CA-CentripetalNet: A novel anchor-free deep learning framework for hardhat wearing detection

Automatic hardhat wearing detection can strengthen the safety management in construction sites, which is still challenging due to complicated video surveillance scenes. To deal with the poor generalization of previous deep learning based methods, a novel anchor-free deep learning framework called CA-CentripetalNet is proposed for hardhat wearing detection. Two novel schemes are proposed to improve the feature extraction and utilization ability of CA-CentripetalNet, which are vertical-horizontal corner pooling and bounding constrained center attention. The former is designed to realize the comprehensive utilization of marginal features and internal features. The latter is designed to enforce the backbone to pay attention to internal features, which is only used during the training rather than during the detection. Experimental results indicate that the CA-CentripetalNet achieves better performance with the 86.63% mAP (mean Average Precision) with less memory consumption at a reasonable speed than the existing deep learning based methods, especially in case of small-scale hardhats and non-worn-hardhats.Comment: It has been accepted for the journal of Signal, Image and Video Processing, which is a complete version. It is noted that it has been deleted for future publishin

Eco-Stoichiometric Alterations in Paddy Soil Ecosystem Driven by Phosphorus Application

Agricultural fertilization may change processes of elemental biogeochemical cycles and alter the ecological function. Ecoenzymatic stoichiometric feature plays a critical role in global soil carbon (C) metabolism, driving element cycles, and mediating atmospheric composition in response to agricultural nutrient management. Despite the importance on crop growth, the role of phosphorous (P) in compliance with eco-stoichiometry on soil C and nitrogen (N) sequestration in the paddy field remains poorly understood in the context of climate change. Here, we collected soil samples from a field experiment after 6 years of chemical P application at a gradient of 0 (P-0), 30 (P-30), 60 (P-60), and 90 (P-90) kg ha⁻¹ in order to evaluate the role of P on stoichiometric properties in terms of soil chemical, microbial biomass, and eco-enzyme activities as well as greenhouse gas (GHG: CO₂, N₂O and CH₄) emissions. Continuous P input increased soil total organic C and N by 1.3–9.2% and 3%–13%, respectively. P input induced C and N limitations as indicated by the decreased ratio of C:P and N:P in the soil and microbial biomass. A synergistic mechanism among the ecoenzymatic stoichiometry, which regulated the ecological function of microbial C and N acquisition and were stoichiometrically related to P input, stimulated soil C and N sequestration in the paddy field. The lower emissions of N₂O and CH₄ under the higher P application (P-60 and P-90) in July and the insignificant difference in N₂O emission in August compared to P-30; however, continuous P input enhanced CO₂ fluxes for both samplings. There is a technical conflict for simultaneously regulating three types of GHGs in terms of the eco-stoichiometry mechanism under P fertilization. Thus, it is recommended that the P input in paddy fields not exceed 60 kg ha⁻¹ may maximize soil C sequestration, minimize P export, and guarantee grain yields

Challenge Analysis and Schemes Design for the CFD Simulation of PWR

CFD simulation for a PWR is an important part for the development of Numerical Virtual Reactor (NVR) in Harbin Engineering University of China. CFD simulation can provide the detailed information of the flow and heat transfer process in a PWR. However, a large number of narrow flow channels with numerous complex structures (mixing vanes, dimples, springs, etc.) are located in a typical PWR. To obtain a better CFD simulation, the challenges created by these structural features were analyzed and some quantitative regularity and estimation were given in this paper. It was found that both computing resources and time are in great need for the CFD simulation of a whole reactor. These challenges have to be resolved, so two schemes were designed to assist/realize the reduction of the simulation burden on resources and time. One scheme is used to predict the combined efficiency of the simulation conditions (configuration of computing resources and application of simulation schemes), so it can assist the better choice/decision of the combination of the simulation conditions. The other scheme is based on the suitable simplification and modification, and it can directly reduce great computing burden

Numerical Investigation of the Effect of Grids and Turbulence Models on Critical Heat Flux in a Vertical Pipe

Numerical simulation has been widely used in nuclear reactor safety analyses to gain insight into key phenomena. The Critical Heat Flux (CHF) is one of the limiting criteria in the design and operation of nuclear reactors. It is a two-phase flow phenomenon, which rapidly decreases the heat transfer performance at the rod surface. This paper presents a numerical simulation of a steady state flow in a vertical pipe to predict the CHF phenomena. The detailed Computational Fluid Dynamic (CFD) modeling methodology was developed using FLUENT. Eulerian two-phase flow model is used to model the flow and heat transfer phenomena. In order to gain the peak wall temperature accurately and stably, the effect of different turbulence models and wall functions are investigated based on different grids. Results show that O type grid should be used for the simulation of CHF phenomenon. Grids with Y+ larger than 70 are recommended for the CHF simulation because of the acceptable results of all the turbulence models while Grids with Y+ lower than 50 should be avoided. To predict the dry-out position accurately in a fine grid, Realizable k-ε model with standard wall function is recommended. These conclusions have some reference significance to better predict the CHF phenomena of vertical pipe. It can also be expanded to rod bundle of Boiling Water Reactor (BWR) by using same pressure condition

