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Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R2 = 0.62), outperformed models driven solely by spectral bands (R2 = 0.33) or vegetation indices (R2 = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m2). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. [ABSTRACT FROM AUTHOR]

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A2NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A2NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A2NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A2NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A2NNs (Q-A2NNs) composed of quantized adder filters have lower computational and memory overheads than A2NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A2NNs exhibit significantly reduced RSSC accuracy compared to A2NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A2NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A2NNs to obtain Q-A2NNs with low computational and memory overheads while maintaining comparable performance to A2NNs, thus having high potential for onboard RSSC. [ABSTRACT FROM AUTHOR]

The vibration signals collected by acceleration sensors are interspersed with noise interference, which increases the difficulty of fault diagnosis for rolling bearings. For this reason, a rolling bearing fault diagnosis method based on complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) and improved convolutional neural network (CNN) is proposed. Firstly, the original vibration signal is decomposed into a series of intrinsic modal function (IMF) components using the CEEMDAN algorithm, the components are filtered according to the correlation coefficients and the signals are reconstructed. Secondly, the reconstructed signals are converted into a two-dimensional grey-scale map and input into a convolutional neural network to extract the features. Lastly, the features are inputted into a support vector machine (SVM) with the optimised parameters of the grey wolf optimiser (GWO) to perform the identification and classification. The experimental results show that the rolling bearing fault diagnosis method based on CEEMDAN and CNN-SVM proposed in this paper can significantly reduce the noise interference, and its average fault diagnosis accuracy is as high as 99.25%. Therefore, it is feasible to apply it in the field of rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Rice–duckweed coculturing as an advanced technique has proven effective for weed control. However, the complex environmental interactions underlying its effectiveness remain unclear. In this study, a controlled pot experiment was conducted to isolate the shading effect of duckweeds (Landoltia punctata and Spirodela polyrhiza) from their intricate environmental interactions with paddy weeds (Cyperus difformis, Eclipta prostrata, and Ammannia auriculata). The results revealed that as the duckweeds grew, there were notable decreases in total weed density (52.41–77.01%) and biomass (42.33–99.23%) while rice biomass remained unaffected. Duckweed covering allowed less than 8.64% of light to pass through and led to decreases in environmental temperature, pH, and dissolved oxygen concentration in the floodwater, but had little impact on inorganic nitrogen levels. Through screening with random forest analysis and structural equation modelling, it was found that weed density and biomass were predominantly influenced by the reduced light transmittance (54.1% and 77.2%, p ≤ 0.001) resulting from duckweed covering while the decline in dissolved oxygen content partly influenced weed density (18%). In conclusion, this study's results bring a different perspective, confirming the sunlight-shading effect of duckweed as a critical mechanism significantly inhibiting the germination and growth of weeds. These findings provide a scientific basis for the improved application of ecological weed control methods in paddy fields. [ABSTRACT FROM AUTHOR]

Ferroptosis is an emerging type of regulated cell death usually accompanied by the accumulation of ferrous ions (Fe2+) and lipid peroxides. As the metabolic hub of the body, the liver is crucial for iron storage and lipid metabolism. The liver seems to be closely related to ferroptosis through iron and lipid metabolism. Liver disease greatly threatens host health, and exploring effective interventions is essential. Mounting studies have demonstrated that ferroptosis is one of the possible pathogenic mechanisms involved in liver disease. Targeting ferroptosis may provide a promising opportunity for treating liver disease. However, drugs targeting ferroptosis are extremely limited. Therefore, it is an urgent need to develop new and safe ferroptosis regulators. Natural active compounds (NAC), especially those derived from traditional Chinese medicine, have recently shown great therapeutic potential in liver disease via modulating ferroptosis-related genes or pathways. Here, we outline the molecular mechanism of ferroptosis and systematically summarize the regulatory function of NAC on ferroptosis in liver disease. Finally, we discuss the application prospects and potential problems concerning NAC as ferroptosis regulators for managing liver disease. [ABSTRACT FROM AUTHOR]

The construction defects in grouting sleeves can jeopardize the safety of precast reinforced concrete structures. Thus, efficient and accurate defect detection is critical in engineering construction. In this paper, a defect detection method based on piezoelectric wave theory was proposed. Two piezoelectric ceramics were arranged within the grouting sleeve. One piezoelectric ceramic was affixed on the top of the steel bar, while the other was embedded in the grouting material, serving as the driver and sensor, respectively. The compactness defects, air cavity defects, steel anchoring defects, and water–binder ratio defects were set in the grouting sleeves, and the trends in time domain signals and signal evaluation indicators based on the wavelet packet total energy value (WPTEV) of different specimens were investigated. Based on the WPTEV, the evaluation index (EI) was proposed. In addition, the effect of the grouting material's age on the piezoelectric wave signal was verified. Furthermore, the influence of grouting material defects on signals was simulated in ABAQUS, and the time domain signals and wavelet packet energy of sensor signals for specimens with varying defect dimensions were evaluated. The results showed that: (1) The defects in the grouting materials reduced the stress waves propagated through the grouting materials, resulting in a decrease in attenuation and an increasing trend in the signal; (2) The peak-to-peak values of piezoelectric ceramic sensors and the WPTEV of the signal increased with the degree of defects, and the WPTEV was more pronounced. For specimen M68, the WPTEV was 3.3 times that of the healthy specimen, however, the peak-to-peak value was only 2.3 times that of the healthy specimen; (3) The degree of defects was accurately determined by the defect evaluation index (EI) based on the WPTEV; (4) The signal was significantly attenuated with the increase in the age of the grouting material, especially in the first three days, and finally the signal achieved a stable value; (5) The numerical simulation indicated that the defects in the length and thickness of the air cavity in the grouting material were efficiently detected by the proposed piezoelectric arrangement in this study. [ABSTRACT FROM AUTHOR]

