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Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

Advances in Atmospheric Sciences 2024年第8期41卷 1559-1568页

作者： Astrid E.j.OGILVIE Leslie A.king Noel KEENLYSIDE François COUNILLON Brynhildur DAVIÐSDÓTTIR Níels EINARSSON Sergey GULEV Ke FAN Torben KOENIGK james R.MCGOODWIN Marianne H.RASMUSSON Shuting YANG Institute of Arctic and Alpine Research(INSTAAR) University of ColoradoBoulder CO 80303USA Stefansson Arctic Institute AkureyriÍS-600Iceland Royal Roads University BC V9B 5Y2Canada University of Bergen Bergen N-5007Norway Nansen Environmental and Remote Sensing Centre Bergen N-5007Norway University of Iceland Reykjavík 102Iceland Shirshov Institute of Oceanology Moscow 117218Russia Moscow State University Moscow 11991Russia Chinese Academy of Sciences Beijing 100045China Swedish Meteorological and Hydrological Institute SE-60176 NorrköpingSweden Danish Meteorological Institute CopenhagenØ2100Denmark Obukhov Institute of Atmospheric Physics RAS119017MoscowRussia

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://***/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

关键词： Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

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Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1129-1142页

作者： Hasan j.Alyamani Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical ***,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual *** study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease *** initiate this endeavor,a multi-faceted approach is embarked *** dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 *** augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification *** outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

关键词： Ulcerative colitis deep learning CLAHE transfer learning geometric transformations

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Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

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Computers, Materials & Continua 2024年第4期79卷 1301-1317页

作者： Sharaf j.Malebary Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityP.O.Box 344Rabigh21911Saudia Arabia

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity ***, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training ***, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation *** experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinician

关键词： Brain tumor Hybrid U-Net CLAHE transfer learning MRI images

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A decomposition of light’s spin angular momentum density

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Light(Science & Applications) 2024年第8期13卷 1578-1589页

作者： Alex j.Vernon Sebastian Golat Claire Rigouzzo Eugene A.Lim Francisco j.Rodríguez-Fortuño Department of Physics King’s College LondonStrandLondon WC2R 2LSUK London Centre for Nanotechnology LondonUK

Light carries intrinsic spin angular momentum(SAM)when the electric or magnetic field vector rotates over time.A familiar vector equation calculates the direction of light’s SAM density using the right-hand rule with reference to the electric and magnetic polarisation *** Maxwell’s equations,this vector equation can be decomposed into a sum of two distinct terms,akin to the well-known Poynting vector decomposition into orbital and spin *** present the first general study of this spin decomposition,showing that the two terms,which we call canonical and Poynting spin,are chiral analogies to the canonical and spin momenta of light in its interaction with *** canonical momentum,canonical spin is directly *** canonical and Poynting spin incorporate spatial variation of the electric and magnetic fields and are influenced by optical *** decomposition allows us to show that a linearly polarised vortex beam,which has no total SAM,can nevertheless exert longitudinal chiral pressure due to equal and opposite canonical and Poynting spins.

关键词： canonical momentum angular

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Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning – CORRIGENDUM

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High Power Laser Science and Engineering 2024年第1期12卷 116-116页

作者： j.Goodman M.king E.j.Dolier R.Wilson R.j.Gray P.McKenna 不详

Due to an isolated error in the 3D simulation parameters,the laser energy and intensity(calculated using the energy)values were incorrectly stated as 10.9 j and 3×10^(22) W cm^(−2),respectively,in Sections 3.3,7 and *** correct values are 39.8 j and 1.1×10^(23) W cm^(−2).Similarly,the values stated for the higher energy case,109 j and 3×10^(23) W cm^(−2) in Section 7,should be 398 j and 1.1×10^(24) W cm^(−2),respectively.

关键词： Bayesian optimization gamma rays laser-solid interactions machine learning radiation reaction

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治疗后早期MRI预测肝细胞癌放射性肝段切除术后的长期反应

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国际医学放射学杂志 2024年第2期 250-251页

作者： D.Stocker M.j.king M.E.Homsi j.Gnerre B.Marinelli M.Wurnig 朱婉秋

摘要目的应用钇-90放射性肝段切除术治疗早期肝细胞癌（HCC）具有重要意义，然而治疗后MRI对疗效评估的价值尚不明确。旨在评估放射性肝段切除术后早期MRI疗

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Putting PLX5622 into perspective:microglia in central nervous system viral infection

