149 research outputs found
A physics-constrained machine learning method for mapping gapless land surface temperature
More accurate, spatio-temporally, and physically consistent LST estimation
has been a main interest in Earth system research. Developing physics-driven
mechanism models and data-driven machine learning (ML) models are two major
paradigms for gapless LST estimation, which have their respective advantages
and disadvantages. In this paper, a physics-constrained ML model, which
combines the strengths in the mechanism model and ML model, is proposed to
generate gapless LST with physical meanings and high accuracy. The hybrid model
employs ML as the primary architecture, under which the input variable physical
constraints are incorporated to enhance the interpretability and extrapolation
ability of the model. Specifically, the light gradient-boosting machine (LGBM)
model, which uses only remote sensing data as input, serves as the pure ML
model. Physical constraints (PCs) are coupled by further incorporating key
Community Land Model (CLM) forcing data (cause) and CLM simulation data
(effect) as inputs into the LGBM model. This integration forms the PC-LGBM
model, which incorporates surface energy balance (SEB) constraints underlying
the data in CLM-LST modeling within a biophysical framework. Compared with a
pure physical method and pure ML methods, the PC-LGBM model improves the
prediction accuracy and physical interpretability of LST. It also demonstrates
a good extrapolation ability for the responses to extreme weather cases,
suggesting that the PC-LGBM model enables not only empirical learning from data
but also rationally derived from theory. The proposed method represents an
innovative way to map accurate and physically interpretable gapless LST, and
could provide insights to accelerate knowledge discovery in land surface
processes and data mining in geographical parameter estimation
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Enhancing speech signal quality in adverse acoustic environments is a
persistent challenge in speech processing. Existing deep learning based
enhancement methods often struggle to effectively remove background noise and
reverberation in real-world scenarios, hampering listening experiences. To
address these challenges, we propose a novel approach that uses pre-trained
generative methods to resynthesize clean, anechoic speech from degraded inputs.
This study leverages pre-trained vocoder or codec models to synthesize
high-quality speech while enhancing robustness in challenging scenarios.
Generative methods effectively handle information loss in speech signals,
resulting in regenerated speech that has improved fidelity and reduced
artifacts. By harnessing the capabilities of pre-trained models, we achieve
faithful reproduction of the original speech in adverse conditions.
Experimental evaluations on both simulated datasets and realistic samples
demonstrate the effectiveness and robustness of our proposed methods.
Especially by leveraging codec, we achieve superior subjective scores for both
simulated and realistic recordings. The generated speech exhibits enhanced
audio quality, reduced background noise, and reverberation. Our findings
highlight the potential of pre-trained generative techniques in speech
processing, particularly in scenarios where traditional methods falter. Demos
are available at https://whmrtm.github.io/SoundResynthesis.Comment: Paper in submissio
Seroprevalence and Genetic Characterization of Toxoplasma Gondii in Three Species of Pet Birds in China
Background
Toxoplasmosis, caused by the protozoan parasite Toxoplasma gondii, is one of the most common zoonosis worldwide, affecting a wide range of warm-blooded mammals and birds worldwide. However, no information on T. gondii infection in pet birds in China is available. Therefore, this study was performed to determine the prevalence of T. gondii infection in pet birds in Gansu province, China. Methods
A total of 687 blood samples were collected from pet birds (Carduelis spinus, Alauda gulgula, Cocothraustes migratorlus) in three representative administrative regions in Gansu province, northwest China between August 2011 and September 2012 T. gondii antibodies were determined using the modified agglutination test (MAT). Genomic DNA was extracted from the brain tissues of seropositive pet birds and T. gondii B1 gene was amplified using a semi-nested PCR.DNA samples giving positive B1 amplification were then genetically characterized using multi-locus PCR-RFLP. Results
The overall T. gondii seroprevalence was 11.21% (77/687). C. spinus had the highest T. gondii seroprevalence (11.65%), followed by A. arvensis (11.39%) and C. migratorlus (5.26%), these differences were not statistically significant (P \u3e 0.05). Of 77 DNA samples, 8 were positive for the T. gondii B1 gene, four showed complete genotyping results. Only one genotype (the Type II variant: ToxoDB genotype #3) was identified. Conclusions
The results of the present survey indicated the presence of T. gondii infection in pet birds in Gansu province, China. These data provide base-line information for the execution of control strategies against T. gondii infection in pet birds. To our knowledge, this is the first report documenting the occurrence of T. gondii prevalence and genotype in pet birds in China
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