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Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address these issues, this paper proposes a framework for predicting CO2 emissions, called CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the five key hyperparameters of the AttBiGRU. We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance. [ABSTRACT FROM AUTHOR]

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO. [ABSTRACT FROM AUTHOR]

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO's exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO. [ABSTRACT FROM AUTHOR]

The dry–hot–windy climate frequently occurs during the grain-filling stage of winter wheat on the North China Plain (NCP) and thus negatively influences wheat yield. Sprinkler irrigation can improve field temperature and humidity and can be used to mitigate dry–hot–windy climate disasters. A two-season field experiment was carried out on the NCP to test how sprinkler irrigation influences the microclimate, canopy temperature and photosynthetic traits, as well as the grain-filling process and final grain yield, when spraying 1.5–2 mm of water on dry–hot–windy days. Field experiments revealed that, compared with the no-spraying treatment, spraying with 2 mm of water each time caused the air and canopy temperatures to decrease by 2.3–7.6 °C and 4.3–9.9 °C, respectively, during and just after spraying stopped, and the temperatures returned to their previous levels approximately one hour after spraying. The air humidity increased by up to 10% during and after spraying. The photosynthesis and transpiration rates and the stomatal conductivity after spraying increased by 34–235%, 15–55% and 24–79%, respectively. The linear relationships between photosynthesis rates and transpiration rates with respect to stomatal conductivity suggest that increases in both photosynthesis and transpiration rates are the main contributors to the increase in stomatal conductivity, which is due mainly to the improved canopy temperature and humidity conditions caused by spraying practices. The grain-filling process was improved by spraying, which ultimately increased the unit grain mass by approximately 5%. One spraying event on a dry–hot–windy day influenced the field microclimate and canopy photosynthetic traits for 90 min (30 min in spraying time + 60 min after spraying). When the intensity of the dry–hot–windy climate is strong, two spraying events can be applied. Spraying 2–2.5 mm of water each time was sufficient when the leaf area index was 4–5 during the grain-filling stage of winter wheat. Spray events can have a slight effect on grain yield when a dry–hot–windy climate occurs within the last five days before harvest. [ABSTRACT FROM AUTHOR]

Considering the high demand for air quality, the development of biomass-based air filtration membranes with high air filtration efficiency and good stability is an urgent task. In this work, polyvinyl alcohol (PVA), gelatin (GA), and cellulose nanocrystals (CNC) were mixed and prepared into a membrane through an electrospinning method for air filtration. After a hydrophobic modification, the modified PVA/GA/CNC composite membrane showed excellent filtration efficiency for PM2.5 (97.65%) through the internal three-dimensional structure barrier and the electrostatic capture effect of the CNC with a negative charge, as well as a low-pressure drop (only 50 Pa). In addition, the modified PVA/GA/CNC composite membrane had good mechanical properties (maximum tensile fracture rate of 78.3%) and high stability (air filtration efficiency of above 90% after five wash-filter cycles and a high-temperature treatment at 200 °C). It is worth noting that the whole preparation process is completed without organic solvents, putting forward a new strategy for the construction of green air filtration membranes. [ABSTRACT FROM AUTHOR]

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pulmonary accelerated rheumatoid nodules (ARN) represent a rare occurrence within the context of rheumatoid arthritis (RA), with conventional treatment typically involving corticosteroids. In this report, we present a unique case of pulmonary ARN managed with baricitinib, a Janus kinase inhibitor. The patient, a 46-year-old woman diagnosed with RA, initially displayed no evident pulmonary nodules upon pulmonary imaging. Her treatment regimen encompassed corticosteroids, methotrexate, and leflunomide. Nevertheless, a chest computed tomography (CT) scan conducted after a year unveiled the presence of multiple bilateral pulmonary nodules. A thoracoscopic biopsy of these nodules confirmed the presence of rheumatoid nodules. Treatment with baricitinib, a Janus kinase inhibitor or synthetic disease-modifying antirheumatic drug (DMARD), effectively reduced the size of the nodules. Our review of 45 articles on ARN published since 1986 found that nine of them reported 13 cases of pulmonary ARN. These nodules may be caused by certain synthetic and biological DMARDs and often present with respiratory symptoms. CT scans typically reveal multiple solid nodules or ground-glass opacities, some of which may have cavities. Treatment customarily involves discontinuing the suspected drugs and administering corticosteroids. This case suggests that Janus kinase inhibitors may be an effective treatment option for ARN. [ABSTRACT FROM AUTHOR]

