| 1. |
- Beal, Jacob, et al.
(author)
-
Robust estimation of bacterial cell count from optical density
- 2020
-
In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
-
Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
|
|
| 2. |
- Jin, Ying-Hui, et al.
(author)
-
Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19 : An evidence-based clinical practice guideline (updated version)
- 2020
-
In: Military Medical Research. - : Springer Science and Business Media LLC. - 2054-9369. ; 7:1
-
Journal article (peer-reviewed)abstract
- The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)"; now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis (including agents and Traditional Chinese Medicine (TCM) agents), diagnosis (including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest x-ray, and CT features of asymptomatic infections), treatments (including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, qingfei paidu decoction, lianhua qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)), and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients.
|
|
| 3. |
- Kristan, Matej, et al.
(author)
-
The first visual object tracking segmentation VOTS2023 challenge results
- 2023
-
In: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
-
Conference paper (peer-reviewed)abstract
- The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
|
|
| 4. |
- Ning, Enhao, et al.
(author)
-
Occluded person re-identification with deep learning : A survey and perspectives
- 2024
-
In: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 239
-
Research review (peer-reviewed)abstract
- Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attention from researchers. Over the past few years, several occlusion-solving person Re-ID methods have been proposed, tackling various sub-problems arising from occlusion. However, there is a lack of comprehensive studies that compare, summarize, and evaluate the potential of occluded person Re-ID methods in detail. In this review, we commence by offering a meticulous overview of the datasets and evaluation criteria utilized in the realm of occluded person Re-ID. Subsequently, we undertake a rigorous scientific classification and analysis of existing deep learning-based occluded person Re-ID methodologies, examining them from diverse perspectives and presenting concise summaries for each approach. Furthermore, we execute a systematic comparative analysis among these methods, pinpointing the state-of-the-art solutions, and provide insights into the future trajectory of occluded person Re-ID research. © 2023 Elsevier Ltd
|
|
| 5. |
|
|
| 6. |
- Albacete, Javier L., et al.
(author)
-
Predictions for p + Pb Collisions at sN N = √5 TeV : Comparison with Data
- 2016
-
In: International Journal of Modern Physics E. - 0218-3013. ; 25:9
-
Research review (peer-reviewed)abstract
- Predictions made in Albacete et al. [Int. J. Mod. Phys. E 22 (2013) 1330007] prior to the LHC p+Pb run at sNN = 5 TeV are compared to currently available data. Some predictions shown here have been updated by including the same experimental cuts as the data. Some additional predictions are also presented, especially for quarkonia, that were provided to the experiments before the data were made public but were too late for the original publication.
|
|
| 7. |
- Hao, Meilan, et al.
(author)
-
Coarse to fine-based image–point cloud fusion network for 3D object detection
- 2024
-
In: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 112, s. 1-12
-
Journal article (peer-reviewed)abstract
- Enhancing original LiDAR point cloud features with virtual points has gained widespread attention in multimodal information fusion. However, existing methods struggle to leverage image depth information due to the sparse nature of point clouds, hindering proper alignment with camera-derived features. We propose a novel 3D object detection method that refines virtual point clouds using a coarse-to-fine approach, incorporating a dynamic 2D Gaussian distribution for better matching and a dynamic posterior density-aware RoI network for refined feature extraction. Our method achieves an average precision (AP) of 90.02% for moderate car detection on the KITTI validation set, outperforming state-of-the-art methods. Additionally, our approach yields AP scores of 86.58% and 82.16% for moderate and hard car detection categories on the KITTI test set, respectively. These results underscore the effectiveness of our method in addressing point cloud sparsity and enhancing 3D object detection performance. The code is available at https://github.com/ZhongkangZ/LidarIG. © 2024 Elsevier B.V.
|
|
| 8. |
- Hao, Manzhao, et al.
(author)
-
Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease.
- 2013
-
In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 8:11
-
Journal article (peer-reviewed)abstract
- Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3-C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics demonstrated a frequency dependent damping on tremor, which may prevent tremor above 8 Hz to occur.
|
|
| 9. |
- Huang, Shoushuang, et al.
