Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression

Deep learning has been recently introduced for efficient acoustic howling suppression (AHS). However, the recurrent nature of howling creates a mismatch between offline training and streaming inference, limiting the quality of enhanced speech. To address this limitation, we propose a hybrid method that combines a Kalman filter with a self-attentive recurrent neural network (SARNN) to leverage their respective advantages for robust AHS. During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN). During streaming inference, the DNN's parameters are fixed while its output serves as a reference signal for updating the Kalman filter. Evaluation in both offline and streaming inference scenarios using simulated and real-recorded data shows that the proposed method efficiently suppresses howling and consistently outperforms baselines.Comment: submitted to INTERSPEECH 2023. arXiv admin note: text overlap with arXiv:2302.0925

Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery

This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings

Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images

Individual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.National Key R&D Program of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationPeer Reviewe

Ferroelastic-switching-driven colossal shear strain and piezoelectricity in a hybrid ferroelectric

Materials that can produce large controllable strains are widely used in shape memory devices, actuators and sensors. Great efforts have been made to improve the strain outputs of various material systems. Among them, ferroelastic transitions underpin giant reversible strains in electrically-driven ferro/piezoelectrics and thermally- or magneticallydriven shape memory alloys. However, large-strain ferroelastic switching in conventional ferroelectrics is very challenging while magnetic and thermal controls are not desirable for applications. Here, we demonstrate an unprecedentedly large shear strain up to 21.5 % in a hybrid ferroelectric, C6H5N(CH3)3CdCl3. The strain response is about two orders of magnitude higher than those of top-performing conventional ferroelectric polymers and oxides. It is achieved via inorganic bond switching and facilitated by the structural confinement of the large organic moieties, which prevents the undesired 180-degree polarization switching. Furthermore, Br substitution can effectively soften the bonds and result in giant shear piezoelectric coefficient (d35 ~ 4800 pm/V) in Br-rich end of the solid solution, C6H5N(CH3)3CdBr3xCl3(1-x). The superior electromechanical properties of the compounds promise their potential in lightweight and high energy density devices, and the strategy described here should inspire the development of next-generation piezoelectrics and electroactive materials based on hybrid ferroelectrics.Comment: 32 pages, 14 figures, 5 table

Kaempferol as a flavonoid induces osteoblastic differentiation via estrogen receptor signaling

<p>Abstract</p> <p>Background</p> <p>Flavonoids, a group of compounds mainly derived from vegetables and herbal medicines, chemically resemble estrogen and some have been used as estrogen substitutes. Kaempferol, a flavonol derived from the rhizome of <it>Kaempferia galanga </it>L., is a well-known phytoestrogen possessing osteogenic effects that is also found in a large number of plant foods.</p> <p>The herb <it>K. galanga </it>is a popular traditional aromatic medicinal plant that is widely used as food spice and in medicinal industries. In the present study, both the estrogenic and osteogenic properties of kaempferol are evaluated.</p> <p>Methods</p> <p>Kaempferol was first evaluated for its estrogenic properties, including its effects on estrogen receptors. The osteogenic properties of kaempferol were further determined its induction effects on specific osteogenic enzymes and genes as well as the mineralization process in cultured rat osteoblasts.</p> <p>Results</p> <p>Kaempferol activated the transcriptional activity of pERE-Luc (3.98 ± 0.31 folds at 50 μM) and induced estrogen receptor α (ERα) phosphorylation in cultured rat osteoblasts, and this ER activation was correlated with induction and associated with osteoblast differentiation biomarkers, including alkaline phosphatase activity and transcription of osteoblastic genes, <it>e.g</it>., type I collagen, osteonectin, osteocalcin, Runx2 and osterix. Kaempferol also promoted the mineralization process of osteoblasts (4.02 ± 0.41 folds at 50 μM). ER mediation of the kaempferol-induced effects was confirmed by pretreatment of the osteoblasts with an ER antagonist, ICI 182,780, which fully blocked the induction effect.</p> <p>Conclusion</p> <p>Our results showed that kaempferol stimulates osteogenic differentiation of cultured osteoblasts by acting through the estrogen receptor signaling.</p

Trajectory Tracking Control Method for Omnidirectional Mobile Robot Based on Self-Organizing Fuzzy Neural Network and Preview Strategy