In order to improve the shale oil production rate and save fracturing costs, based on dynamic production data, a production-oriented optimization method for fracture spacing of multi-stage fractured horizontal wells is proposed in this study. First, M. Brown et al.'s trilinear seepage flow models and their pressure and flow rate solutions are applied. Second, deconvolution theory is introduced to normalize the production data. The data of variable pressure and variable flow rate are, respectively, transformed into the pressure data under unit flow rate and the flow rate data under unit production pressure drop; and the influence of data error is eliminated. Two kinds of typical curve of the normalized data are analyzed using the pressure and flow rate solutions of M. Brown et al.'s models. The two fitting methods constrain each other. Thus, reservoir and fracture parameters are interpretated. A practical model has been established to more accurately describe the seepage flow behavior in shale oil reservoirs. Third, using Duhamel's principle and the rate solution, the daily and cumulative production rate under any variable production pressure can be obtained. The productivity can be more accurately predicted. Finally, the analysis method is applied to analyze the actual dynamic production data. The fracture spacing of a shale oil producing well in an actual block is optimized from the aspects of production life, cumulative production, economic benefits and other influencing factors, and some significant conclusions are obtained. The research results show that with the goal of maximum cumulative production, the optimal fracture spacing is 5.5 m for 5 years and 11.4 m for 10 years. All in all, the fracture spacing optimization and design theory of multi-stage fractured horizontal wells is enriched. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of the covalent binding of flaxseed protein (FP) and chlorogenic acid (CA) on the structure and functional properties of FP–CA complexes fabricated using the alkali method. The results suggested that the encapsulation efficiency of CA encapsulated by FP ranged from 66.11% to 72.20% and the loading capacity of CA increased with an increasing addition ratio of CA with a dose-dependent relationship, which increased from 2.34% to 10.19%. The particle size, turbidity, zeta potential and PDI of FP and the FP–CA complexes had no significant discrepancy. UV–Vis and fluorescence spectra showed the existence of the interaction between FP and CA. SEM images showed that the surface of the FP–0.35%CA complex had more wrinkles compared to FP. Differential scanning calorimetry analysis indicated the decomposition temperature of FP at 198 °C was higher than that (197 °C) of the FP–0.35%CA complex, implying that the stability of the FP–CA complexes was lower than FP. The functional properties suggested that the FP–CA complex with 1.40% CA had a higher water holding capacity (500.81%), lower oil holding capacity (273.495%) and lower surface hydrophobicity. Moreover, the FP–CA complexes had better antioxidant activities than that of FP. Therefore, this study provides more insights for the potential application of FP–CA covalent complexes in functional food processing. [ABSTRACT FROM AUTHOR]

Lactobacillus plantarum is a kind of probiotic that benefits the host by regulating the gut microbiota, but it is easily damaged when passing through the gastrointestinal tract, hindering its ability to reach the destination and reducing its utilization value. Encapsulation is a promising strategy for solving this problem. In this study, transglutaminase (TGase)-crosslinked gelatin (GE)/sodium hexametaphosphate (SHMP) hydrogels were used to encapsulate L. plantarum. The effects of TGase concentration and drying method on the physiochemical properties of the hydrogels were determined. The results showed that at a TGase concentration of 9 U/gGE, the hardness, chewiness, energy storage modulus, and apparent viscosity of the hydrogel encapsulation system were maximized. This concentration produced more high-energy isopeptide bonds, strengthening the interactions between molecules, forming a more stable three-dimensional network structure. The survival rate under the simulated gastrointestinal conditions and storage stability of L. plantarum were improved at this concentration. The thermal stability of the encapsulation system dried via microwave vacuum freeze drying (MFD) was slightly higher than that when dried via freeze drying (FD). The gel structure was more stable, and the activity of L. plantarum decreased more slowly during the storage period when dried using MFD. This research provides a theoretical basis for the development of encapsulation technology of probiotics. [ABSTRACT FROM AUTHOR]

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly. [ABSTRACT FROM AUTHOR]

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed. [ABSTRACT FROM AUTHOR]

Most energy-saving testing methods for plunger pumps use hydraulic motors. The loading test of coal mine emulsion pumps generally uses an overflow valve as the loading unit, which is characterized by high energy consumption. The coal mine emulsion pump uses emulsion as the transmission medium, and the viscosity and lubricity of the emulsion are much lower than those of hydraulic oil, which creates great difficulties in the development of high water-based hydraulic products. The nominal flow rate of the emulsion motor is much smaller than that of the emulsion pump, and there is no mature and reliable water-based flow control valve. Based on the above reasons, traditional energy-saving testing methods cannot be utilized for the testing process of emulsion pumps. The loading test of emulsion pumps generally uses an overflow valve as the loading unit, and during the testing process, all electrical energy is converted into internal energy, resulting in very high energy consumption. This article proposes an energy-saving testing system for emulsion pumps based on multiple emulsion motors in parallel. In order to solve the flow regulation problem of each parallel branch, a flow-intelligent control algorithm is proposed that utilizes the pressure difference flow characteristics of digital relief valves combined with artificial neural network predictive control. Firstly, the feasibility of the proposed system and method is theoretically verified through the analysis of the mathematical model of the digital relief valve. Secondly, further verification is carried out by establishing simulation and testing platforms. The simulation results show that the energy recovery efficiency of the system exceeds 53%. The experimental results show that the proposed testing system has a pressure control error of less than 1%, a flow control error of about 5%, and a maximum overshoot of about 9 L/min relative to the steady-state flow rate. The control accuracy and system stability are high. [ABSTRACT FROM AUTHOR]