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Neural Regeneration Research 2023年第6期18卷 1269-1270页

作者： Alanna G.Spiteri Nicholas j.C.king Viral Immunopathology Laboratory InfectionImmunity and Inflammation Research ThemeSchool of Medical SciencesFaculty of Medicine and HealthThe University of SydneySydneyNSWAustralia Sydney Cytometry The University of Sydney and Centenary InstituteSydneyNSWAustralia Ramaciotti Facility for Human Systems Biology The University of Sydney and Centenary InstituteSydneyNSWAustralia Charles Perkins Centre The University of SydneyAustralia The University of Sydney Institute for Infectious Diseases The University of SydneySydneyNSWAustralia The University of Sydney Nano Institute the University of SydneySydneyNSWAustralia

Elucidating the exact contribution of microglia to central nervous system(CNS)pathology has historically been extremely *** resident parenchymal myeloid cells are considered to have critical roles as frontline responders during pathogen invasion and CNS ***,understanding the precise temporal kinetics of microglial function is central to the evolution of novel therapeutics for disease intervention and/or resolution(Spiteri et al.,2022a).The development of PLX5622,a colony-stimulating factor 1 receptor(CSF-1R)inhibitor typically formulated into a rodent chow for simple oral administration has facilitated exploration of microglial functions in disease(Spangenberg et al.,2019).

关键词： invasion system precise

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Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1469-1483页

作者： Laila M.Halman Mohammed j.F.Alenazi Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11451Saudi Arabia

The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and *** this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe *** attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human *** can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or *** this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various *** propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS *** then evaluate the accuracy and performance of the proposed TBDC *** technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

关键词： Network resilience network management attack prediction software defined networking(SDN) distributed denial of service(DDoS) healthcare

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Automating selective area electron diffraction phase identification using machine learning

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journal of Materiomics 2024年第4期10卷 896-905页

作者： M.Mika N.Tomczak C.Finney j.Carter A.Aitkaliyeva Nuclear Engineering Program in Department of Materials Science University of Florida100 Rhines HallGainesville32611FLUSA Department of Materials Science&Engineering Case Western Reserve University2111 Martin Luther King Jr DrCleveland44106OHUSA

Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a ***,the manual identification of collected patterns can be a significant bottleneck in the overall phase classification *** this work,we utilize the recent advances in computer vision and machine learning(ML)to automate the indexing of SAED *** performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium *** most successful approach trained a neural network(NN)to make a classification of the phase and zone axis,and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase *** results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.

关键词： Selective area electron diffraction Machine learning Phase identification Metallic fuels Pu alloys

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3D Printing of Polylactic Acid Bioplastic–Carbon Fibres and Twisted Kevlar Composites Through Coextrusion Using Fused Deposition Modeling

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journal of Renewable Materials 2020年第12期8卷 1671-1680页

作者： j.Y.Tey W.H.Yeo Y.j.king W.O.Ding Department of Mechanical and Material Engineering Lee Kong Chian Faculty of Engineering and ScienceUniversiti Tunku Abdul RahmanJalan Sungai LongBandar Sungai LongCheras43000 KajangSelangorMalaysia

Polylactic acid(PLA)bioplastic is a common material used in Fused Deposition Modeling(FDM)3D *** is biodegradable and environmentally friendly biopolymer which made out of ***,it exhibits weak mechanical properties which reduced its usability as a functional prototype in a real-world *** the present study,two PLA composites are created through coextruded with 3K carbon fibres and twisted Kevlar string(as core fibre)to form a fibre reinforced parts(FRP).The mechanical strength of printed parts was examined using ASTM D638 standard with a strain rate of 1 mm/*** has been demonstrated that the FRPs coextruded with 3K carbon fibres had achieved significant improvement in Young’s modulus(+180.6%,9.205 GPa),ultimate tensile strength(+175.3%,103 MPa)and maximum tensile strain(+21.6%,1.833%).Although the Young’s modulus of Kevlar FRP was found to be similar to as compared to unreinforced PLA(~3.29 GPa),it has gained significant increment in terms of maximum tensile strain(+179.7%,104.64 MPa),and maximum tensile strain(+257%,5.384%).Thus,this study revealed two unique composite materials,in which the 3K carbon FRP can offer stiff and high strength structure while Kevlar FRP offers similar strength but at a higher elasticity.

关键词： 3D printing Kevlar fibre 3K carbon fibre coextruded reinforced plastic polylactic acid bioplastic

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