From the theoretical prognostication and experimental validation, the interface electrostatic field holds greater significance in ORR kinetics compared to the electronic properties during alloy engineering. [Display omitted] • The nature of alloy engineering for ORR kinetics modulation has been elucidated. • The interface property holds greater significance than the intermediate adsorption. • The theoretically prognosticated polarization curves match well with experiment outcomes. • The ORR kinetic mechanism of Pt 3 M is independent of potential. • The RDS occupies the almost control weight of the ORR kinetics. Alloy engineering has been utilized as a potent strategy to modulate the oxygen reduction reaction （ORR） activity. However, the regulatory mechanism underpinning the ORR kinetics by means of alloy engineering is still shrouded in ambiguity. This work places emphasis on the kinetics of the ORR concerning Pt 3 M (M = Cr, Co, Cu, Pd, Sn, and Ir) catalysts, and integrates theoretical prognostication and experimental validation to illuminate the fundamental principles of alloy engineering. The ORR kinetic activity, as prognosticated by theory, shows significant agreement with experimental results, provided that the rate-determining step (RDS) accounts for a dominant role in the potential-independent kinetic mechanism. In essence, alloy engineering manipulates electronic properties through electron transfer to modulate intermediate adsorption and adjusts the interface electric field (E field) to regulate hydrogen atom transport, ultimately influencing kinetics. The E field holds greater significance in ORR kinetics compared to the intermediate adsorption (E a d s O), the corresponding degrees of correlation with free energy barriers (E a) of RDS are −0.89, and 0.75, respectively. This work highlights the nature of alloy engineering for ORR kinetics modulation and assists in the design of efficient catalysts. [ABSTRACT FROM AUTHOR]

Climate change and water resource shortages have become important problems limiting winter wheat production in the North China Plain (NCP). Understanding the physiological characteristics of different wheat varieties and their relationship with crop growth and yield is of great importance for addressing climate change through a scientific approach, adopting reliable wheat varieties, and ensuring food production. This study was conducted throughout three winter wheat seasons from 2018 to 2021. The crop growth, physiological indicators, crop yields, and water productivity (WP) of 12 wheat varieties widely cultivated in the NCP were measured to investigate the relationships between physiological characteristics and crop yield. The results showed that among the three wheat seasons, the maximum plant height of each wheat variety was relatively stable, while the changes in maximum plant density (PDm), maximum leaf area index (LAIm), and maximum dry matter (DMm) were highly variable. The gas exchange parameters and fluorescence parameters of wheat flag leaves varied with growth stage, and certain varieties were sensitive to water stress. The wheat grain yield, seasonal crop evapotranspiration (ETa), and WP of the 12 varieties were similar in the 2019–2020 and 2020–2011 seasons and were approximately 25%, 7%, and 19% higher than those in the 2018–2019 season, respectively, in which there were consecutive cool events in the winter and spring. Generally, the grain yields were positively correlated with PDm, LAIm, DMm, ETa, the harvest index, and WP, at a significance level of 0.01. The high-yield wheat varieties had higher photosynthetic rates in the middle and late grain-filling stages. Based on these relationships, high-yield wheat varieties may be selected to address climate change in the NCP as well as other regions in the world. Considering the variations in yield and WP in the three different climatic conditions, we recommend Yingbo700, Lunxuan103, Shimai26, Shinong086, Han6172, and Hanong1412 as high-yield and drought-resistant wheat varieties to be used in the NCP. [ABSTRACT FROM AUTHOR]

