High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demonstrated outstanding performance on the artificial leather defect dataset, achieving an impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a significant reduction of 5.2% - 7.2% in the error detection rate. Moreover, YOLOD also exhibited remarkable performance on the general MS-COCO dataset, with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% - 4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of YOLOD in both artificial leather defect detection and general object detection tasks, making it a highly efficient and effective model for real-world applications

Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard method of machine learning technique and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control and system theory, which also leads to a clear system interpretation of the latent variable. The major efforts are devoted to the development of a control theoretical solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification

In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon the central concept of this method, we introduce two innovative modules for backbone and head networks: the Dense Channel Compression for Feature Spatial Solidification Structure (DCFS) and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When integrated into the YOLOv5 model, these two modules demonstrate exceptional performance, resulting in a modified model referred to as YOLOCS. Evaluated on the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of 50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method

7-Pyrrolidinethoxy-4′-Methoxyisoflavone Prevents Amyloid β–Induced Injury by Regulating Histamine H3 Receptor-Mediated cAMP/CREB and AKT/GSK3β Pathways

In studies on the treatment of Alzheimer’s disease (AD), in which cognition is enhanced even modestly or selectively, it has been considered that the histamine H3 receptor (H3R) may be a potential target. In this study, we aimed at evaluating the ability of 7-pyrrolidinethoxy-4′-methoxyisoflavone (indicated as LC1405), a novel potential H3R antagonist identified from our H3R antagonist screening system, to ameliorate amyloid β (Aβ)-induced cognitive deficits, and to explore the underlying mechanisms that are related to H3R-modulated signaling. Our results demonstrated that LC1405 effectively reduced the progression of Aβ-associated disorders, such as improved learning and memory capabilities, preserved tissues from suffering neurodegeneration and ultrastructural abnormalities, and ameliorated cholinergic dysfunction in an APP/PS1 double transgenic mouse model of AD. In an in vitro model, LC1405 protected neuronal cells against copper-induced Aβ toxicity, as demonstrated by the improvement in cell viability and decrease in neuronal apoptotic ratio. In addition, treatment with LC1405 resulted in the up-regulation of acetylcholine (ACh) or histamine release and provided neuroprotection through cellular signaling cascades involving H3R-mediated cAMP/CREB and AKT/GSK3β pathways. Furthermore, the beneficial effects of LC1405 on Aβ-mediated toxicity and H3R-mediated cAMP/CREB and AKT/GSK3β axes were reversed after pharmacological activation of H3R. In conclusion, our results demonstrated that LC1405 blocked Aβ-induced toxicity through H3R-modulated signaling transduction both in vitro and in vivo. The results also suggested that LC1405 might have translational potential as a complementary therapy to control disease progression in AD patients who developed cognitive deficits with H3R-related ACh neurotransmission abnormality

Identification of Blue Horizontal-Branch Stars From LAMOST DR5

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 dataset, which contains 5355+81 BHB stars at high Galactic latitude (($|Glat|>20^{\circ}$). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of \ion{Ca}{2}\,K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by cross-matching our BHB stars with the catalog provided by \citet{Xiang2022}. The results show that our sample is consistent with the theoretical $T_{\rm eff}$-log\,$g$ evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes it a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.Comment: accepted by ApJS.15 pages, 18 figure

Imaging manifestations of neonatal necrotizing enterocolitis to predict timing of surgery

Background. To find the predictor of optimal surgical timing for neonatal necrotizing enterocolitis (NEC) patients by analyzing the risk factors of conservative treatment and surgical therapy. Methods. Data were collected from 184 NEC patients (Surgery, n=41; conservative treatment, n=143) between the years 2015 and 2019. Data were analyzed by univariate analysis, and multivariate binary logistic regression analysis. Results. Univariate analysis showed that statistically significant differences between the surgery and conservative treatment groups. The results of multivariate Logistic regression analysis indicated intestinal wall thickening by B-ultrasound and gestational age were independent factors to predict early surgical indications of NEC (p < 0.05). The true positive rate, false positive rate, true negative rate and false negative rate in the diagnosis of necrotic bowel perforation guided by DAAS (Duke abdominal X-ray score) ≥7 and MD7 (seven clinical metrics of metabolic derangement) ≥3 were 12.8%, 0.0%, 100.0% and 87.2%, respectively. Conclusions. In summary, the ultrasound examination in NEC children showing thickening intestinal wall and poor intestinal peristalsis indicated for early operation