The application of flexible indium tin oxide (ITO-free) electrochromic devices has steadily attracted widespread attention in wearable devices. Recently, silver nanowire/poly(dimethylsiloxane) (AgNW/PDMS)-based stretchable conductive films have raised great interest as ITO-free substrate for flexible electrochromic devices. However, it is still difficult to achieve high transparency with low resistance due to the weak binding force between AgNW and PDMS with low surface energy because of the possibility of detaching and sliding occurring at the interface. Herein, we propose a method to pattern the pre-cured PDMS (PT-PDMS) by stainless steel film as a template through constructed micron grooves and embedded structure, to prepare a stretchable AgNW/PT-PDMS electrode with high transparency and high conductivity. The stretchable AgNW/PT-PDMS electrode can be stretched (5000 cycles), twisted, and surface friction (3M tape for 500 cycles) without significant loss of conductivity (ΔR/R ≈ 16% and 27%). In addition, with the increase of stretch (stretching to 10–80%), the AgNW/PT-PDMS electrode transmittance increased, and the conductivity increased at first and then decreased. It is possible that the AgNWs in the micron grooves are spread during PDMS stretching, resulting in a larger spreading area and higher transmittance of the AgNWs film; at the same time, the nanowires between the grooves come into contact, thus increasing conductivity. An electrochromic electrode constructed with the stretchable AgNW/PT-PDMS exhibited excellent electrochromic behavior (transmittance contrast from ~61% to ~57%) even after 10,000 bending cycles or 500 stretching cycles, indicating high stability and mechanical robustness. Notably, this method of preparing transparent stretch electrodes based on patterned PDMS provides a promising solution for developing electronic devices with unique structures and high performance. [ABSTRACT FROM AUTHOR]

Improvements in and the assessment of land use efficiency are crucial pillars for achieving the Sustainable Development Goals (SDGs). This study reviews 208 representative papers, oral reports, and project reports to provide a systematic and comprehensive understanding of the current status and future trends of research on land use efficiency assessment. The findings reveal that (1) the number of papers on land use efficiency assessment is rapidly increasing, with research primarily focused on environmental science and ecology (n = 157, 75.48%). (2) Quantitative models are gaining popularity for land use efficiency assessment, with more than 46.63% of the studies adopting the data envelopment analysis (DEA) model. (3) The definition and analysis perspectives of land use efficiency are diverse, but research on relative land use efficiency and comprehensive analysis perspectives accounts for a significant proportion. (4) Constructing a large and complex model that incorporates geospatial effects, big data, and computer technology is a hot topic for future research methods. On the other hand, conducting land use efficiency research on a global scale is more conducive to achieving the SDGs. (5) The core to improving land use efficiency lies in the joint implementation of multi-pronged measures. [ABSTRACT FROM AUTHOR]

With the continuous development of conventional oil and gas resources, the strategic transformation of energy structure is imminent. Shale condensate gas reservoir has high development value because of its abundant reserves. However, due to the multi-scale flow of shale gas, adsorption and desorption, the strong stress sensitivity of matrix and fractures, the abnormal condensation phase transition mechanism, high-speed non-Darcy seepage in artificial fractures, and heterogeneity of reservoir and multiphase flows, the multi-scale nonlinear seepage mechanisms are extremely complicated in shale condensate gas reservoirs. A certain theoretical basis for the engineering development can be provided by mastering the percolation law of shale condensate gas reservoirs, such as improvement of productivity prediction and recovery efficiency. The productivity evaluation method of shale condensate gas wells based on empirical method is simple in calculation but poor in reliability. The characteristic curve analysis method has strong reliability but a great dependence on the selection of the seepage model. The artificial intelligence method can deal with complex data and has a high prediction accuracy. Establishing an efficient shale condensate gas reservoir development simulation technology and accurately predicting the production performance of production wells will help to rationally formulate a stable and high-yield mining scheme, so as to obtain better economic benefits. [ABSTRACT FROM AUTHOR]

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures. [ABSTRACT FROM AUTHOR]

The dynamic productivity prediction of shale condensate gas reservoirs is of great significance to the optimization of stimulation measures. Therefore, in this study, a dynamic productivity prediction method for shale condensate gas reservoirs based on a convolution equation is proposed. The method has been used to predict the dynamic production of 10 multi-stage fractured horizontal wells in the Duvernay shale condensate gas reservoir. The results show that flow-rate deconvolution algorithms can greatly improve the fitting effect of the Blasingame production decline curve when applied to the analysis of unstable production of shale gas condensate reservoirs. Compared with the production decline analysis method in commercial software HIS Harmony RTA, the productivity prediction method based on a convolution equation of shale condensate gas reservoirs has better fitting affect and higher accuracy of recoverable reserves prediction. Compared with the actual production, the error of production predicted by the convolution equation is generally within 10%. This means it is a fast and accurate method. This study enriches the productivity prediction methods of shale condensate gas reservoirs and has important practical significance for the productivity prediction and stimulation optimization of shale condensate gas reservoirs. [ABSTRACT FROM AUTHOR]

Field tests indicate that temporary well shut-ins may enhance oil recovery from a shale reservoir; however, there is currently no systematic research to specifically guide such detailed operations in the field, especially for the design of the shut-in scheme and multiple rounds of shut-ins. In this study, the applicability of well shut-in operations for shale oil reservoirs is studied, and a numerical model is built using the finite element method. In order to simulate the production in a shale oil reservoir, two separate modules (i.e., Darcy's law and phase transport) were two-way coupled together. The established model was validated by comparing its results with the analytical Buckley–Leverett equation. In this paper, the geological background and parameters of a shale oil reservoir in Chang-7 Member (Chenghao, China) were used for the analyses. The simulation results show that temporary well shut-in during production can significantly affect well performance. Implementing well shut-in could decrease the initial oil rate while decreasing the oil decline rate, which is conducive to long-term production. After continuous production for 1000 days, the oil rate with 120 days shut-in was 9.85% larger than the case with no shut-in. Besides, an optimal shut-in time has been identified as 60 days under our modeling conditions. In addition, the potential of several rounds of well shut-in operations was also tested in this study; it is recommended that one or two rounds of shut-ins be performed during development. When two rounds of shut-ins are implemented, it is recommended that the second round shut-in be performed after 300 days of production. In summary, this study reveals the feasibility of temporary well shut-in operations in the development of a shale oil reservoir and provides quantitative guidance to optimize these development scenarios. [ABSTRACT FROM AUTHOR]