(author)
-
Encapsulating Fe2O3 Nanotubes into Carbon-Coated Co9S8 Nanocages Derived from a MOFs-Directed Strategy for Efficient Oxygen Evolution Reactions and Li-Ions Storage
- 2021
-
In: Small. - : Wiley-V C H Verlag GMBH. - 1613-6810 .- 1613-6829. ; 17:51
-
Journal article (peer-reviewed)abstract
- The development of high-efficiency, robust, and available electrode materials for oxygen evolution reaction (OER) and lithium-ion batteries (LIBs) is critical for clean and sustainable energy system but remains challenging. Herein, a unique yolk-shell structure of Fe2O3 nanotube@hollow Co9S8 nanocage@C is rationally prepared. In a prearranged sequence, the fabrication of Fe2O3 nanotubes is followed by coating of zeolitic imidazolate framework (ZIF-67) layer, chemical etching of ZIF-67 by thioacetamide, and eventual annealing treatment. Benefiting from the hollow structures of Fe2O3 nanotubes and Co9S8 nanocages, the conductivity of carbon coating and the synergy effects between different components, the titled sample possesses abundant accessible active sites, favorable electron transfer rate, and exceptional reaction kinetics in the electrocatalysis. As a result, excellent electrocatalytic activity for alkaline OER is achieved, which delivers a low overpotential of 205 mV at the current density of 10 mA cm(-2) along with the Tafel slope of 55 mV dec(-1). Moreover, this material exhibits excellent high-rate capability and excellent cycle life when employed as anode material of LIBs. This work provides a novel approach for the design and the construction of multifunctional electrode materials for energy conversion and storage.
|
|
| 10. |
- Huang, Shoushuang, et al.
(author)
-
Synergistically modulating electronic structure of NiS2 hierarchical architectures by phosphorus doping and sulfur-vacancies defect engineering enables efficient electrocatalytic water splitting
- 2021
-
In: Chemical Engineering Journal. - : ELSEVIER SCIENCE SA. - 1385-8947 .- 1873-3212. ; 420
-
Journal article (peer-reviewed)abstract
- The synergistic achievement of heteroatom doping, defect engineering and appropriate structural design is efficient to adjust and boost the catalytic performance of catalysts yet challenging. Herein, phosphorus (P)-doped NiS2 hierarchical architectures with sulfur vacancies are synthesized via a Prussian-blue-analogue-sacrificed strategy followed by a phosphidation process. By modulation of P doping and sulfur vacancies, the optimal catalyst manifests outstanding electrocatalytic activities, affording low overpotentials of 73 mV at 10 mA cm-2 for hydrogen evolution reaction (HER), and 255 mV at 20 mA cm-2 for oxygen evolution reaction (OER), respectively. Density functional theory calculations certify that the P dopant not only serves as the new active sites, but also activates the electrochemical activity of neighboring Ni and S sites. Moreover, the synergistic effect of P-doping and sulfur vacancies further improve electrochemical activities of HER and OER by optimizing the adsorption free energy of hydrogen (Delta GH*) and oxygen-containing intermediates (OH*, O* and OOH*), respectively. This finding provides a directive strategy to achieve efficient non-noble metal catalysts for energy conversion and storage.
|
|
| 11. |
- Li, Guoshuai, et al.
(author)
-
Site selection of desert solar farms based on heterogeneous sand flux
- 2024
-
In: npj Climate and Atmospheric Science. - 2397-3722. ; 7:1
-
Journal article (peer-reviewed)abstract
- Site selection for building solar farms in deserts is crucial and must consider the dune threats associated with sand flux, such as sand burial and dust contamination. Understanding changes in sand flux can optimize the site selection of desert solar farms. Here we use the ERA5-Land hourly wind data with 0.1° × 0.1° resolution to calculate the yearly sand flux from 1950 to 2022. The mean of sand flux is used to score the suitability of global deserts for building solar farms. We find that the majority of global deserts have low flux potential (≤ 40 m3 m-1 y-1) and resultant flux potential (≤ 2.0 m3 m-1 y-1) for the period 1950–2022. The scoring result demonstrates that global deserts have obvious patchy distribution of site suitability for building solar farms. Our study contributes to optimizing the site selection of desert solar farms, which aligns with the United Nations sustainability development goals for achieving affordable and clean energy target by 2030.
|
|
| 12. |
- Li, Si, et al.
(author)
-
Coordinated alpha and gamma control of muscles and spindles in movement and posture
- 2015
-
In: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 9
-
Journal article (peer-reviewed)abstract
- Mounting evidence suggests that both a and gamma motoneurons are active during movement and posture, but how does the central motor system coordinate the alpha-gamma controls in these tasks remains sketchy due to lack of in vivo data. Here a computational model of alpha-gamma control of muscles and spindles was used to investigate a -gamma integration and coordination for movement and posture. The model comprised physiologically realistic spinal circuitry, muscles, proprioceptors, and skeletal biomechanics. In the model, we divided the cortical descending commands into static and dynamic sets, where static commands (alpha(s) and gamma(s)) were for posture maintenance and dynamic commands (alpha(d) and gamma(d)) were responsible for movement. We matched our model to human reaching movement data by straightforward adjustments of descending commands derived from either minimal-jerk trajectories or human EMGs. The matched movement showed smooth reach-to-hold trajectories qualitatively close to human behaviors, and the reproduced EMGs showed the classic tri-phasic patterns. In particular, the function of gamma(d) was to gate the alpha(d) command at the propriospinal neurons (PN) such that antagonistic muscles can accelerate or decelerate the limb with proper timing. Independent control of joint position and stiffness could be achieved by adjusting static commands. Deefferentation in the model indicated that accurate static commands of as and gamma(s) are essential to achieve stable terminal posture precisely, and that the gamma(d) command is as important as the alpha(d) command in controlling antagonistic muscles for desired movements. Deafferentation in the model showed that losing proprioceptive afferents mainly affected the terminal position of movement, similar to the abnormal behaviors observed in human and animals. Our results illustrated that tuning the simple forms of alpha-gamma commands can reproduce a range of human reach-to-hold movements, and it is necessary to coordinate the set of alpha-gamma descending commands for accurate and stable control of movement and posture.
|
|
| 13. |
- Liang, Guojun, et al.