Primary cylindroma of the breast is extremely rare. To the best of our knowledge, there have been only 18 tumors reported. All of them were benign and had no mitosis. However, the rarity of this entity made the correct diagnosis preoperatively very difficult. Here we present a unique cylindroma with focal brisk mitosis and largest diameter of 5cm in an 80-year woman, she was misdiagnosed as adenoid cystic carcinoma in biopsy and then underwent a radical breast resection. We reviewed the related literature and emphasized differential diagnosis. [ABSTRACT FROM AUTHOR]

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. [ABSTRACT FROM AUTHOR]

Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when the object is not salient in the depth maps. Furthermore, most existing (UNet-based) methods cannot fully exploit high-level abstract features to guide low-level features in a coarse-to-fine fashion. In this paper, we propose a compensated attention feature fusion and hierarchical multiplication decoder network (CAF-HMNet) for RGB-D SOD. Specifically, we first propose a compensated attention feature fusion module to fuse multi-modal features based on the complementarity between depth and RGB features. Then, we propose a hierarchical multiplication decoder to refine the multi-level features from top down. Additionally, a contour-aware module is applied to enhance object contour. Experimental results show that our model achieves satisfactory performance on five challenging SOD datasets, including NJU2K, NLPR, STERE, DES, and SIP, which verifies the effectiveness of the proposed CAF-HMNet. [ABSTRACT FROM AUTHOR]

The theory of congruences on semigroups is an important part in the theory of semigroups. The aim of this paper is to study (2,1)-congruences on a glrac semigroup. It is proved that the (2,1)-congruences on a glrac semigroup become a complete sublattice of its lattice of congruences. Especially, the structures of left restriction semigroup (2,1)-congruences and the projection-separating (2,1)-congruences on a glrac semigroup are established. Also, we demonstrate that they are both complete sublattice of (2,1)-congruences and consider their relations with respect to complete lattice homomorphism. [ABSTRACT FROM AUTHOR]

The RRAM-based accelerators have become very popular candidates for neural network acceleration due to they perform matrix-vector multiplication in-memory with high storage density and low latency. Many related works have used fixed-precision quantization to achieve the model compression and enhance the tolerance of process variation, but these methods still suffer a large accuracy degradation and poor robustness to nonideal effects. In this work, we propose a crossbar-aware mixed-precision quantization scheme, which enables to search for the optimal precision of each part of the network as a way to improve the accuracy and robustness to noise. First, we introduce a group quantization strategy that can flexibly adjust the group size dynamically according to the crossbar size. Then, we propose a detailed mixed-precision search flow to search for the optimal precision set of the network. Finally, we give a noise injection adaption training method to enhance the tolerance of noise. Experimental results show that our proposed method can improve inference accuracy by at least 2.04% compared to the fixed-precision quantization under the same resource cost. The searched architecture with the highest accuracy most accurate (MA) could achieve an accuracy of 92.39% and a resource saving of 93.30% compared to the full precision model. The searched architecture with the biggest resource savings most efficient (ME) could achieve an accuracy of 91.11% and a resource saving of 95.57% compared to the full precision model. And, the average precision of the ME mixed-precision architecture is only 1.4 bits. Besides, the results show the mixed-precision network with noise adaption training is more robust to noise than the fixed-precision network with noise adaption training. [ABSTRACT FROM AUTHOR]

