Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.Comment: submitted to IEEE ICASSP 202

Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded similar to 0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of similar to 81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs

A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework

In the past few years, structural health monitoring (SHM) has become an important technology to ensure the safety of structures. Structural damage identification methods based on machine learning techniques have gained wide attention due to the advantages of effectively extracting features from monitoring data. However, the existing machine learning-based methods heavily depend on manually selected feature parameters from raw signals. This will cause the selected feature to obtain the optimal solution for a specific condition but may fail to provide a similar performance in other cases. In addition, the feature selection takes a long time, which can fail to achieve real-time performance in a practical structure. To address these problems, this article proposes a hybrid deep learning framework for structural damage identification that includes three components, namely, ensemble empirical mode decomposition (EEMD), Pearson correlation coefficient (PCC), and a convolutional neural network (CNN). The proposed EEMD-PCC-CNN method is capable of automatically extracting features from raw signals to satisfy any damage identification objective. To evaluate the performance of the proposed EEMD-PCC-CNN method, a three-story building structure is investigated. The acceleration signal of the three-story building structure is first analyzed by EEMD. After obtaining the time-frequency information, PCC is utilized to select optimal time-frequency information as the input of the CNN for damage identification. Compared with other classical methods (SVM, KNN, RF, etc.), the experimental results show that the newly proposed EEMD-PCC-CNN method has significant performance advantages in damage identification. In addition, the accuracy of the proposed damage identification method is improved by more than 4% after utilizing EEMD in comparison with CNN alone

A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework

In the past few years, structural health monitoring (SHM) has become an important technology to ensure the safety of structures. Structural damage identification methods based on machine learning techniques have gained wide attention due to the advantages of effectively extracting features from monitoring data. However, the existing machine learning-based methods heavily depend on manually selected feature parameters from raw signals. This will cause the selected feature to obtain the optimal solution for a specific condition but may fail to provide a similar performance in other cases. In addition, the feature selection takes a long time, which can fail to achieve real-time performance in a practical structure. To address these problems, this article proposes a hybrid deep learning framework for structural damage identification that includes three components, namely, ensemble empirical mode decomposition (EEMD), Pearson correlation coefficient (PCC), and a convolutional neural network (CNN). The proposed EEMD-PCC-CNN method is capable of automatically extracting features from raw signals to satisfy any damage identification objective. To evaluate the performance of the proposed EEMD-PCC-CNN method, a three-story building structure is investigated. The acceleration signal of the three-story building structure is first analyzed by EEMD. After obtaining the time-frequency information, PCC is utilized to select optimal time-frequency information as the input of the CNN for damage identification. Compared with other classical methods (SVM, KNN, RF, etc.), the experimental results show that the newly proposed EEMD-PCC-CNN method has significant performance advantages in damage identification. In addition, the accuracy of the proposed damage identification method is improved by more than 4% after utilizing EEMD in comparison with CNN alone

Geochronology and geochemistry of garnet from Tongguanshan skarn Cu deposit, Tongling, eastern China: insights into ore-forming process

Skarn Cu deposits are one of most important deposit-type in Middle-Lower Yangtze River region, eastern China, but skarn formation process remains unclear. Mineralogical, morphological and in situ geochemical data from the skarn stage of Tongguanshan skarn Cu deposit in Tongling region are systemically investigated, to reveal the timing, physical-chemical conditions, and fluid evolution during the skarn formation. The Tongguanshan garnets can be identified homogeneous and unzoned early generation garnet (GrtI), and oscillating zoned late generation garnet (GrtII) with the Fe-rich core (GrtII-Fe) and Al-rich edge (GrtII-Al). Garnet U–Pb dating results show that the Tongguanshan Cu mineralization was formed in 145.6 ± 4.4 Ma. In situ elemental composition results of the garnet samples indicate that they belong to grossular-andradite solid solution series, and are a magmatic-hydrothermal origin. The distinctly geochemical characteristics (e.g., Sn and U contents, (La/Yb)N, δEu and Y/Ho values) reveal that the physiochemical conditions from GrtI to GrtII-Fe, and GrtII-Fe to GrtII-Al stages in the Tongguanshan skarn formation were an increase and a decrease of fluid salinity and oxygen fugacity, closed to open and then to closed of fluid environment, and neutral-weakly acidic to acidic and acidic to neutral-weakly acidic of fluid pH, respectively. A comprehensive discriminant analysis indicates a fluid boiling occurred in the GrtI to GrtII-Fe stage of the Tongguanshan skarn Cu deposit, and there is little or no external fluid mixed during the skarn stage