To achieve the goal of decarbonized energy and greenhouse gas reduction, underground gas storage (UGS) has proven to be an important source for energy storage and regulation of natural gas supply. The special working conditions in UGS cause offset vertical wells to easily interfere with target vertical wells. The current well testing methodology assumes that there is only one well, and the interference from offset wells is ignored. This paper proposes a solution and analysis method for the interference from adjacent vertical wells to target vertical wells by analytical theory. The model solution is obtained by the solution with a constant rate and the Laplace transform method. The pressure superposition is used to deal with the interference from adjacent vertical wells. The model reliability in the gas injection and production stages is verified by commercial software. Pressure analysis shows that the heterogeneity and interference in the gas storage are caused by long-term gas injection and production. As both the adjacent well and the target well are in the gas production stage, the pressure derivative value in radial flow is related to production rate, mobility ratio, and 0.5. Gas injection from offset wells will cause the pressure derivative to drop later. Multiple vertical wells from the Hutubi UGS are used to illustrate the properties of vertical wells and the formation. [ABSTRACT FROM AUTHOR]

The mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the small number of plungers in the emulsion pump will lead to large flow pulsation and pressure pulsation, and the nominal flow of different types of emulsion pumps varies greatly. These factors lead to the deficiency of a traditional PID control algorithm in control accuracy and efficiency. In order to improve control accuracy and efficiency, firstly, the influence of the flow rate of the tested pump and extension of the linear stepping motor shaft on the working pressure is studied. A backpropagation (BP) artificial neural network (ANN) model is used to fit a functional relationship between the three parameters. The flow rate of the tested pump and target pressure were provided as inputs to predict the extension of the linear stepping motor shaft, thereby realizing the remote intelligent control of the system pressure. Next, a BP ANN model is constructed, and its reliability is verified; the BP neural network algorithm and proportional-integral-derivative (PID) algorithm are compared through simulation. The simulation results show that the BP neural network algorithm has high control accuracy and small overshoot. Finally, two pumps with different flows are tested in a self-developed digital relief valve and test platform. The test results show that the proposed loading test method is intelligent and efficient, and it has high accuracy. [ABSTRACT FROM AUTHOR]

This study evaluates the effects of a broad-spectrum antibiotic (florfenicol) on antibiotic resistance genes (ARGs) and bacterial community structure in aquatic environments. We constructed an indoor aquatic microcosm model, adding different concentrations of florfenicol (0.1, 1, 10, 100 mg L−1), and water and sediment samples were collected after 0, 7, 30, and 60 days. qPCR and 16S rDNA amplicon sequencing were used to study the changes in the ARGs and bacterial community structure of the collected samples. The results show that the inclusion of florfenicol resulted in an increased abundance of the floR and optrA genes. Adding 100 mg L−1 florfenicol to the water increased the abundance of optrA gene copies with the maximum on the Day 7, and increased the abundance of floR gene copies with the maximum on Day 30. Adding 100 mg L−1 florfenicol to the sediment increased the abundance of floR and optrA genes by one order of magnitude on Day 60. Meanwhile, the average number of operational taxonomic units (OTUs) in the water samples was 257, and the average number of OTUs in sediment samples was 823. The bacterial community diversity and richness in sediments were higher than those in water. The difference between the maximal and minimal values of the Shannon diversity index in the water and sediment samples was 4.36 and 1.95, respectively. The effect of florfenicol on the bacterial community structure in water was much higher than that in sediment. At 30 days, the diversity index and richness index of the florfenicol treatment groups with 1 and 10 mg L−1 concentrations began to increase; at 60 days, the diversity and richness indices of the 100 mg L−1 florfenicol treatment group began to increase. The samples at the same sampling time in the sediments clustered closer together. The results of this study provide a scientific basis for guiding the rational use of florfenicol in aquaculture, maintaining a healthy and stable microecological environment in aquaculture, and provide theoretical data for environmental ecological risk assessment and safety management caused by microbial resistance under the abuse of florfenicol. [ABSTRACT FROM AUTHOR]

Threshold pressure gradient (TPG) is a key parameter determining the pore-scale fluid dynamics. In tight gas reservoirs, both gas and water exist in the porous rock, and the existing water can be divided into irreducible and movable water. However, how movable water saturation will influence TPG has not yet been investigated. Therefore herein, nuclear magnetic resonance (NMR) and high-pressure mercury intrusion (HPMI) experiments were performed to determine pore-scale water distribution, movable water saturation, and pore throat distribution in the core plugs. Subsequently, the air bubble method was used to measure TPG as a function of movable water saturation and permeability inside tight gas core plugs, finding that TPG increased from 0.01 MPa/m to 0.25 MPa/m with the movable saturation increased from 2% to 35%. Finally, a semi-empirical model was derived to describe the correlation between TPG, movable water saturation, and permeability, which performed better than previous models in the literature. These insights will advance the fundamental understanding of TPG in tight gas reservoirs and provide useful guidance on tight gas reservoirs development. [ABSTRACT FROM AUTHOR]

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment. [ABSTRACT FROM AUTHOR]

A new S/D trimming process was proposed to significantly reduce the parasitic RC of gate-all-around (GAA) nanosheet transistors (NS-FETs) while retaining the channel stress from epitaxy S/D stressors at most. With optimized S/D trimming, the 7-stage ring oscillator (RO) gained up to 27.8% improvement of delay with the same power consumption, for a 3-layer stacked GAA NS-FETs. Furthermore, the proposed S/D trimming technology could enable more than 4-layer vertical stacking of nanosheets for GAA technology extension beyond 3 nm CMOS technology. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×. [ABSTRACT FROM AUTHOR]

Epoxy resin, CFRP (Carbon Fiber Reinforced Polymer) sheet, and concrete flexural specimens are selected to study the durability of carbon fiber strengthened cementitious materials in a cold region. Two exposure environments, chloride immersion and salt-freeze coupling, are set up, and the mechanical deterioration is discussed utilizing a microscopic observation mechanical test and finite element analysis. The damage to the epoxy resin, CFRP sheet, and concrete exerts a more severe performance degradation in the salt-freeze coupling environment when compared with the chlorine salt immersion environment. The freeze–thaw action destroys the bonding surface of CFRP and concrete based on the microscope observation. The flexural strength of the specimens strengthened with CFRP is 3.6 times higher than that of the specimens without CFRP, while the degradation rate is only 50%. These observations show that the strengthened CFRP effectively improves the cementitious material's flexural performance in the cold region. The finite element model of epoxy and CFRP subjected to chloride immersion and salt-freeze coupling environment is established. The degradation formula of bond performance between CFRP and concrete is proposed. In addition, the flexural mechanical numerical model is established with and without CFRP strengthened concrete, respectively. Research results provide a technical reference for applying CFRP reinforced cementitious materials in a cold region. [ABSTRACT FROM AUTHOR]