(author)
-
Semantics-aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting
- 2023
-
In: IEEE Transactions on Vehicular Technology. - Piscataway, NJ : IEEE. - 0018-9545 .- 1939-9359. ; 72:6, s. 7796-7809
-
Journal article (peer-reviewed)abstract
- Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to extract the dynamic spatio-temporal relationships of traffic data and to interpret trip patterns or motivation behind traffic flows. In this paper, we propose a novel Semantics-aware Dynamic Graph Convolutional Network (SDGCN) for traffic flow forecasting. A sparse, state-sharing, hidden Markov model is applied to capture the patterns of traffic flows from sparse trajectory data; this way, latent states, as well as transition matrices that govern the observed trajectory, can be learned. Consequently, we can build dynamic Laplacian matrices adaptively by jointly considering the trip pattern and motivation of traffic flows. Moreover, high-order Laplacian matrices can be obtained by a newly designed forward algorithm of low time complexity. GCN is then employed to exploit spatial features, and Gated Recurrent Unit (GRU) is applied to exploit temporal features. We conduct extensive experiments on three real-world traffic datasets. Experimental results demonstrate that the prediction accuracy of SDGCN outperforms existing traffic flow forecasting methods. In addition, it provides better explanations of the generative Laplace matrices, making it suitable for traffic flow forecasting in large cities and providing insight into the causes of various phenomena such as traffic congestion. The code is publicly available at https://github.com/gorgen2020/SDGCN. © 2023 IEEE.
|
|
| 14. |
- Liang, Xiaoyong, et al.
(author)
-
Colloidal metal oxide nanocrystals as charge transporting layers for solution-processed light-emitting diodes and solar cells
- 2017
-
In: Chemical Society Reviews. - : ROYAL SOC CHEMISTRY. - 0306-0012 .- 1460-4744. ; 46:6, s. 1730-1759
-
Research review (peer-reviewed)abstract
- Colloidal metal oxide nanocrystals offer a unique combination of excellent low-temperature solution processability, rich and tuneable optoelectronic properties and intrinsic stability, which makes them an ideal class of materials as charge transporting layers in solution-processed light-emitting diodes and solar cells. Developing new material chemistry and custom-tailoring processing and properties of charge transporting layers based on oxide nanocrystals hold the key to boosting the efficiency and lifetime of all-solution-processed light-emitting diodes and solar cells, and thereby realizing an unprecedented generation of high-performance, low-cost, large-area and flexible optoelectronic devices. This review aims to bridge two research fields, chemistry of colloidal oxide nanocrystals and interfacial engineering of optoelectronic devices, focusing on the relationship between chemistry of colloidal oxide nanocrystals, processing and properties of charge transporting layers and device performance. Synthetic chemistry of colloidal oxide nanocrystals, ligand chemistry that may be applied to colloidal oxide nanocrystals and chemistry associated with post-deposition treatments are discussed to highlight the ability of optimizing processing and optoelectronic properties of charge transporting layers. Selected examples of solution-processed solar cells and light-emitting diodes with oxide-nanocrystal charge transporting layers are examined. The emphasis is placed on the correlation between the properties of oxide-nanocrystal charge transporting layers and device performance. Finally, three major challenges that need to be addressed in the future are outlined. We anticipate that this review will spur new material design and simulate new chemistry for colloidal oxide nanocrystals, leading to charge transporting layers and solution-processed optoelectronic devices beyond the state-of-the-art.
|
|
| 15. |
- Liu, Na, et al.
