Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device

The safety of children in children home has become an increasing social concern, and the purpose of this experiment is to use machine learning applied to detect the scenarios of child abuse to increase the safety of children. This experiment uses machine learning to classify and recognize a child's voice and predict whether the current sound made by the child is crying, screaming or laughing. If a child is found to be crying or screaming, an alert is immediately sent to the relevant personnel so that they can perceive what the child may be experiencing in a surveillance blind spot and respond in a timely manner. Together with a hybrid use of video image classification, the accuracy of child abuse detection can be significantly increased. This greatly reduces the likelihood that a child will receive violent abuse in the nursery and allows personnel to stop an imminent or incipient child abuse incident in time. The datasets collected from this experiment is entirely from sounds recorded on site at the children home, including crying, laughing, screaming sound and background noises. These sound files are transformed into spectrograms using Short-Time Fourier Transform, and then these image data are imported into a CNN neural network for classification, and the final trained model can achieve an accuracy of about 92% for sound detection.Comment: 5 pages, 7 figures, PRAI 202

Generalized-Equiangular Geometry CT: Concept and Shift-Invariant FBP Algorithms

With advanced X-ray source and detector technologies being continuously developed, non-traditional CT geometries have been widely explored. Generalized-Equiangular Geometry CT (GEGCT) architecture, in which an X-ray source might be positioned radially far away from the focus of arced detector array that is equiangularly spaced, is of importance in many novel CT systems and designs. GEGCT, unfortunately, has no theoretically exact and shift-invariant analytical image reconstruction algorithm in general. In this study, to obtain fast and accurate reconstruction from GEGCT and to promote its system design and optimization, an in-depth investigation on a group of approximate Filtered BackProjection (FBP) algorithms with a variety of weighting strategies has been conducted. The architecture of GEGCT is first presented and characterized by using a normalized-radial-offset distance (NROD). Next, shift-invariant weighted FBP-type algorithms are derived in a unified framework, with pre-filtering, filtering, and post-filtering weights. Three viable weighting strategies are then presented including a classic one developed by Besson in the literature and two new ones generated from a curvature fitting and from an empirical formula, where all of the three weights can be expressed as certain functions of NROD. After that, an analysis of reconstruction accuracy is conducted with a wide range of NROD. We further stretch the weighted FBP-type algorithms to GEGCT with dynamic NROD. Finally, the weighted FBP algorithm for GEGCT is extended to a three-dimensional form in the case of cone-beam scan with a cylindrical detector array.Comment: 31 pages, 13 figure

Gender-specific association of decreased estimated glomerular filtration rate and left vertical geometry in the general population from rural Northeast China

Abstract Background Left ventricular hypertrophy (LVH) is common and associated with cardiovascular outcomes among patients with known chronic kidney disease (CKD). However, the link between decreased estimated glomerular filtration rate (eGFR) and left ventricular (LV) geometry remains poorly explored in general population. In this study, we examined the gender-specific association between eGFR and LVH in the general population from rural Northeast China. Methods This survey was conducted from July 2012 to August 2013. A total of 10907 participants (5,013 men and 5,894 women) from the rural Northeast China were randomly selected and examined. LV mass index (LVMI) was used to define LVH (LVMI\u2009>\u200946.7\ua0g/m 2.7 in women; > 49.2\ua0g/m 2.7 in men). LV geometry was defined as normal, or with concentric remodeling, eccentric or concentric hypertrophy, according to relative wall thickness (RWT) and LVMI. Mildly decreased eGFR was defined as eGFR\u2009\u2265\u200960 and\u2009<\u200990\ua0ml/min/1.73\ua0m 2 , and moderate-severely decreased eGFR was defined as eGFR\u2009<\u200960\ua0ml/min/1.73\ua0m 2 . Results As eGFR decreased, LVH showed a gradual increase in the entire study population. Multivariate analysis revealed a gender-specific relationship between eGFR and LV geometry. Only in men, mildly decreased eGFR was associated with concentric remodeling [odds ratio (OR): =1.58; 95% CI: 1.14\u20132.20; P \u2009<\u20090.01] and concentric LVH OR \u2009=\u20091.63; 95% CI: 1.15\u20132.31; P \u2009<\u20090.01). And only in men, moderate-severely decreased eGFR was a risk factor for concentric LVH ( OR \u2009=\u20094.56; 95% CI: 2.14\u20139.73; P \u2009<\u20090.001) after adjusting for confounding factors. Conclusions These findings suggested that decreased eGFR was a risk factor for LV geometry in men, and a gender-specific difference should be taken into account in clinical practice

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%

Identification of Ligularia Herbs Using the Complete Chloroplast Genome as a Super-Barcode

More than 30 Ligularia Cass. (Asteraceae) species have long been used in folk medicine in China. Morphological features and common DNA regions are both not ideal to identify Ligularia species. As some Ligularia species contain pyrrolizidine alkaloids, which are hazardous to human and animal health and are involved in metabolic toxification in the liver, it is important to find a better way to distinguish these species. Here, we report complete chloroplast (CP) genomes of six Ligularia species, L. intermedia, L. jaluensis, L. mongolica, L. hodgsonii, L. veitchiana, and L. fischeri, obtained through high-throughput Illumina sequencing technology. These CP genomes showed typical circular tetramerous structure and their sizes range from 151,118 to 151,253 bp. The GC content of each CP genome is 37.5%. Every CP genome contains 134 genes, including 87 protein-coding genes, 37 tRNA genes, eight rRNA genes, and two pseudogenes (ycf1 and rps19). From the mVISTA, there were no potential coding or non-coding regions to distinguish these six Ligularia species, but the maximum likelihood tree of the six Ligularia species and other related species showed that the whole CP genome can be used as a super-barcode to identify these six Ligularia species. This study provides invaluable data for species identification, allowing for future studies on phylogenetic evolution and safe medical applications of Ligularia

Defect-free graphene enhances enzyme delivery to fibroblasts derived from patients with lysosomal storage disorders

Enzyme replacement therapy shows remarkable clinical improvement in treating lysosomal storage disorders. However, this therapeutic approach is hampered by limitations in the delivery of the enzyme to cells and tissues. Therefore, there is an urgent, unmet clinical need to develop new strategies to enhance the enzyme delivery to diseased cells. Graphene-based materials, due to their dimensionality and favourable pattern of interaction with cells, represent a promising platform for the loading and delivery of therapeutic cargo. Herein, the potential use of graphene-based materials, including defect-free graphene with positive or negative surface charge and graphene oxide with different lateral dimensions, was investigated for the delivery of lysosomal enzymes in fibroblasts derived from patients with Mucopolysaccharidosis VI and Pompe disease. We report excellent biocompatibility of all graphene-based materials up to a concentration of 100 μg mL in the cell lines studied. In addition, a noticeable difference in the uptake profile of the materials was observed. Neither type of graphene oxide was taken up by the cells to a significant extent. In contrast, the two types of graphene were efficiently taken up, localizing in the lysosomes. Furthermore, we demonstrate that cationic graphene flakes can be used as carriers for arylsulfatase B enzyme, for the delivery of the lacking enzyme to the lysosomes of Mucopolysaccharidosis VI fibroblasts. Arylsulfatase B complexed with cationic graphene flakes not only retained the enzymatic activity, but also exerted biological effects almost twice as high as arylsulfatase B alone in the clearance of the substrate in Mucopolysaccharidosis VI fibroblasts. This study lays the groundwork for the potential use of graphene-based materials as carriers for enzyme replacement therapy in lysosomal storage disorders