Ketogenic therapy towards precision medicine for brain diseases

Precision nutrition and nutrigenomics are emerging in the development of therapies for multiple diseases. The ketogenic diet (KD) is the most widely used clinical diet, providing high fat, low carbohydrate, and adequate protein. KD produces ketones and alters the metabolism of patients. Growing evidence suggests that KD has therapeutic effects in a wide range of neuronal diseases including epilepsy, neurodegeneration, cancer, and metabolic disorders. Although KD is considered to be a low-side-effect diet treatment, its therapeutic mechanism has not yet been fully elucidated. Also, its induced keto-response among different populations has not been elucidated. Understanding the ketone metabolism in health and disease is critical for the development of KD-associated therapeutics and synergistic therapy under any physiological background. Here, we review the current advances and known heterogeneity of the KD response and discuss the prospects for KD therapy from a precision nutrition perspective

The Role of the Mindoro-Sibutu Pathway on the South China Sea Multi–layer Circulation

The role of the Mindoro Strait–Sibutu Passage pathway in influencing the Luzon Strait inflow to the South China Sea (SCS) and the SCS multilayer circulation is investigated with a high-resolution (0.1830.18) regional ocean model. Significant changes are evident in the SCS upper-layer circulation (250–900 m) by closing the Mindoro–Sibutu pathway in sensitivity experiments, as Luzon Strait transport is reduced by 75%, from 24.4 to 21.2 Sv (1 Sv [ 106m3 s21). Because of the vertical coupling between the upper and middle layers, closing the Mindoro–Sibutu pathway also weakens clockwise circulation in the middle layer (900–2150 m), but there is no significant change in the deep layer (below 2150 m). The Mindoro–Sibutu pathway is an important branch of the SCS throughflow into the Indonesian Seas. It is also the gateway for oceanic waves propagating clockwise around the Philippines Archipelago from the western Pacific Ocean into the eastern SCS, projecting El Niño–Southern Oscillation sea level signals to the SCS, impacting its interannual variations and multilayer circulation. The results provide insights into the dynamics of how upstream and downstream passage throughflows are coupled to affect the general circulation in marginal seas

The apple 14-3-3 gene MdGRF6 negatively regulates salt tolerance

The 14-3-3 (GRF, general regulatory factor) regulatory proteins are highly conserved and are widely distributed throughout the eukaryotes. They are involved in the growth and development of organisms via target protein interactions. Although many plant 14-3-3 proteins were identified in response to stresses, little is known about their involvement in salt tolerance in apples. In our study, nineteen apple 14-3-3 proteins were cloned and identified. The transcript levels of Md14-3-3 genes were either up or down-regulated in response to salinity treatments. Specifically, the transcript level of MdGRF6 (a member of the Md14-3-3 genes family) decreased due to salt stress treatment. The phenotypes of transgenic tobacco lines and wild-type (WT) did not affect plant growth under normal conditions. However, the germination rate and salt tolerance of transgenic tobacco was lower compared to the WT. Transgenic tobacco demonstrated decreased salt tolerance. The transgenic apple calli overexpressing MdGRF6 exhibited greater sensitivity to salt stress compared to the WT plants, whereas the MdGRF6-RNAi transgenic apple calli improved salt stress tolerance. Moreover, the salt stress-related genes (MdSOS2, MdSOS3, MdNHX1, MdATK2/3, MdCBL-1, MdMYB46, MdWRKY30, and MdHB-7) were more strongly down-regulated in MdGRF6-OE transgenic apple calli lines than in the WT when subjected to salt stress treatment. Taken together, these results provide new insights into the roles of 14-3-3 protein MdGRF6 in modulating salt responses in plants