As the development of the polar region continues to increase, the fatigue properties of structures at low temperature are increasingly receiving researcher attention. This study aimed to investigate the fatigue properties of T-welded and cruciform welded joints at −60 °C. Logarithmic S–N curves based on the hot-spot stress of the T-welded and cruciform welded joints at 50% and 95% confidence levels were obtained at −60 °C. The test results showed that the fatigue properties of T-welded joints were almost 2–7% better than those of the cruciform welded joints at −60 °C. Factors that affected the fatigue properties of welded joints, such as the stress concentration factor, microstructure, Vickers hardness profiles, and fractography, were also studied and the test results showed that the fatigue properties of cruciform welded joints at −60 °C were 57.215% better than at room temperature. [ABSTRACT FROM AUTHOR]

Featured Application: Evaluate the connectivity of wells near the fault or connected by hydraulic fractures. Pressure communication between adjacent wells is frequently encountered in multi-stage hydraulic fractured shale gas reservoirs. An interference test is one of the most popular methods for testing the connectivity of a reservoir. Currently, there is no practical analysis model of an interference test for wells connected by large fractures. A one-dimensional equation of flow in porous media is established, and an analytical solution under the constant production rate is obtained using a similarity transformation. Based on this solution, the extremum equation of the interference test for wells connected by a large fracture is derived. The type-curve of pressure and the pressure derivative of an interference test of wells connected by a large fracture are plotted, and verified against interference test data. The extremum equation of wells connected by a large fracture differs from that for homogeneous reservoirs by a factor 2. Considering the difference of the flowing distance, it can be concluded that the pressure conductivity coefficient computed by the extremum equation of homogeneous reservoirs is accurate in the order of magnitude. On the double logarithmic type-curve, as time increases, the curves of pressure and the pressure derivative tend to be parallel straight lines with a slope of 0.5. When the crossflow of the reservoir matrix to the large fracture cannot be ignored, the slope of the parallel straight lines is 0.25. They are different from the type-curves of homogeneous and double porosity reservoirs. Therefore, the pressure derivative curve is proposed to diagnose the connection form of wells. [ABSTRACT FROM AUTHOR]

Recycle Aggregate Concrete (RAC) is a common "green" product used for a variety of purposes, although the durability and strength of the material still need more research to adequately determine influences on these parameters. The failure pattern, punching bearing capacity, and deflection variation of wallboard of both ordinary concrete and RAC with various rebar reinforcement diameters and spacing were analyzed. Based on experimental data, the ABAQUS finite element analysis of the thin wallboard was performed, and the calculated results are in good agreement with the experimental results. The results show that the failure characteristics of ordinary concrete wallboards and RAC wallboards are similar, but the brittleness of the RAC specimens is more significant. Under the same reinforcement ratio, concrete material, whether new or recycled, had little effect on the punching bearing capacity of the specimens. In the case of a low reinforcement ratio, increasing the reinforcement ratio (decreasing the spacing of steel bars) can effectively improve the bearing capacity of the wallboard. Under the same reinforcement ratio, the wallboard with larger diameter and larger spacing has a higher bearing capacity and less deformation after failure. [ABSTRACT FROM AUTHOR]

Boerhavia diffusa L. Nyctanginaceae (B. diffusa) is a medicinal herb commonly considered as a weed. The exploration of phytochemicals in different parts of B. diffusa with different solvents will create awareness, along with the suitable solvent and method for extraction of pharmaceutical compounds. Hence, the present study focuses on phytochemical analysis of B. diffusa leaves, stems, and roots in various solvents with hot and cold extraction. The decoctions performed well in most of the qualitative and quantitative tests, along with the DPPH assay. The aqueous extract showed a good result in the FRAP assay and ABTS assay. In the antimicrobial test, the B. diffusa root ethanol extract inhibited the growth of Pseudomonas aeruginosa and Staphylococcus aureus with zones of inhibition of about 8 mm and 20 mm at 200 µg concentration, respectively. Using a molecular docking approach, the top four ranked molecules from the crude extract of B. diffusa profiled from GC–MS spectroscopy in terms of growth inhibition of the pathogenic bacterium P. aeruginosa were selected; among them, 2-(1,2 dihydroxyethyl)-5-[[2,5,7,8-tetramethyl-2-(4,8,12-trimethyltridecyl)-3,4-dihydrochromen-6-yl]oxy]oxolane-3,4-diol exhibited the minimum binding score, revealing high affinity in complex. B. diffusa is highly nutritious, and the maceration and decoction extracts were similar except for the chloroform extract that was found to be weak. [ABSTRACT FROM AUTHOR]

China's transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and economic connection within and between regions of different scales. The existing research only carries out the characteristic analysis of urban network spatial connection and pattern from a single perspective such as "flow space" theory, spatial interaction model and accessibility method, and fails to accurately describe the complex socio-economic relations between regions. Based on the big data of railway passenger flow, this study selected weighted average travel time, railway network density, and the economic connection model to express the urban network spatial connection and structure of China in 2016 from the perspectives of time, space, and interaction. In 2016, the accessibility, connectivity, and total urban external economic connection of the railway network showed a trend of declining from the east to the west. The top 50 cities ranked by interurban economic connection were all located in the central and eastern regions and showed "diamond shape" distribution characteristics. The four diamond-shaped pairs were Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengyu urban agglomerations. This shape was basically in line with the T-shaped space that has existed for a long time in China's regional development. The accessibility, connectivity, and total external economic connection of national-level urban agglomerations were greater than those of regional-level urban agglomerations, and far greater than those of local-level urban agglomerations. The results showed that there was a mismatch between the layout of the railway network and the population. It will still be necessary to focus on strengthening the construction of transportation infrastructure in urban agglomerations and densely populated areas in the future. This study enriches the "flow space" theory, more fully describes urban network spatial connection and structure in China by considering the three perspectives of time, space, and interaction, and can provides reasonable suggestions for the development of national comprehensive three-dimensional transportation network planning, regional spatial structure optimization, and sustainable development. [ABSTRACT FROM AUTHOR]