(author)
-
The Critical Role of Dysregulated RhoB Signaling Pathway in Radioresistance of Colorectal Cancer
- 2019
-
In: International Journal of Radiation Oncology, Biology, Physics. - : Elsevier. - 0360-3016 .- 1879-355X. ; 104:5, s. 1153-1164
-
Journal article (peer-reviewed)abstract
- PurposeTo explore whether the Rho protein is involved in the radioresistance of colorectal cancer and investigate the underlying mechanisms.Methods and MaterialsRho GTPase expression was measured after radiation treatment in colon cancer cells. RhoB knockout cell lines were established using the CRISPR/Cas9 system. In vitro assays and zebrafish embryos were used for analyzing radiosensitivity and invasive ability. Mass cytometry was used to detect RhoB downstream signaling factors. RhoB and Forkhead box M1 (FOXM1) expression were detected by immunohistochemistry in rectal cancer patients who participated in a radiation therapy trial.ResultsRhoB expression was related to radiation resistance. Complete depletion of the RhoB protein increased radiosensitivity and impaired radiation-enhanced metastatic potential in vitro and in zebrafish models. Probing signaling using mass cytometry–based single-cell analysis showed that the Akt phosphorylation level was inhibited by RhoB depletion after radiation. FOXM1 was downregulated in RhoB knockout cells, and the inhibition of FOXM1 led to lower survival rates and attenuated migration and invasion abilities of the cells after radiation. In the patients who underwent radiation therapy, RhoB overexpression was related to high FOXM1, late Tumor, Node, Metastasis stage, high distant recurrence, and poor survival independent of other clinical factors.ConclusionsRhoB plays a critical role in radioresistance of colorectal cancer through Akt and FOXM1 pathways.
|
|
| 16. |
- Miao, Junfeng, et al.
(author)
-
A blockchain-enabled privacy-preserving authentication management protocol for Internet of Medical Things
- 2024
-
In: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 237, Part A
-
Journal article (peer-reviewed)abstract
- Over the last decade, with the increasing popularity and usage of the internet of things worldwide, Internet of Medical Things (IoMT) has emerged as a key technology of the modern era. IoMT uses Artificial Intelligence, 5G, big data, edge computing, and blockchain to provide users with electronic medical services. However, it may face several security threats and attacks over an insecure public network. Therefore, to protect sensitive medical data in IoMT, it is necessary to design a secure and efficient authentication protocol. In this study, we propose a privacy-preserving authentication management protocol based on blockchain. The protocol uses a blockchain to store identities and related parameters to assist communication entities in authentication. In addition, the protocol adopts a three-factor authentication method and introduces Chebyshev chaotic map to ensure the security of user login and authentication. Formal security proof and analysis using the random oracle model and Burrows-Abadi-Needham logic show that the proposed protocol is secure. Moreover, we use informal security analysis to demonstrate that the protocol can resist various security attacks. The functional comparison shows that the protocol has high security. Through performance analysis and comparison with other protocols, the proposed protocol can increase computation overhead, communication overhead, and storage overhead by up to 39.8%, 93.6%, and 86.7%,respectively. © 2023 Elsevier Ltd
|
|
| 17. |
|
|
| 18. |
- Ning, Weihua, et al.
(author)
-
Thermochromic Lead-Free Halide Double Perovskites
- 2019
-
In: Advanced Functional Materials. - : WILEY-V C H VERLAG GMBH. - 1616-301X .- 1616-3028. ; 29:10
-
Journal article (peer-reviewed)abstract
- Lead-free halide double perovskites with diverse electronic structures and optical responses, as well as superior material stability show great promise for a range of optoelectronic applications. However, their large bandgaps limit their applications in the visible light range such as solar cells. In this work, an efficient temperature-derived bandgap modulation, that is, an exotic fully reversible thermochromism in both single crystals and thin films of Cs2AgBiBr6 double perovskites is demonstrated. Along with the thermochromism, temperature-dependent changes in the bond lengths of Ag Symbol of the Klingon Empire Br (R-Ag Symbol of the Klingon Empire Br) and Bi Symbol of the Klingon Empire Br (R-Bi Symbol of the Klingon Empire Br) are observed. The first-principle molecular dynamics simulations reveal substantial anharmonic fluctuations of the R-Ag Symbol of the Klingon Empire Br and R-Bi Symbol of the Klingon Empire Br at high temperatures. The synergy of anharmonic fluctuations and associated electron-phonon coupling, and the peculiar spin-orbit coupling effect, is responsible for the thermochromism. In addition, the intrinsic bandgap of Cs2AgBiBr6 shows negligible changes after repeated heating/cooling cycles under ambient conditions, indicating excellent thermal and environmental stability. This work demonstrates a stable thermochromic lead-free double perovskite that has great potential in the applications of smart windows and temperature sensors. Moreover, the findings on the structure modulation-induced bandgap narrowing of Cs2AgBiBr6 provide new insights for the further development of optoelectronic devices based on the lead-free halide double perovskites.
|
|
| 19. |
- Ning, Xin, et al.
(author)
-
DILF : Differentiable rendering-based multi-view Image–Language Fusion for zero-shot 3D shape understanding
- 2024
-
In: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 102, s. 1-12
-
Journal article (peer-reviewed)abstract
- Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model's ability to fully comprehend 3D shapes and adversely impacts the text–image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image–Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text–image fusion loss, which aids in parameters’ regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP. © 2023 The Author(s)
|
|
| 20. |
- Ning, Xin, et al.
