow-up period. Patients with mild cognitive impairment and high CSF 14-3-3β levels at baseline had a significantly increased risk [hazard ratio = 2.894 (1.599–5.238), P &lt; 0.001] of progression to Alzheimer’s disease dementia during follow-up. These findings indicate that CSF 14-3-3β may be a potential biomarker for Alzheimer’s disease and could provide a more comprehensive understanding of the underlying pathological changes of Alzheimer’s disease, as well as aid in the diagnosis and monitoring of disease progression.
Functional hub disruption emphasizes consciousness recovery in severe traumatic brain injury
Authors: Oujamaa Lydia , Delon-Martin Chantal , Jaroszynski Chloé , Termenon Maite , Silva Stein , Payen Jean-François , Achard Sophie 
Published: November 2023

Abstract
Severe traumatic brain injury can lead to transient or even chronic disorder of consciousness. To increase diagnosis and prognosis accuracy of disorder of consciousness, functional neuroimaging is recommended 1 month post-injury. Here, we investigated brain networks remodelling on longitudinal data between 1 and 3 months post severe traumatic brain injury related to change of consciousness. Thirty-four severe traumatic brain-injured patients were included in a cross-sectional and longitudinal clinical study, and their MRI data were compared to those of 20 healthy subjects. Long duration resting-state functional MRI were acquired in minimally conscious and conscious patients at two time points after their brain injury. The first time corresponds to the exit from intensive care unit and the second one to the discharge from post-intensive care rehabilitation ward. Brain networks data were extracted using graph analysis and metrics at each node quantifying local (clustering) and global (degree) connectivity characteristics. Comparison with brain networks of healthy subjects revealed patterns of hyper- and hypo-connectivity that characterize brain networks reorganization through the hub disruption index, a value quantifying the functional disruption in each individual severe traumatic brain injury graph. At discharge from intensive care unit, 24 patients’ graphs (9 minimally conscious and 15 conscious) were fully analysed and demonstrated significant network disruption. Clustering and degree nodal metrics, respectively, related to segregation and integration properties of the network, were relevant to distinguish minimally conscious and conscious groups. At discharge from post-intensive care rehabilitation unit, 15 patients’ graphs (2 minimally conscious, 13 conscious) were fully analysed. The conscious group still presented a significant difference with healthy subjects. Using mixed effects models, we showed that consciousness state, rather than time, explained the hub disruption index differences between minimally conscious and conscious groups. While severe traumatic brain-injured patients recovered full consciousness, regional functional connectivity evolved towards a healthy pattern. More specifically, the restoration of a healthy brain functional segregation could be necessary for consciousness recovery after severe traumatic brain injury. For the first time, extracting the hub disruption index directly from each patient’s graph, we were able to track the clinical alteration and subsequent recovery of consciousness during the first 3 months following a severe traumatic brain injury.
Classifying real-world macroscopic images in the primary–secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images
Authors: Carse Jacob , Süveges Tamás , Chin Gillian , Muthiah Shareen , Morton Colin , Proby Charlotte , Trucco Emanuele , Fleming Colin , McKenna Stephen 
Published: November 2023

Abstract

Background
The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability. Further research is necessary to determine the generalizability of DL algorithms across populations and acquisition settings.

Objectives
To assess the extent to which DL can generalize to nondermoscopic datasets acquired at the primary–secondary care interface in the National Health Service (NHS); to explore how to obtain a clinically satisfactory performance on nonstandardized, real-world local data without the availability of large diagnostically labelled local datasets; and to measure the impact of pretraining DL algorithms on external, public datasets.

Methods
Diagnostic macroscopic image datasets were created from previous referrals from primary to secondary care. These included 2213 images referred from primary care practitioners in NHS Tayside and 1510 images from NHS Forth Valley acquired by medical photographers. Two further datasets with identical diagnostic labels were obtained from sources in the public domain, namely the International Skin Imaging Collaboration (ISIC) dermoscopic dataset and the SD-260 nondermoscopic dataset. DL algorithms, specifically EfficientNets and Self-attention with Window-wise Inner-product based Network (SWIN) transformers, were trained using data from each of these datasets. Algorithms were also fine-tuned on images from the NHS datasets after pretraining on different data combinations, including the larger public-domain datasets. Receiver operating characteristic curves and the area under such curves (AUC) were used to assess performance.

Results
SWIN transformers tested on Forth Valley data had AUCs of 0.85 and 0.89 when trained on SD-260 and Forth Valley data, respectively. Training on SD-260 followed by fine tuning of Forth Valley data gave an AUC of 0.91. Similar effects of pretraining and tuning on local data were observed using Tayside data and EfficientNets. Pretraining on the larger dermoscopic image dataset (ISIC 2019) provided no additional benefit.

Conclusions
Pretraining on public macroscopic images followed by tuning to local data gave promising results. Further improvements are needed to afford deployment in real clinical pathways. Larger datasets local to the target domain might be expected to yield further improved performance.