Poultry breeding is one of the most significant components of agriculture and an essential link of material exchange between humans and nature. Moreover, poultry breeding technology has a considerable impact on the life quality of human beings, and could even influence the survival of human beings. As one of the most popular poultry, broiler has a good economic benefit due to its excellent taste and fast growing cycle. This paper aims to improve the efficiency of raising broilers by understanding the impact of ammonia concentration distribution within a smart broiler breeding chamber, and the rationality of the system's design. More specifically, we used computational fluid dynamics (CFD) technology to simulate the process of ammonia production and identify the characteristics of ammonia concentration. Based on the simulation results, the structure of the broiler chamber was reformed, and the ammonia uniformity was significantly improved after the structural modification of the broiler chamber and the ammonia concentration in the chamber had remained extremely low. In general, this study provides a reference for structural optimization of the design of broiler chambers and the environmental regulation of ammonia. [ABSTRACT FROM AUTHOR]

Due to a proportionally increasing population and food demands, the food industry has come up with wide innovations, opportunities, and possibilities to manufacture meat under in vitro conditions. The amalgamation of cell culture and tissue engineering has been the base idea for the development of the synthetic meat, and this has been proposed to be a pivotal study for a futuristic muscle development program in the medical field. With improved microbial and chemical advancements, in vitro meat matched the conventional meat and is proposed to be eco-friendly, healthy, nutrient rich, and ethical. Despite the success, there are several challenges associated with the utilization of materials in synthetic meat manufacture, which demands regulatory and safety assessment systems to manage the risks associated with the production of cultured meat. The role of 3D bioprinting meat analogues enables a better nutritional profile and sensorial values. The integration of nanosensors in the bioprocess of culture meat eased the quality assessment throughout the food supply chain and management. Multidisciplinary approaches such as mathematical modelling, computer fluid dynamics, and biophotonics coupled with tissue engineering will be promising aspects to envisage the future prospective of this technology and make it available to the public at economically feasible rates. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due to the limited budget of the logical resources and power consumption in these systems, an embedded device is a good choice to implement the CNN-based methods. However, it is still a challenge to strike a balance between performance and power consumption. In this paper, we propose an efficient hardware-implementation method for optical remote sensing object detection. Firstly, we optimize the CNN-based model for hardware implementation, which establishes a foundation for efficiently mapping the network on a field-programmable gate array (FPGA). In addition, we propose a hardware architecture for the CNN-based remote sensing object detection model. In this architecture, a general processing engine (PE) is proposed to implement multiple types of convolutions in the network using the uniform module. An efficient data storage and access scheme is also proposed, and it achieves low-latency calculations and a high memory bandwidth utilization rate. Finally, we deployed the improved YOLOv2 network on a Xilinx ZYNQ xc7z035 FPGA to evaluate the performance of our design. The experimental results show that the performance of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP). Under a 200 MHz working frequency, our design achieves a throughput of 111.5 giga-operations per second (GOP/s) with a 5.96 W on-chip power consumption. Comparison with the related works demonstrates that the proposed design has obvious advantages in terms of energy efficiency and that it is suitable for deployment on embedded devices. [ABSTRACT FROM AUTHOR]

As rate-limited enzyme of polyol pathway, aldose reductase (ALR2) is one of the key inhibitory targets for alleviating diabetic complications. To reduce the toxic side effects of the inhibitors and to decrease the level of oxidative stress, the inhibitory selectivity towards ALR2 against detoxicating aldehyde reductase (ALR1) and antioxidant activity are included in the design of multifunctional ALR2 inhibitors. Hydroxypyridinone derivatives were designed, synthesized and evaluated their inhibitory behavior and antioxidant activity. Notably, {2-[2-(3,4-dihydroxy-phenyl)-vinyl]-5-hydroxy-4-oxo-4H-pyridin-1-yl}-acetic acid (7l) was the most potent, with IC50 values of 0.789 μM. Moreover, 7l showed excellent selectivity towards ALR2 with selectivity index 25.23, which was much higher than that of eparlestat (17.37), the positive control. More significantly, 7l performed powerful antioxidative action. At a concentration of 1 μM, phenolic compounds 7l scavenged DPPH radical with an inhibitory rate of 41.48%, which was much higher than that of the well-known antioxidant Trolox, at 11.89%. Besides, 7l remarkably suppressed lipid peroxidation with a rate of 88.76% at a concentration of 100 μM. The binding mode derived from molecular docking proved that the derivatives were tightly bound to the activate site, suggesting strongly inhibitory action of derivatives against ALR2. Therefore, these results provided an achievement of multifunctional ALR2 inhibitors capable with potency for both selective ALR2 inhibition and as antioxidants. [ABSTRACT FROM AUTHOR]