(author)
-
ICGNet : An intensity-controllable generation network based on covering learning for face attribute synthesis
- 2024
-
In: Information Sciences. - New York : Elsevier. - 0020-0255 .- 1872-6291. ; 660
-
Journal article (peer-reviewed)abstract
- Face-attribute synthesis is a typical application of neural network technology. However, most current methods suffer from the problem of uncontrollable attribute intensity. In this study, we proposed a novel intensity-controllable generation network (ICGNet) based on covering learning for face attribute synthesis. Specifically, it includes an encoder module based on the principle of homology continuity between homologous samples to map different facial images onto the face feature space, which constructs sufficient and effective representation vectors by extracting the input information from different condition spaces. It then models the relationships between attribute instances and representational vectors in space to ensure accurate synthesis of the target attribute and complete preservation of the irrelevant region. Finally, the progressive changes in the facial attributes by applying different intensity constraints to the representation vectors. ICGNet achieves intensity-controllable face editing compared to other methods by extracting sufficient and effective representation features, exploring and transferring attribute relationships, and maintaining identity information. The source code is available at https://github.com/kllaodong/-ICGNet.•We designed a new encoder module to map face images of different condition spaces into face feature space to obtain sufficient and effective face feature representation.•Based on feature extraction, we proposed a novel Intensity-Controllable Generation Network (ICGNet), which can realize face attribute synthesis with continuous intensity control while maintaining identity and semantic information.•The quantitative and qualitative results showed that the performance of ICGNet is superior to current advanced models.© 2024 Elsevier Inc.
|
|
| 21. |
- Peng, Shi-Yu, et al.
(author)
-
Reduced motor cortex GABABR function following chronic alcohol exposure
- 2021
-
In: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 26:2, s. 383-395
-
Journal article (peer-reviewed)abstract
- The GABA(B) receptor (GABA(B)R) agonist baclofen has been used to treat alcohol and several other substance use disorders (AUD/SUD), yet its underlying neural mechanism remains unclear. The present study aimed to investigate cortical GABA(B)R dynamics following chronic alcohol exposure. Ex vivo brain slice recordings from mice chronically exposed to alcohol revealed a reduction in GABA(B)R-mediated currents, as well as a decrease of GABA(B1/2)R and G-protein-coupled inwardly rectifying potassium channel 2 (GIRK2) activities in the motor cortex. Moreover, our data indicated that these alterations could be attributed to dephosphorylation at the site of serine 783 (ser-783) in GABA(B2) subunit, which regulates the surface expression of GABA(B)R. Furthermore, a human study using paired-pulse-transcranial magnetic stimulation (TMS) analysis further demonstrated a reduced cortical inhibition mediated by GABA(B)R in patients with AUD. Our findings provide the first evidence that chronic alcohol exposure is associated with significantly impaired cortical GABA(B)R function. The ability to promote GABA(B)R signaling may account for the therapeutic efficacy of baclofen in AUD.
|
|
| 22. |
- Piao, Shilong, et al.
(author)
-
Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends
- 2013
-
In: Global Change Biology. - : Wiley. - 1354-1013. ; 19:7, s. 2117-2132
-
Journal article (peer-reviewed)abstract
- The purpose of this study was to evaluate 10 process-based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity (GPP) is compared with flux-tower-based estimates by Jung etal. [Journal of Geophysical Research 116 (2011) G00J07] (JU11). The net primary productivity (NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free-Air CO2 Enrichment (FACE) experiments. The simulated global net biome productivity (NBP) is compared with the residual land sink (RLS) of the global carbon budget from Friedlingstein etal. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 +/- 15Pg Cyr-1) than JU11 (118 +/- 6Pg Cyr-1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 +/- 0.8Pg Cyr-1 is remarkably close to the mean value of RLS (2.1 +/- 1.2 Pg Cyr-1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980-2009. Both model-to-model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is -3.0 +/- 1.5Pg Cyr-1 degrees C-1, within the uncertainty of what derived from RLS (-3.9 +/- 1.1Pg Cyr-1 degrees C-1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation-based GPP and NBP can be fortuitous. Carbon-nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.
|
|
| 23. |
- Qu, Zhiguo, et al.
(author)
-
QEPP : A Quantum Efficient Privacy Protection Protocol in 6G-Quantum Internet of Vehicles
- 2024
-
In: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904. ; 9:1, s. 905-916
-
Journal article (peer-reviewed)abstract
- The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection. © IEEE
|
|
| 24. |
- Qu, Zhiguo, et al.
(author)
-
Quantum detectable Byzantine agreement for distributed data trust management in blockchain
- 2023
-
In: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 637
-
Journal article (peer-reviewed)abstract
- No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors
|
|
| 25. |
- Ran, Hang, et al.