This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In particular, since the structure of traditional approximation network is preset, it will cause problems such as input constraints and rule redundancy, resulting in low adaptability of the controller. Therefore, a self-organizing algorithm including rule growth and local access is designed according to the tracking control requirements of omnidirectional mobile robots. In addition, a preview strategy (PS) based on Bezier curve trajectory re-planning is proposed to solve the problem of tracking curve instability caused by the lag of tracking starting point. Finally, the simulation verifies the effectiveness of this method in tracking and trajectory starting point optimization

A passive air sampler for characterizing the vertical concentration profile of gaseous phase polycyclic aromatic hydrocarbons in near soil surface air

Air–soil exchange is an important process governing the fate of polycyclic aromatic hydrocarbons (PAHs). A novel passive air sampler was designed and tested for measuring the vertical concentration profile of 4 low molecular weight PAHs in gaseous phase (PAHLMW4) in near soil surface air. Air at various heights from 5 to 520 mm above the ground was sampled by polyurethane foam disks held in down-faced cartridges. The samplers were tested at three sites: A: an extremely contaminated site, B: a site near A, and C: a background site on a university campus. Vertical concentration gradients were revealed for PAHLMW4 within a thin layer close to soil surface at the three sites. PAH concentrations either decreased (Site A) or increased (Sites B and C) with height, suggesting either deposition to or evaporation from soils. The sampler is a useful tool for investigating air–soil exchange of gaseous phase semi-volatile organic chemicals

MicroRNA-221-5p Inhibits Porcine Epidemic Diarrhea Virus Replication by Targeting Genomic Viral RNA and Activating the NF-κB Pathway

MicroRNAs (miRNAs) are a class of noncoding RNAs involved in posttranscriptional regulation of gene expression and many critical roles in numerous biological processes. Porcine epidemic diarrhea virus (PEDV), the etiological agent of porcine epidemic diarrhea, causes substantial economic loss in the swine industry worldwide. Previous studies reported miRNA involvement in viral infection; however, their role in regulating PEDV infection remains unknown. In this study, we investigated the regulatory relationship between miRNA-221-5p and PEDV infection, finding that miR-221-5p overexpression inhibited PEDV replication in a dose-dependent manner, and that silencing endogenous miR-221-5p enhanced viral replication. Our results showed that miR-221-5p directly targets the 3&#8242; untranslated region (UTR) of PEDV genomic RNA to inhibit PEDV replication, and that miR-221-5p overexpression activates nuclear factor (NF)-&#954;B signaling via p65 nuclear translocation, thereby upregulating interferon (IFN)-&#946;, IFN-stimulated gene 15, and MX1 expression during CH/HBTS/2017 infection. We subsequently identified NF-&#954;B-inhibitor &#945; and suppressor of cytokine signaling 1, negative regulators of the NF-&#954;B pathway, as miR-221-5p targets. These results demonstrated the ability of miR-221-5p to inhibit PEDV replication by targeting the 3&#8217; UTR of the viral genome and activating the NF-&#954;B-signaling pathway. Our findings will aid the development of preventive and therapeutic strategies for PEDV infection

Relationship between Depression Symptoms and Different Types of Measures of Obesity (BMI, SAD) in US Women

Objective. To estimate the relationship between obesity (defined by both BMI and SAD) and various levels of depressive symptoms in women in the United States. Methods. This is a cross-sectional design. All data were collected from NHANES 2011-2012 and 2013-2014. The Patient Health Questionnaire (PHQ-9) was the primary variable used to index depressive symptoms. SAD was assessed using an abdominal caliper. We stratified participates into three groups according to SAD (trisection): T1: low (11.8-18.4 cm), T2: middle (18.5-22.8 cm), and T3: high (22.9-40.1 cm). Other data were collected following the NHANES protocols. We aimed to investigate the effects of obesity on the depression in the NHANES populations. Results. A total of 4477 women were enrolled in the final study population. Participants with a high SAD had the highest risk of clinical depression symptoms (OR=1.2, 95% CI: 1.1-1.4), which was, in particular, the case for moderate-severe depression (OR=1.4, 95% CI: 1.1-1.7) and severe depression (OR=1.4, 95% CI: 1.0-1.9). We also found a significant relationship between SAD and BMI (r=0.836). We did, however, not find a significant relationship between BMI and severe depression. Conclusions. SAD had a better correlation with clinical depression symptoms than BMI, especially regarding severe depression symptoms