| 1. |
- Pecunia, Vincenzo, et al.
(författare)
-
Roadmap on energy harvesting materials
- 2023
-
Ingår i: Journal of Physics. - : IOP Publishing. - 2515-7639. ; 6:4
-
Tidskriftsartikel (refereegranskat)abstract
- Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.
|
|
| 2. |
- Yu, Wenjin, et al.
(författare)
-
Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
-
Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
|
|
| 3. |
- Chen, Yan, et al.
(författare)
-
Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation
- 2024
-
Ingår i: European Journal of Finance. - : ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD. - 1351-847X .- 1466-4364.
-
Tidskriftsartikel (refereegranskat)abstract
- We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions.
|
|
| 4. |
- Chen, Yan, et al.
(författare)
-
Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China
- 2023
-
Ingår i: Global Finance Journal. - : ELSEVIER. - 1044-0283 .- 1873-5665. ; 58
-
Tidskriftsartikel (refereegranskat)abstract
- This study examines the connectedness and risk spillovers between Chinese FinTech and traditional financial institutions by using quantile-based vector autoregression (QVAR) networks. Specifically, by using daily data from January 2014 to June 2022, we focus on system-, sector-, and institution-level quantile connectedness characteristics, with the following findings. At the system level, the QVAR networks linking FinTech and traditional financial institutions are more connected at the extreme quantiles than at the median quantile. At the sector level, banks, real estate firms, and FinTech sectors act as net risk receivers, whereas securities and insurers act as net risk emitters. At the institutional level, risk transmission and reception of institutions significantly increase when market conditions rapidly change. We also investigate the determinants of quantile connectedness by using an exponential random graph model and find that (i) across different quantiles, the book-to-market and return on equity of institutions have a positive impact on their risk spillovers; (ii) at the extreme quantiles, the book-to-market is more pronounced than the return on equity; and (iii) at the median quantile, banks and FinTech institutions are more connected than insurers, real estate firms, securities, and other financials.
|
|
| 5. |
- Feng, Shaohong, et al.
(författare)
-
Dense sampling of bird diversity increases power of comparative genomics
- 2020
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 587:7833
-
Tidskriftsartikel (refereegranskat)abstract
- Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity(1-4). Sparse taxon sampling has previously been proposed to confound phylogenetic inference(5), and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird families-including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confidently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specific variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will offer new perspectives on evolutionary processes in cross-species comparative analyses and assist in efforts to conserve species. A dataset of the genomes of 363 species from the Bird 10,000 Genomes Project shows increased power to detect shared and lineage-specific variation, demonstrating the importance of phylogenetically diverse taxon sampling in whole-genome sequencing.
|
|
| 6. |
- Hu, Gang, et al.
(författare)
-
CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning
- 2024
-
Ingår i: Biomimetics. - : MDPI. - 2313-7673. ; 9:7
-
Tidskriftsartikel (refereegranskat)abstract
- The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the L & eacute;vy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.
|
|
| 7. |
- Ishigaki, Kazuyoshi, et al.
(författare)
-
Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis
- 2022
-
Ingår i: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 54:11, s. 1640-1651
-
Tidskriftsartikel (refereegranskat)abstract
- Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
|
|
| 8. |
- Li, Xing-Yu, et al.
(författare)
-
Comparative Study of Dayside Pulsating Auroras Induced by Ultralow-Frequency Waves
- 2023
-
Ingår i: Universe. - : MDPI AG. - 2218-1997. ; 9:6
-
Tidskriftsartikel (refereegranskat)abstract
- Pulsating auroras are usually observed with ultralow-frequency (ULF) waves in the Pc 3-5 band (period 10-600 s). These auroras are thought to result from interactions between energetic electrons and chorus waves, but their relationship with ULF waves remains an open question. In this study, we investigated this question by conducting a comparative study on two ULF wave events with pulsating auroras observed near the magnetic footprints. Conjugate observations from the Magnetospheric Multiscale mission and the Chinese Yellow River Station were used. In both events, lower-band chorus waves were observed, which were suggested to be connected with the auroral pulsations by wavelet analysis. The intensity of these waves oscillates at the period of the ULF waves, but the physics laid behind them differs by events. During the event of 22 January 2019, compressional ULF waves changed the threshold for the whistler anisotropy instability periodically, affecting the emission of chorus waves. In the event on 10 January 2016, poloidal ULF waves modulated the chorus wave generation by regulating electron temperature anisotropy through drift resonance. ULF waves in these events may originate from perturbations in the solar wind. We highlight the role of ULF waves in the solar wind-magnetosphere-ionosphere coupling, which requires further study.