(author)
-
3D human pose and shape estimation via de-occlusion multi-task learning
- 2023
-
In: Neurocomputing. - Amsterdam : Elsevier. - 0925-2312 .- 1872-8286. ; 548
-
Journal article (peer-reviewed)abstract
- Three-dimensional human pose and shape estimation is to compute a full human 3D mesh given a single image. The contamination of features caused by occlusion usually degrades its performance significantly. Recent progress in this field typically addressed the occlusion problem implicitly. By contrast, in this paper, we address it explicitly using a simple yet effective de-occlusion multi-task learning network. Our key insight is that feature for mesh parameter regression should be noiseless. Thus, in the feature space, our method disentangles the occludee that represents the noiseless human feature from the occluder. Specifically, a spatial regularization and an attention mechanism are imposed in the backbone of our network to disentangle the features into different channels. Furthermore, two segmentation tasks are proposed to supervise the de-occlusion process. The final mesh model is regressed by the disentangled occlusion-aware features. Experiments on both occlusion and non-occlusion datasets are conducted, and the results prove that our method is superior to the state-of-the-art methods on two occlusion datasets, while achieving competitive performance on a non-occlusion dataset. We also demonstrate that the proposed de-occlusion strategy is the main factor to improve the robustness against occlusion. The code is available at https://github.com/qihangran/De-occlusion_MTL_HMR. © 2023
|
|
| 26. |
- Ran, Hang, et al.
(author)
-
Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning
- 2024
-
In: Information Processing & Management. - London : Elsevier. - 0306-4573 .- 1873-5371. ; 61:3
-
Journal article (peer-reviewed)abstract
- Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot imbalanced data. To address this gap, we propose a Meta-learning- and NC-based FSCIL method, MetaNC-FSCIL, to compute the optimal margin adaptively and maintain it at each incremental session. Specifically, we first compute the theoretically optimal margin based on the NC theory. Then we introduce a novel loss function to ensure that the loss value is minimized precisely when the inter-class margin reaches its theoretically best. Motivated by the intuition that “learn how to preserve the margin” matches the meta-learning's goal of “learn how to learn”, we embed the loss function in base-session meta-training to preserve the margin for future meta-testing sessions. Experimental results demonstrate the effectiveness of MetaNC-FSCIL, achieving superior performance on multiple datasets. The code is available at https://github.com/qihangran/metaNC-FSCIL. © 2024 The Author(s)
|
|
| 27. |
- Ren, Xin, et al.
(author)
-
The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene : results from PlioMIP2
- 2023
-
In: Climate of the Past. - 1814-9324 .- 1814-9332. ; 19:10, s. 2053-2077
-
Journal article (peer-reviewed)abstract
- The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land–sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial.In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.
|
|
| 28. |
- Sun, Le, et al.
(author)
-
Energy-efficient Online Continual Learning for Time Series Classification in Nanorobot-based Smart Health
- 2023
-
In: IEEE journal of biomedical and health informatics. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208.
-
Journal article (peer-reviewed)abstract
- Nanorobots have been used in smart health to collect time series data such as electrocardiograms and electroencephalograms. Real-time classification of dynamic time series signals in nanorobots is a challenging task. Nanorobots in the nanoscale range require a classification algorithm with low computational complexity. First, the classification algorithm should be able to dynamically analyze time series signals and update itself to process the concept drifts (CD). Second, the classification algorithm should have the ability to handle catastrophic forgetting (CF) and classify historical data. Most importantly, the classification algorithm should be energy-efficient to use less computing power and memory to classify signals in real-time on a smart nanorobot. To solve these challenges, we design an algorithm that can Prevent Concept Drift in Online continual Learning for time series classification (PCDOL). The prototype suppression item in PCDOL can reduce the impact caused by CD. It also solves the CF problem through the replay feature. The computation per second and the memory consumed by PCDOL are only 3.572M and 1KB, respectively. The experimental results show that PCDOL is better than several state-of-the-art methods for dealing with CD and CF in energy-efficient nanorobots. © IEEE
|
|
| 29. |
- Tian, Songsong, et al.
(author)
-
A survey on few-shot class-incremental learning
- 2024
-
In: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 307-324
-
Research review (peer-reviewed)abstract
- Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. © 2023 The Author(s)
|
|
| 30. |
- Tian, Songsong, et al.
(author)
-
Continuous transfer of neural network representational similarity for incremental learning
- 2023
-
In: Neurocomputing. - Amsterdam : Elsevier. - 0925-2312 .- 1872-8286. ; 545
-
Journal article (peer-reviewed)abstract
- The incremental learning paradigm in machine learning has consistently been a focus of academic research. It is similar to the way in which biological systems learn, and reduces energy consumption by avoiding excessive retraining. Existing studies utilize the powerful feature extraction capabilities of pre-trained models to address incremental learning, but there remains a problem of insufficient utilization of neural network feature knowledge. To address this issue, this paper proposes a novel method called Pre-trained Model Knowledge Distillation (PMKD) which combines knowledge distillation of neural network representations and replay. This paper designs a loss function based on centered kernel alignment to transfer neural network representations knowledge from the pre-trained model to the incremental model layer-by-layer. Additionally, the use of memory buffer for Dark Experience Replay helps the model retain past knowledge better. Experiments show that PMKD achieved superior performance on various datasets and different buffer sizes. Compared to other methods, our class incremental learning accuracy reached the best performance. The open-source code is published athttps://github.com/TianSongS/PMKD-IL. © 2023 The Author(s)
|
|
| 31. |
- Wang, Gang, et al.