Colorectal carcinoma is one of the utmost diagnosed cancer with a steep increase in mortality rate. The incidence has been increasing in developing countries like India due to a westernization life style. Flavonoids have been explored widely for its various pharmacological activity including antitumor activity. Orientin, an analogue of luteolin (citrus flavonoid) isolated from rooibos and tulsi leaves is also expected to deliver significant antitumor activity similar to that of luteolin. The present study anticipates exploring the antitumor activity of orientin against colorectal carcinoma cells (HT29). Orientin exhibited remarkable cytotoxicity and antiproliferative activity against HT29 cells, which is clearly evident from tetrazolium based cytotoxicity and lactate dehydrogenase release assays. Orientin induce G0/G1 cell cycle arrest and regulates cyclin and cyclin-dependent protein kinases in order to prevent the entry of the cell cycle to the S phase. Annexin V-FITC (V-Fluorescein Isothiocyanate) dual staining reveals the apoptotic induction ability of orientin. The Bcl-2 family proteins along with the inhibitor of apoptotic proteins were regulated and the tumor suppressor p-53 expression have been decreased. In conclusion, our results proposed that orientin could be a potent chemotherapeutic agent against colorectal cancer after ascertaining their molecular mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: Heat stress, one of the major problems in tropical and subtropical regions, adversely affects poultry production. This study was designed to evaluate the effects of dietary betaine on growth performance, digestive function, carcass traits, and meat quality in indigenous yellow-feathered broilers subjected to long-term heat stress. The results demonstrated that long-term heat exposure reduced the growth performance, digestive function, and carcass yield, and dietary betaine supplementation partially alleviated the adverse effects of heat stress on these parameters. These findings are useful for development of anti-heat stress feed additives in indigenous yellow-feathered broilers. Heat stress has a profound effect on poultry health and productivity. The present study evaluated whether feeding betaine could ameliorate long-term heat stress-induced impairment of productive performance in indigenous yellow-feathered broilers. A total of 240 five-week-old male broilers were randomly allocated to five treatments with six replicates of eight broilers each. The five treatments included a thermoneutral zone control group (TN, fed basal diet), a heat stress control group (HS, fed basal diet), and an HS control group supplemented 500, 1000, 2000 mg/kg betaine, respectively. The TN group was raised at 26 ± 1 °C during the whole study, HS groups exposed to 32 ± 1 °C for 8 h/day from 9:00 am to 17:00 pm. The results showed that heat stress decreased the body weight gain (BWG) and feed intake of broilers during 1–5, 6–10, and 1–10 weeks (p < 0.05). Dietary betaine tended to improve the BWG and feed intake of broilers under 5 weeks of heat stress (linear, p < 0.10), and betaine supplementation linearly increased the BWG and feed intake during 6–10 and 1–10 weeks (p < 0.05). Additionally, nitrogen retention was reduced by 5 weeks and 10 weeks of heat stress (p < 0.05), whereas dietary betaine could improve nitrogen retention in heat stressed broilers after both 5 and 10 weeks of heat stress (linear, p < 0.05). Moreover, this study observed that the trypsin activity of jejunum was decreased by 5 weeks of heat stress (p < 0.05), whereas betaine supplementation had quadratic effects on trypsin activity of jejunum in heat stressed broilers (p < 0.05). Furthermore, 10 weeks of heat stress induced a reduction of villus height of the duodenum and jejunum (p < 0.05), and decreased the villus height to crypt depth ratio of the jejunum (p < 0.05). Supplementation with betaine ameliorated the adverse effects of heat stress on these parameters (p < 0.05). Compared with the TN group, 10 weeks of heat stress reduced carcass and breast yield (p < 0.05) and betaine supplementation improved carcass and breast yield of heat stressed broilers (linear, p < 0.05). In conclusion, dietary supplementation of betaine could reduce the detrimental effects of long-term heat stress on growth performance, digestive function, and carcass traits in indigenous yellow-feathered broilers. [ABSTRACT FROM AUTHOR]

Recently, extensive convolutional neural network (CNN)-based methods have been used in remote sensing applications, such as object detection and classification, and have achieved significant improvements in performance. Furthermore, there are a lot of hardware implementation demands for remote sensing real-time processing applications. However, the operation and storage processes in floating-point models hinder the deployment of networks in hardware implements with limited resource and power budgets, such as field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). To solve this problem, this paper focuses on optimizing the hardware design of CNN with low bit-width integers by quantization. First, a symmetric quantization scheme-based hybrid-type inference method was proposed, which uses the low bit-width integer to replace floating-point precision. Then, a training approach for the quantized network is introduced to reduce accuracy degradation. Finally, a processing engine (PE) with a low bit-width is proposed to optimize the hardware design of FPGA for remote sensing image classification. Besides, a fused-layer PE is also presented for state-of-the-art CNNs equipped with Batch-Normalization and LeakyRelu. The experiments performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset using a graphics processing unit (GPU) demonstrate that the accuracy of 8-bit quantized model drops by about 1%, which is an acceptable accuracy loss. The accuracy result tested on FPGA is consistent with that of GPU. As for the resource consumptions of FPGA, the Look Up Table (LUT), Flip-flop (FF), Digital Signal Processor (DSP), and Block Random Access Memory (BRAM) are reduced by 46.21%, 43.84%, 45%, and 51%, respectively, compared with that of floating-point implementation. [ABSTRACT FROM AUTHOR]

In order to study the durability behavior of CFRP (carbon fiber reinforced polymer) reinforced concrete, three category specimens (plain, partially reinforced, and fully reinforced) were selected to investigate its performance variation concerning chlorine salt and salt-freeze coupled environment, which included the microscopic examination, the distribution of chloride ion concentration, and the compressive properties. By observing the microscopic of the specimens, the surface and cross-section corrosion deterioration was examined with increasing exposure time, and the physical behavior of CFRP and core concrete were discussed. The chloride ion diffusion test exerted that the chloride ion concentration in plain specimens is at least 200 times higher than that of fully reinforced specimens. Therefore, the effectiveness of CFRP reinforcement will be proved to effectively hinder the penetration of chloride ions into the core section. The formula of the time-dependent effect of concrete diffusivity with salt-freeze coupling effect was presented and its accuracy verified. A time-varying finite element model of chloride ion distribution was established by using ABAQUS software. It can be seen from the axial compression test that the strength loss rate of three categories of specimens was varied when subjected to the corrosion environment. Therefore, it is proved that CFRP reinforcement can effectively reduce the deterioration of the specimen's mechanical properties caused by the exposure environment. The research results can provide technical reference for applying the CFRP strengthened concrete in a severe salt-freeze environment.

In this study, the cyclic behaviors of low rise concrete shear walls using recycled coarse or fine aggregates were investigated. Eight low rise Recycled Aggregates Concrete (RAC) shear wall specimens were designed and tested under a cyclic loading. The following parameters were varied: replacement percentages of recycled coarse or fine aggregates, reinforcement ratio, axial force ratio and X-shaped rebars brace. The failure characteristics, hysteretic behavior, strength and deformation capacity, strain characteristics and stiffness were studied. Test results showed that the using of the Recycled Coarse Aggregates (RCA) and its replacement ratio had almost no influence on the mechanical behavior of the shear wall; however, the using of Recycled Fine Aggregates (RFA) had a certain influence on the ductility of the shear wall. When the reinforcement ratio increased, the strength and ductility also increased. By increasing the axial force ratio, the strength increased but the ductility decreased significantly. The encased brace had a significant effect on enhancing the RAC shear walls. The experimental maximum strengths were evaluated with existing design codes, it was indicated that the strength evaluation of the low rise RAC shear walls can follow the existing design codes of the conventional concrete shear walls., Competing Interests: The authors declare no conflict of interest.