|
|
| 9. |
- Liu, Xuan, et al.
(författare)
-
Increased southerly and easterly water vapor transport contributed to the dry-to-wet transition of summer precipitation over the Three-River Headwaters in the Tibetan Plateau
- 2023
-
Ingår i: Advances in Climate Change Research. - 1674-9278 .- 2524-1761. ; 14:4, s. 502-510
-
Tidskriftsartikel (refereegranskat)abstract
- The Three-River Headwaters (TRH) region in the Tibetan Plateau is vulnerable to climate change; changes in summer (June–August) precipitation have a significant impact on water security and sustainability in both local and downstream areas. However, the changes in summer precipitation of different intensities over the TRH region, along with their influencing factors, remain unclear. In this study, we used observational and ERA5 reanalysis data and employed a precipitation categorization and water vapor budget analysis to quantify the categorized precipitation variations and investigate their possible linkages with the water vapor budget. Our results showed an increasing trend in summer precipitation at a rate of 0.9 mm per year (p < 0.1) during 1979–2020, with a significant dry-to-wet transition in 2002. The category ‘very heavy precipitation’ (≥10 mm d−1) contributed 65.1% of the increased summer precipitation, which occurred frequently in the northern TRH region. The dry-to-wet transition was caused by the effects of varied atmospheric circulations in each subregion. Southwesterly water vapor transport through the southern boundary was responsible for the increased net water vapor flux in the western TRH region (158.2%), while southeasterly water vapor transport through the eastern boundary was responsible for the increased net water vapor flux in the central TRH (155.2%) and eastern TRH (229.2%) regions. Therefore, we inferred that the dry-to-wet transition of summer precipitation and the increased ‘very heavy precipitation’ over the TRH was caused by increased easterly and southerly water vapor transport.
|
|
| 10. |
- Liu, Xiaowan, et al.
(författare)
-
Measurements and modelling of dynamic stiffness of a railway vehicle primary suspension element and its use in a structure-borne noise transmission model
- 2021
-
Ingår i: Applied Acoustics. - : Elsevier BV. - 0003-682X .- 1872-910X. ; 182
-
Tidskriftsartikel (refereegranskat)abstract
- The noise inside railway vehicles is transmitted by both structure-borne and airborne paths and, although there are many sources, the rolling noise is often the most important. This paper focuses on the structure-borne transmission of rolling noise in a metro vehicle. Measurements are presented first of the vertical and lateral dynamic stiffness of a primary suspension element consisting of conical rubber/metal elements. Results are presented for various constant preloads over the frequency range 60600 Hz. An analytical model of the suspension element is also developed, based on a mass-spring system and including wave motion within the rubber elements. The dynamic stiffness results are used in a finite element model of the running gear, consisting of the bogie frame, wheelsets and suspension elements. The excitation is provided by the combined wheel/rail roughness at the contact point. This model is used to calculate the blocked forces at the connection points between the secondary suspension elements and the car body. The blocked forces are combined with measured vibro-acoustic transfer functions from these mounting points to the vehicle interior to determine the structure-borne noise inside the vehicle. The proposed methodology is validated against measurements during operation in terms of acceleration levels, blocked forces and structure-borne noise levels inside the vehicle, showing reasonably good agreement. Including the dynamic stiffness for the primary suspension leads to improved agreement between 100 and 500 Hz compared with using a constant stiffness.
|
|