(author)
-
US2Mask : Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation
- 2024
-
In: Computers in Biology and Medicine. - Oxford : Elsevier. - 0010-4825 .- 1879-0534. ; 172, s. 1-13
-
Journal article (peer-reviewed)abstract
- Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework utilizing artificial intelligence generation technology to produce multi-class RGB masks for cardiac US image segmentation. The proposed approach directly performs semantic segmentation of the heart's main structures in US images from various scanning modes. Additionally, we introduce a novel learning approach based on conditional generative adversarial networks (CGAN) for cardiac US image segmentation, incorporating a conditional input and paired RGB masks. Experimental results from three cardiac US image datasets with diverse scan modes demonstrate that our approach outperforms several state-of-the-art models, showcasing improvements in five commonly used segmentation metrics, with lower noise sensitivity. Source code is available at https://github.com/energy588/US2mask. © 2024 Elsevier Ltd
|
|
| 32. |
- Xiao, Wenming, et al.
(author)
-
Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- 2021
-
In: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 39:9, s. 1141-1150
-
Journal article (peer-reviewed)abstract
- Recommendations are given on optimal read coverage and selection of calling algorithm to maximize the reproducibility of cancer mutation detection in whole-genome or whole-exome sequencing. Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.
|
|
| 33. |
- Yang Song-Yuan,, et al.
(author)
-
A D-band communication transmitter module with a novel self-aligned microstrip line-to-waveguide transition
- 2019
-
In: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves. - 1001-9014. ; 38:3, s. 296-302
-
Journal article (peer-reviewed)abstract
- A D-band (110 similar to 170 GHz) transmitter module, based on a novel self-aligned microstrip-to-waveguide transition, was demonstrated. The simulated average insertion loss of the transition is about 0.6 dB and return loss is better than 10 dB during working band. A D-band transmitter module was developed using such transition with resistive mixer and multiplier chips. The transmitter module operates between 110 similar to 153 GHz and provides a peak saturated output power of -4.6 dBm at 150 GHz and with 13.5 GHz 3 dB bandwidth from 145.8 to 159.3 GHz. 3 Gb/s wireless communication with this module at 145 GHz was demonstrated with spectrum efficient 64-QAM modulation.
|
|
| 34. |
- Yu, Zaiyang, et al.
(author)
-
MV-ReID : 3D Multi-view Transformation Network for Occluded Person Re-Identification
- 2024
-
In: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 283
-
Journal article (peer-reviewed)abstract
- Re-identification (ReID) of occluded persons is a challenging task due to the loss of information in scenes with occlusions. Most existing methods for occluded ReID use 2D-based network structures to directly extract representations from 2D RGB (red, green, and blue) images, which can result in reduced performance in occluded scenes. However, since a person is a 3D non-grid object, learning semantic representations in a 2D space can limit the ability to accurately profile an occluded person. Therefore, it is crucial to explore alternative approaches that can effectively handle occlusions and leverage the full 3D nature of a person. To tackle these challenges, in this study, we employ a 3D view-based approach that fully utilizes the geometric information of 3D objects while leveraging advancements in 2D-based networks for feature extraction. Our study is the first to introduce a 3D view-based method in the areas of holistic and occluded ReID. To implement this approach, we propose a random rendering strategy that converts 2D RGB images into 3D multi-view images. We then use a 3D Multi-View Transformation Network for ReID (MV-ReID) to group and aggregate these images into a unified feature space. Compared to 2D RGB images, multi-view images can reconstruct occluded portions of a person in 3D space, enabling a more comprehensive understanding of occluded individuals. The experiments on benchmark datasets demonstrate that the proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks. These results also suggest that our approach has the potential to solve occlusion problems and contribute to the field of ReID. The source code and dataset are available at https://github.com/yuzaiyang123/MV-Reid. © 2023 Elsevier B.V.
|
|
| 35. |
- Zhang, Jie, et al.