Recycled aggregate concrete (RAC) is an environmentally friendly building material. This paper investigates the mechanical behavior of recycled aggregate concrete filled steel tube (RACFST) columns exposed to fire. Two groups of 12 columns were designed and tested, under axial compression, before and after fire, to evaluate the degradation of bearing capacity due to fire exposure. Six specimens were subjected to axial compression tests at room temperature and the other six specimens were subjected to axial compression tests after a fire exposure. The main parameters of the specimens include the wall thickness of the steel tube (steel content) and the type of concrete materials. Several parameters as obtained from the experimental results were compared and analyzed, including the load-bearing capacity, deformation capacity, and failure characteristics of the specimens. Meanwhile, rate of loss of bearing capacity of specimens exposed to fire were calculated based on the standards EC4 and CECS28:90. The results show that concrete material has a large influence on the rate of loss of bearing capacity in the case of a relatively lower steel ratio. While steel content has little effect on the rate of loss of bearing capacity of concrete-filled steel tube (CFST) columns after fire, it has a relatively large influence on the loss rate of bearing capacity of the RACFST columns. The loss of bearing capacity of the specimens from the experiment is more serious than that from the calculation. As the calculated values are less conservative, particular attention should be given to the application of recycled aggregate concrete in actual structures.

The seismic performance of recycled aggregate concrete (RAC) composite shear walls with different expandable polystyrene (EPS) configurations was investigated. Six concrete shear walls were designed and tested under cyclic loading to evaluate the effect of fine RAC in designing earthquake-resistant structures. Three of the six specimens were used to construct mid-rise walls with a shear-span ratio of 1.5, and the other three specimens were used to construct low-rise walls with a shear-span ratio of 0.8. The mid-rise and low-rise shear walls consisted of an ordinary recycled concrete shear wall, a composite wall with fine aggregate concrete (FAC) protective layer (EPS modules as the external insulation layer), and a composite wall with sandwiched EPS modules as the insulation layer. Several parameters obtained from the experimental results were compared and analyzed, including the load-bearing capacity, stiffness, ductility, energy dissipation, and failure characteristics of the specimens. The calculation formula of load-bearing capacity was obtained by considering the effect of FAC on composite shear walls as the protective layer. The damage process of the specimen was simulated using the ABAQUS Software, and the results agreed quite well with those obtained from the experiments. The results show that the seismic resistance behavior of the EPS module composite for shear walls performed better than ordinary recycled concrete for shear walls. Shear walls with sandwiched EPS modules had a better seismic performance than those with EPS modules lying outside. Although the FAC protective layer slightly improved the seismic performance of the structure, it undoubtedly slowed down the speed of crack formation and the stiffness degradation of the walls.

This research aims to deepen the understanding of the interaction mechanisms between microwave and matter in a metal-ceramic system based on in situ synchrotron radiation computed tomography. A special internal "core-shell" microstructure was discovered for the first time and used as an indicator for the interaction mechanisms between microwave and matter. Firstly, it was proved that the microwave magnetic field acted on metal particles by way of inducing an eddy current in the surface of the metal particles, which led to the formation of a "core-shell" microstructure in the metal particles. On this basis, it was proposed that the ceramic particles could change the microwave field and open a way for the microwave, thereby leading to selective heating in the region around the ceramic particles, which was verified by the fact that all the "core-shell" microstructure was located around ceramic particles. Furthermore, it was indicated that the ceramic particles would gather the microwaves, and might lead to local heating in the metal-ceramic contact region. The focusing of the microwave was proved by the quantitative analysis of the evolution rate of the "core-shell" microstructure in a different region. This study will help to reveal the microwave-matter interaction mechanisms during microwave sintering.

Increasing evidence suggests that caveolin-1 and large conductance Ca²⁺-activated potassium (BKCa) channels are implicated in the carcinogenesis processes, including cell proliferation and invasion. These two proteins have been proven to interact with each other in vascular endothelial and smooth muscle cells and modulate vascular contractility. In this study, we investigated the probable interaction between caveolin-1 and BKCa in MCF-7 breast cancer cells. We identified that caveolin-1 and BKCa were co-localized and could be reciprocally co-immunoprecipitated in human breast cancer MCF-7 cells. siRNA mediated caveolin-1 knockdown resulted in activation and increased surface expression of BKCa channel, and subsequently promoted the proliferation and invasiveness of breast cancer cells. These effects were attenuated in the presence of BKCa-siRNA. Conversely, up-regulated caveolin-1 suppressed function and surface expression of BKCa channel and exerted negative effects on breast cancer cell proliferation and invasion. Similarly, these opposing effects were abrogated by BKCa up-regulation. Collectively, our findings suggest that BKCa is a critical target for suppression by caveolin-1 in suppressing proliferation and invasion of breast cancer cells. The functional complex of caveolin-1 and BKCa in the membrane microdomain may be served as a potential therapeutic target in breast cancer.

Secret key leakage in wireless sensor networks (WSNs) is a high security risk especially when sensor nodes are deployed in hostile environment and physically accessible to attackers. With nowadays semi/fully-invasive attack techniques attackers can directly derive the cryptographic key from non-volatile memory (NVM) storage. Physically Unclonable Function (PUF) is a promising technology to resist node capture attacks, and it also provides a low cost and tamper-resistant key provisioning solution. In this paper, we designed a PUF based on double-data-rate SDRAM Type 3 (DDR3) memory by exploring its memory decay characteristics. We also described a prototype of 128-bit key generation based on DDR3 PUF with integrated fuzzy extractor. Due to the wide adoption of DDR3 memory in WSN, our proposed DDR3 PUF technology with high security levels and no required hardware changes is suitable for a wide range of WSN applications.