A New Approach for the Preparation of Variable Valence Rare Earth Alloys from Nano Rare Earth Oxides at a Low Temperature in Molten Salt

The solubility of RE2O3 (RE = Eu, Sm, and Yb) with variable valence in molten salts is extremely low. It is impossible to directly obtain variable valence metals or alloys from RE2O3 using electrolysis in molten salts. We describe a new approach for the preparation of variable valence rare earth alloys from nano rare earth oxide. The excellent dispersion of nano–Eu2O3 in LiCl–KCl melts was clearly observed using a luminescent feature of Eu3+ as a probe. The ratio of solubility of nano-Sm2O3/common Sm2O3 is 16.98. Electrochemical behavior of RE2O3 on a molybdenum and Al electrode in LiCl–KCl melts containing AlCl3 at 480 °C was investigated by different electrochemical techniques, such as cyclic voltammetry (CV), square wave voltammetry, and chronopotentiometry. Prior to the reduction peak of Al, the reduction peaks of Sm(III)/Sm(II), Yb(III)/Yb(II), and Eu(III)/Eu(II) were observed at about −0.85, −0.45, and 0.39 V insquare wave voltammetry, respectively. The underpotential deposition of RE on pre-deposited aluminum leads to the formation of Al–RE alloy. The structure, morphology, and energy dispersion analysis of the deposit obtained by potentiostatic electrolysis are analyzed. Al2Sm and Al3Sm alloys were successfully obtained from nano–Sm2O3

Simultaneous treatment of phosphorus and fluoride wastewater using acid-modified iron-loaded electrode capacitive deionization: Preparation and performance

Here, capacitive deionization technology (CDI) using modified activated carbon fiber felt (ACF) electrodes was proposed to provide a new strategy for the challenge of simultaneous phosphorus and fluoride wastewater treatment. The acid-modified iron-loaded ACF (A@Fe-ACF) was obtained by modifying ACF through a two-step impregnation method. After the modification, the oxygen-containing functional groups on ACF increased and provided more adsorption sites. The electron transfer efficiency on the A@Fe-ACF was increased by introducing Fe and synergistically promoted the adsorption of phosphorus and fluorine. Results showed that the removal efficiencies of total phosphorus (TP) and total fluorine (TF) in wastewater reached 89.4% and 85% under optimal conditions (voltage intensity 1.5 V, pH 7, plate spacing 1 cm), while the adsorption mechanism of phosphorus and fluorine was dominated by chemical adsorption. Meanwhile, A@Fe-ACF electrode has good recyclability and stability after five cycles

SdPI, The First Functionally Characterized Kunitz-Type Trypsin Inhibitor from Scorpion Venom

Background: Kunitz-type venom peptides have been isolated from a wide variety of venomous animals. They usually have protease inhibitory activity or potassium channel blocking activity, which by virtue of the effects on predator animals are essential for the survival of venomous animals. However, no Kunitz-type peptides from scorpion venom have been functionally characterized. Principal Findings: A new Kunitz-type venom peptide gene precursor, SdPI, was cloned and characterized from a venom gland cDNA library of the scorpion Lychas mucronatus. It codes for a signal peptide of 21 residues and a mature peptide of 59 residues. The mature SdPI peptide possesses a unique cysteine framework reticulated by three disulfide bridges, different from all reported Kunitz-type proteins. The recombinant SdPI peptide was functionally expressed. It showed trypsin inhibitory activity with high potency (Ki = 1.6610 27 M) and thermostability. Conclusions: The results illustrated that SdPI is a potent and stable serine protease inhibitor. Further mutagenesis and molecular dynamics simulation revealed that SdPI possesses a serine protease inhibitory active site similar to other Kunitztype venom peptides. To our knowledge, SdPI is the first functionally characterized Kunitz-type trypsin inhibitor derive

Investigation on unfrozen water content models of freezing soils

Unfrozen water content is a significant hydro-thermal property in numerical modeling in cold regions. Although numerous models have been developed to mimic the variation of unfrozen water content with subzero temperature, comprehensive evaluation of unfrozen water content models is scarce. This study collected a total of 29 models and divided them into four categories, namely, theoretical models, soil water characteristic curve (SWCC)-based models, empirical models, and estimation models. These models were evaluated with 1278 experimental points from 16 studies covering multiple soil types, including 24 clays, 18 silty clays, 7 silts, 19 sands, and 10 sandstones. Root mean square error and average deviations were applied to judge the performance of these models. Most unfrozen water content models can well simulate the relationship between unfrozen water content and subzero temperature. Among the aforementioned four categories of unfrozen water content models, Lizhm et al. model, Fredlund and Xing (C=1)-Wen model, Kozlowski empirical model, and Kozlowski estimation model performed best in their respective categories. Compared to the rest three categories, estimation models can be applied to predict the variation of unfrozen water content with subzero temperature by some easy-to-obtain soil physical parameters and provide guidance for the development of unfrozen water content models