(author)
-
Nested hollow architectures of nitrogen-doped carbon-decorated Fe, Co, Ni-based phosphides for boosting water and urea electrolysis
- 2022
-
In: Nano Reseach. - : Tsinghua University Press. - 1998-0124 .- 1998-0000. ; 15, s. 1916-1925
-
Journal article (peer-reviewed)abstract
- Tailoring the nanostructure/morphology and chemical composition is important to regulate the electronic configuration of electrocatalysts and thus enhance their performance for water and urea electrolysis. Herein, the nitrogen-doped carbon-decorated tricomponent metal phosphides of FeP4 nanotube@Ni-Co-P nanocage (NC-FNCP) with unique nested hollow architectures are fabricated by a self-sacrifice template strategy. Benefiting from the multi-component synergy, the modification of nitrogen-doped carbon, and the modulation of nested porous hollow morphology, NC-FNCP facilitates rapid electron/mass transport in water and urea electrolysis. NC-FNCP-based anode shows low potentials of 248 mV and 1.37 V (vs. reversible hydrogen electrode) to attain 10 mA/cm(2) for oxygen evolution reaction (OER) and urea oxidation reaction (UOR), respectively. In addition, the overall urea electrolysis drives 10 mA/cm(2) at a comparatively low voltage of 1.52 V (vs. RHE) that is 110 mV lower than that of overall water electrolysis, as well as exhibits excellent stability over 20 h. This work strategizes a multi-shell-structured electrocatalyst with multi-compositions and explores its applications in a sustainable combination of hydrogen production and sewage remediation.
|
|
| 36. |
- Zhang, Ke, et al.
(author)
-
Revisiting the physical processes controlling the tropical atmospheric circulation changes during the Mid-Piacenzian Warm Period
- 2024
-
In: Quaternary International. - 1040-6182 .- 1873-4553. ; 682, s. 46-59
-
Journal article (peer-reviewed)abstract
- The Mid-Piacenzian Warm Period (MPWP; 3.0–3.3 Ma), a warm geological period about three million years ago, has been deemed as a good past analog for understanding the current and future climate change. Based on 12 climate model outputs from Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we investigate tropical atmospheric circulation (TAC) changes under the warm MPWP and associated underlying mechanisms by diagnosing both atmospheric static stability and diabatic processes. Our findings underscore the advantage of analyzing atmospheric diabatic processes in elucidating seasonal variations of TAC compared to static stability assessments. Specifically, by diagnosing alterations in diabatic processes, we achieve a quantitative understanding and explanation the following TAC changes (incl. Strength and edge) during the MPWP: the weakened (annual, DJF, JJA) Northern Hemisphere and (DJF) Southern Hemisphere Hadley circulation (HC), reduced (annual, DJF) Pacific Walker circulation (PWC) and enhanced (annual, JJA) Southern Hemisphere HC and (JJA) PWC, and westward shifted (annual, DJF, JJA) PWC. We further addressed that the increasing bulk subtropical static stability and/or decreasing vertical shear of subtropical zonal wind - two crucial control factors for changes in subtropical baroclinicity - may promote HC widening, and vice versa. Consequently, our study of spatial diabatic heating and cooling, corresponding to upward and downward motions within the TAC, respectively, provides a new perspective for understanding the processes controlling seasonal TAC changes in response to surface warming.
|
|
| 37. |
- Zhang, Yiyang, et al.
(author)
-
DCNet : A Self-supervised EEG Classification Framework for Improving Cognitive Computing-enabled Smart Healthcare
- 2024
-
In: IEEE journal of biomedical and health informatics. - Piscataway, NJ : IEEE. - 2168-2194 .- 2168-2208. ; , s. 1-9
-
Journal article (peer-reviewed)abstract
- Cognitive computing explores brain mechanisms and develops brain-like computing models for cognitive processes. EEG measures brain activity, and EEG classification identifies patterns using machine learning algorithms. Combining EEG classification with cognitive computing offers insights into cognitive processes, brainmachine interfaces, and cognitive state monitoring. We propose (DreamCatcher Network) DCNet, a self-supervised learning method for diagnosing sleep disorders using EEG. DCNet autonomously learns comprehensive representations through contrast learning, reducing annotation time. The training process involves feature learning, classification, time-series contrast learning, and data enhancement. Experimental results on the Sleep-EDF dataset achieved 81.28% average accuracy. Validation on the HAR dataset showed model efficiency and scalability, with 92.51% accuracy on the test set. DCNet has the potential to revolutionize sleep disorder diagnosis and enhance the development of cognitive computing-enabled smart healthcare systems. © Copyright 2024 IEEE
|
|
| 38. |
- Zhu, Ning, et al.
(author)
-
Photo-responsive chiral cyclic molecular switches based on stiff stilbene
- 2016
-
In: Dyes and pigments. - : ELSEVIER SCI LTD. - 0143-7208 .- 1873-3743. ; 125, s. 259-265
-
Journal article (peer-reviewed)abstract
- Two novel photo-responsive chiral cyclic molecular switches constituted of stiff stilbene and binaphthyl moieties connected through alkyl chains of different length were fabricated. The cyclization synthetic strategy employed herein made it convenient to obtain the pure Z isomers rather than Z/E isomer mixtures. The detailed photo-switching behaviors of target compounds were studied by the UV-Vis absorption and circular dichroism spectra in dichloromethane. The twist angles of the binaphthyl of the switches were able to be reversibly modulated by Z/E isomerization of stiff stilbene unit under alternative UV light stimuli and influenced by the length of alkyl chain to some extent.
|
|
