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A Dy(III) coordination polymer (CP), [Dy(spasds)(H2O)2]n (1) (Na2Hspasds = 5-(4-sulfophenylazo)salicylic disodium salt), has been synthesized using a hydrothermal method and characterized. 1 features a 2D layered structure, where the spasda3− anions act as pentadentate ligands, adopting carboxylate, sulfonate and phenolate groups to bridge with four Dy centers in η3-μ1: μ2, η2-μ1: μ1, and monodentate coordination modes, respectively. It possesses a unique (4,4)-connected net with a Schläfli symbol of {44·62}{4}2. The luminescence study revealed that 1 exhibited a broad fluorescent emission band at 392 nm. Moreover, the visual blue color has been confirmed by the CIE plot. 1 can serve as a dual-functional luminescent sensor toward Fe3+ and MnO4− through the luminescence quenching effect, with limits of detection (LODs) of 9.30 × 10−7 and 1.19 × 10−6 M, respectively. The LODs are relatively low in comparison with those of the reported CP-based sensors for Fe3+ and MnO4−. In addition, 1 also has high selectivity and remarkable anti-interference ability, as well as good recyclability for at least five cycles. Furthermore, the potential application of the sensor for the detection of Fe3+ and MnO4− was studied through simulated wastewater samples with different concentrations. The possible sensing mechanisms were investigated using Ultraviolet-Visible (UV-Vis) absorption spectroscopy and density functional theory (DFT) calculations. The results revealed that the luminescence turn-off effects toward Fe3+ and MnO4− were caused by competitive absorption and photoinduced electron transfer (PET), and competitive absorption and inner filter effect (IFE), respectively.

Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available.

With the rapid development of artificial intelligence and deep learning technology, automatic speech recognition technology is experiencing new vitality [...]

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion’s boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved 0.9246 for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images.

The extraction of a target speaker from mixtures of different speakers has attracted extensive amounts of attention and research. Previous studies have proposed several methods, such as SpeakerBeam, to tackle this speech extraction problem using clean speech from the target speaker to provide information. However, clean speech cannot be obtained immediately in most cases. In this study, we addressed this problem by extracting features from the electroglottographs (EGGs) of target speakers. An EGG is a laryngeal function detection technology that can detect the impedance and condition of vocal cords. Since EGGs have excellent anti-noise performance due to the collection method, they can be obtained in rather noisy environments. In order to obtain clean speech from target speakers out of the mixtures of different speakers, we utilized deep learning methods and used EGG signals as additional information to extract target speaker. In this way, we could extract target speaker from mixtures of different speakers without needing clean speech from the target speakers. According to the characteristics of the EGG signals, we developed an EGG_auxiliary network to train a speaker extraction model under the assumption that EGG signals carry information about speech signals. Additionally, we took the correlations between EGGs and speech signals in silent and unvoiced segments into consideration to develop a new network involving EGG preprocessing. We achieved improvements in the scale invariant signal-to-distortion ratio improvement (SISDRi) of 0.89 dB on the Chinese Dual-Mode Emotional Speech Database (CDESD) and 1.41 dB on the EMO-DB dataset. In addition, our methods solved the problem of poor performance with target speakers of the same gender and the different between the same gender situation and the problem of greatly reduced precision under the low SNR circumstances.

Speech emotion recognition (SER) is an important component of emotion computation and signal processing. Recently, many works have applied abundant acoustic features and complex model architectures to enhance the model’s performance, but these works sacrifice the portability of the model. To address this problem, we propose a model utilizing only the fundamental frequency from electroglottograph (EGG) signals. EGG signals are a sort of physiological signal that can directly reflect the movement of the vocal cord. Under the assumption that different acoustic features share similar representations in the internal emotional state, we propose cross-modal emotion distillation (CMED) to train the EGG-based SER model by transferring robust speech emotion representations from the log-Mel-spectrogram-based model. Utilizing the cross-modal emotion distillation, we achieve an increase of recognition accuracy from 58.98% to 66.80% on the S70 subset of the Chinese Dual-mode Emotional Speech Database (CDESD 7-classes) and 32.29% to 42.71% on the EMO-DB (7-classes) dataset, which shows that our proposed method achieves a comparable result with the human subjective experiment and realizes a trade-off between model complexity and performance.

Background: Solid dispersions are an effective formulation technique to improve the solubility, dissolution rate, and bioavailability of water-insoluble drugs for oral delivery. In the last 15 years, increased attention was focused on this technology. There were 23 marketed drugs prepared by solid dispersion techniques. Objective: This study aimed to report the big picture of solid dispersion research from 1980 to 2015. Method: Scientific knowledge mapping tools were used for the qualitative and the quantitative analysis of patents and literature from the time and space dimensions. Results: Western Europe and North America were the major research areas in this field with frequent international cooperation. Moreover, there was a close collaboration between universities and industries, while research collaboration in Asia mainly existed between universities. The model drugs, main excipients, preparation technologies, characterization approaches and the mechanism involved in the formulation of solid dispersions were analyzed via the keyword burst and co-citation cluster techniques. Integrated experimental, theoretical and computational tools were useful techniques for in silico formulation design of the solid dispersions. Conclusions: Our research provided the qualitative and the quantitative analysis of patents and literature of solid dispersions in the last three decades.

Perishable fresh vegetables that do not meet cosmetic standards and by-products of processing are currently wasted. Broccoli and carrots were selected as model vegetables to demonstrate that they can be converted into nutrient-dense and shelf-stable food ingredients and formulated into convenient ready-to-eat snacks. Broccoli powder was a rich source of protein (30%) and dietary fibre (28%). Carrot powder had lower protein (6.5%) and dietary fibre content (24%) and was higher in sugar (47%) compared to broccoli powder (21%). Compared to the whole-vegetable powders, pomace powders were richer in fibre but had lower levels of total carbohydrates. There was a reduced expansion of extruded snacks with increasing levels of the vegetable component in the formulation. Processing and storage for 12 months at 25 °C or 40 °C resulted in changes in the measured soluble phenolic content. Changes during storage were dependent on the temperature and time. The changes may be in part due to the changes in the material properties of the matrix as a consequence of processing and storage, which affect extractability. The conversion of perishable vegetables and pomace into shelf-stable nutrient-dense food ingredients and products will reduce food loss and waste in the vegetable industry.