Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio

Friction and heat partition coefficients in incremental sheet forming process

To understand the thermo-mechanical behaviour in incremental sheet forming (ISF), it is important to precisely determine the interfacial and thermal-relevant parameters including coefficient of friction (COF) and heat partition coefficient (HPC), and to characterise the effect of thermo-mechanically induced heat generation under ISF processing conditions. In the present study, a new tool path-defined straight groove test combined with mechanical and thermal detection is proposed to determine the COF and HPC of Aluminium alloy (AA1050) and commercially pure titanium Grade 1 (CP Ti Grade 1) sheets. The experimental and numerical results show that the determined COF and HPC values are sufficiently accurate. The interaction between friction force and thermal response is observed by this testing method. A novel theoretical thermal model is developed to correlate the relationship between friction-induced heat generation and the thermal effect. The results indicate that the new theoretical model can capture the temperature distribution and variation under different processing conditions, and the results show a good agreement with the finite element (FE) simulation. The presented testing method and theoretical model provide an insight into the determination of the thermal-relevant parameters (COF and HPC), and the quantification of the effect of friction-induced heat generation on the thermal response of the materials

A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction

Effect of herbal medicine compound promotes beta cell function among type 2 diabetes (T2D) adults: A randomized controlled clinical trial

Background: Herbal medicine as complementary ways are needed for patients who refuse to take medicine for life. Early intervention and lifestyle could delay the time of taking drugs. Objective: To assess the efficacy of XiaokeGranules vs Sitagliptin in patients who diagnosed T2D. Methods: This is a randomized, double-blind trial. T2D patients were randomly assigned in a 1:1 ratio to Xiaoke Granules and Sitagliptin(50 mg), an anti-diabetic medicine group, for 12 weeks. Before randomization, the consenting participants were subjected to 2 weeks of screening, followed by twelve weeks of intervention. The primary outcome was change from baseline in glycosylated haemoglobin (HbA1C) and homeostasis model assessment-estimated insulin resistance (HOMA-IR) calculation at 12 week. Results: We recruited 103 patients, 84 patients participated in the clinical trial and finally 79 patients finished., the least square mean change in HbA1c and HOMA-IR from baseline was similar in both groups, in which HbA1c (6.93 ± 0.76, P = 0.007) and 2hPG (10.87 ± 2.20, P = 0.016) for the Control group, and HbA1c (6.76 ± 0.67, P < 0.001) and 2hPG (10.52 ± 2.46, P = 0.019). Conclusions: This study revealed that the herbal medicine Xiaoke Granules provides effective glycaemic control with similar safety and immunogenicity profile to Sitagliptin among T2D patients treated for 12 weeks. This study strongly suggests further studies and utilization of Xiaoke Granules usage for controlling hyperglycemia among T2D patients. Trial registration: ClinicalTrials: ChiCTR1900026782

Ultrafine Ir Nanowires with Microporous Channels and Superior Electrocatalytic Activity for Oxygen Evolution Reaction

Hydrogen energy is considered as an ideal candidate energy of traditional fossil fuels. Oxygen evolution reaction (OER) is a crucial reaction for the sustainable hydrogen generation as a half reaction of water splitting. It is important to develop efficient electrocatalysts for the OER. Herein, the porous iridium nanowires (Ir PNWs) with polyallylamine hydrochloride (PAH) as a complex-forming agent and capping agent have been successfully synthesized through a one-pot hydrothermal approach. Benefiting from the inherent anisotropic characteristic of one-dimensional nanowires structure, abundant microporous channels and high atom utilization efficiency, the synthesized Ir PNWs possess very large electrochemically active surface area (118.2 m(2) g(-1)), and thus exhibit superior electrocatalytic activity and stability towards the OER compared with commercial RuO2 catalyst and other Ir nanocrystals

Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width

Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021), we make a step towards drawing a phase diagram for three-layer ReLU NNs with infinite width. First, we derive a normalized gradient flow for three-layer ReLU NNs and obtain two key independent quantities to distinguish different dynamical regimes for common initialization methods. With carefully designed experiments and a large computation cost, for both synthetic datasets and real datasets, we find that the dynamics of each layer also could be divided into a linear regime and a condensed regime, separated by a critical regime. The criteria is the relative change of input weights (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) as the width approaches infinity during the training, which tends to $0$, $+\infty$ and $O(1)$, respectively. In addition, we also demonstrate that different layers can lie in different dynamical regimes in a training process within a deep NN. In the condensed regime, we also observe the condensation of weights in isolated orientations with low complexity. Through experiments under three-layer condition, our phase diagram suggests a complicated dynamical regimes consisting of three possible regimes, together with their mixture, for deep NNs and provides a guidance for studying deep NNs in different initialization regimes, which reveals the possibility of completely different dynamics emerging within a deep NN for its different layers.Comment: arXiv admin note: text overlap with arXiv:2007.0749

Unmanned air vehicle adaptability and application evaluation for new rice panicle fertilizers: Fertilizer characteristics and mechanical adaptability

High-efficiency fertilizers suitable for mechanical spraying can improve the fertilizer application and utilization efficiency. In this study, two types of granular compound fertilizers suitable for unmanned air vehicle spraying were developed using urea, potassium chloride, humic acid, and an amino acid synergist according to the fertilization requirements of rice at the booting stage. The physical and chemical properties of the compound fertilizers were analyzed by Fourier transform infrared spectroscopy and scanning electron microscopy. The release kinetics of the fertilizer was studied via a soil column leaching experiment. The effect of fertilizer particles on unmanned air vehicle spraying uniformity was studied via an orthogonal simulation experiment. Results showed that the chemical interaction between compound-fertilizer raw materials formed complexes such as C-NH-C and -CO-NH-, which caused a change in fertilizer particle morphology. This led to chemical monolayer adsorption or internal fertilizer diffusion, which in turn delayed the fertilizer release by 5−10 d. A simulation bench test showed that the fertilizer particle hardness and roundness exceeded 20 N and 85%, respectively, while the particle size was in the range of 2.07−5.13 mm, which resulted in good unmanned air vehicle spraying uniformity with a lateral variation coefficient of less than 15%. Finally, the experimental field results demonstrated that the new compound fertilizers, when sprayed by an unmanned air vehicle, could improve the economic benefit by 916−2742 CNY ha−1 without reducing the nitrogen utilization rate and rice yield. The new compound fertilizers exhibit delayed release, good spraying uniformity, and improved economic benefit, rendering them suitable for large-scale promotion and application