Climate change and water shortage have brought great challenges to agricultural production in the North China Plain, and it's crucial to find the suitable method to address these challenges. Climate model projections were used to drive the Agro-Hydrological & chemical and Crop systems simulator (AHC) that considering the impact of irrigation on field microclimate. Yield, evapotranspiration (ET), water productivity (WP), and the impact on groundwater level of 6 wheat varieties under 6 sprinkler irrigation scheduling (IS1: no irrigation; IS6: full irrigation without water deficit; IS2–5: deficit irrigation with 0.2, 0.4, 0.6, and 0.8 times water of IS6 after wheat greening) were estimated in Xingtai under current and two shared social economic paths scenarios (SSP2–4.5 and SSP5–8.5). The optimal combination of irrigation and variety (OC) was selected by Topsis method. The results indicated that AHC had a high accuracy in simulating the growth of six varieties (R2>0.80, NRMSE<20 %) after parameter optimization. The temperature, solar radiation, and precipitation in 2021–2100 increased by 0.1–2.8 °C, 0.7–1.7 MJ m−2 d−1, and 5.2–28.8 mm under SSP2–4.5 and 0.2–4.8 °C, 0.9–1.2 MJ m−2 d−1, and 4.7–73.1 mm under SSP5–8.5 than current climate. As the irrigation amount increased, the simulated yield and ET increased, while WP first increased and then decreased. Due to climate change, irrigation failed to improve the wheat potential yield after 2080 under SSP2–4.5 and 2060 under SSP5–8.5. The OC implied that "Shimai26" and IS5 and IS4 in the future were the best strategy. It saved water by 13–23 % with similar yield than full irrigation. Moreover, the inhibitory effect of OC on annual groundwater level drawdown was stronger than other irrigation by 0.04–0.24 m. These findings were beneficial for mitigating climate impacts on grain yields in water scarce areas. ● Shimai26 was the best wheat variety under climate change. ● 0.8 and 0.6 times full irrigation after wheat regreening were the best in future. ● Irrigation couldn't increase wheat potential yield at the end of 21st century. ● Best irrigation saved 13–23 % water with similar yield than full irrigation. ● Best irrigation had the greatest inhibitory effect on groundwater level decline. [ABSTRACT FROM AUTHOR]

The article describes the case of a 29-year-old male with a 7-year history of generalized desquamative rash who presented with an increase in systemic erythema and was diagnosed with pustular psoriasis. Topics covered include the medical history of the patient, the findings of physical and laboratory examinations which led to the diagnosis of Guillain-Barré syndrome (GBS) associated with ixekizumab, and patient outcome following intravenous immunoglobulin (IVIG) therapy.

Tomato crops are increasingly cultivated in winter in solar greenhouses to achieve high economic benefit in the North China Plain (NCP). Accurate predictions of crop transpiration (Tr) are of great significance for formulating a scientific irrigation system and increasing water productivity in this water shortage region. In this study, tomato transpiration at daily and hourly scales were estimated using Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Priestley-Taylor (PT) models, and results were compared to the measured sap flow data (SF) in three tomato growth seasons in winter from 1 November 2018 to 9 December 2020. Results showed that both PM and SW models could perfectly estimate daily tomato Tr, with a determination coefficient R2 of 0.96 and 0.94 and slopes of 0.99 and 0.98, respectively, when all three seasons' data were pooled together. The estimated daily Tr by the original PT model with a coefficient (α) of 1.26 was also linearly related to the SF with R2 of 0.92; however, the Tr was underestimated by 33%. Then α was calibrated using the data in the 2018 winter season. When the calibrated α was used in the 2019 and 2020 seasons, the estimated daily Tr showed comparable results with the PM and SW models. At hourly scales, the PM model performed best with an error of 3.0%, followed by the PT model (7.8%); the SW model underestimated Tr by 18.2%. In conclusion, all three models could be used to estimate daily Tr, and the PM and calculated PT models can be used to estimate hourly Tr. [ABSTRACT FROM AUTHOR]

Multi-spectral semantic segmentation has shown great advantages under poor illumination conditions, especially for remote scene understanding of autonomous vehicles, since the thermal image can provide complementary information for RGB image. However, methods to fuse the information from RGB image and thermal image are still under-explored. In this paper, we propose a simple but effective module, add–multiply fusion (AMFuse) for RGB and thermal information fusion, consisting of two simple math operations—addition and multiplication. The addition operation focuses on extracting cross-modal complementary features, while the multiplication operation concentrates on the cross-modal common features. Moreover, the attention module and atrous spatial pyramid pooling (ASPP) modules are also incorporated into our proposed AMFuse modules, to enhance the multi-scale context information. Finally, in the UNet-style encoder–decoder framework, the ResNet model is adopted as the encoder. As for the decoder part, the multi-scale information obtained from our proposed AMFuse modules is hierarchically merged layer-by-layer to restore the feature map resolution for semantic segmentation. The experiments of RGBT multi-spectral semantic segmentation and salient object detection demonstrate the effectiveness of our proposed AMFuse module for fusing the RGB and thermal information. [ABSTRACT FROM AUTHOR]

Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity. [ABSTRACT FROM AUTHOR]

Many unmixing methods hold the assumption that endmembers correspond to major land covers, but not true for some unmixing tasks where observed minor object signals corresponding to some special types of endmembers are relatively weak. When there exist weak signals that have low intensity potentially caused by subtle mixing abundance fractions regarding the endmembers of minor objects, the traditional unmixing techniques may fail. This article pioneers weak signal scenarios in hyperspectral unmixing using an efficient method called HyperWeak. In particular, HyperWeak involves a sparse nonnegative matrix factorization (NMF) model that contains two main parts, where the unsupervised part estimates the endmember and abundance matrices, and the supervised part ensures the minimal degradation of prior knowledge. To enhance the robustness of the HyperWeak model, this article considers a reweighted sparsity constraint to boost the sparseness of the abundance matrix. For effectively solving optimization problems, a Nesterov’s optimal gradient method (OGM) is used in this article. Experiments conducted on synthetic and real hyperspectral images indicate that HyperWeak can improve the unmixing performances of hyperspectral data in weak signal situations. [ABSTRACT FROM AUTHOR]

• An accurate admittance model for the grid-connected converter with droop control is proposed. • The frequency coupling phenomenon is induced by the asymmetry of the droop controller for the grid-connected converter. • The outer control loop will impact the frequency-coupled phenomenon, but has slight effects on positive and negative sequence admittance of VSCs. • The proportional gain of the current control loop impacts obviously the frequency coupling phenomenon. The droop control scheme is widely applied for the paralleled operation of the grid-connected converter. Yet, the frequency-domain admittance model for the grid-connected converter with droop control is still inaccurate due to the complex coupling relation of the droop control loop, the voltage control loop, and the current control loop. To address this problem, the coupling relationship of these control loops is analyzed in this paper, and then an accurate admittance model for the grid-connected converter with droop control is proposed based on the complex vector modeling method in both dq -frame and αβ -frame. The influence of different control parameters on the admittance model is analyzed further. The proposed model accurately describes the admittance characteristics of the grid-connected converter with droop control, and reveals the frequency coupling phenomenon caused by the droop control loop. The frequency response measured by frequency scan and simulation results verify the effectiveness of the established model. Finally, the impedance interactive stability analysis results show that the grid-connected converter with droop control is prone to instability in stiff grid conditions. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Fig Schematic of cation transport mechanism: driven by a concentration gradient. The schematic diagram is not to scale. During the operation of PEM fuel cells, a large amount of water is generated at the cathode, while the anode side remains relatively dry. Under the influence of a water concentration gradient, Cr3+ migrates through the PEM from the cathode catalyst layer to the anode catalyst layer. [Display omitted] • Cr3+ and its concentration significantly affect the performance of PEM fuel cells. • The charge transfer impedance and mass transfer resistance increase with the increasing degree of Cr3+ contamination. • Cr3+ contamination slows down ORR kinetics and reduces the limiting diffusion current density. • The presence of Cr3+ affects the durability and stability of the PEM fuel cell. • Cr3+ migrates from the cathode to the anode catalyst layer across the membrane during the operation of the PEM fuel cell. It is known that contamination ions stemming from the corrosive deterioration of vital subassemblies, such as bipolar plates (BPs) and sealing gaskets, exert a profound influence on the performance and durability degradation of PEM fuel cells. The impacts of Cr3+ on the catalyst layers (CLs) and single-cell performance were investigated by immersing the cathode catalyst layer (CCL) and anode catalyst layer (ACL) in a Cr3+ solution with different concentrations for contamination treatment. During the fuel cell test, polarization curves (PCs), electrochemical impedance spectroscopy (EIS), H+ transfer impedance, and cyclic voltammetry (CV) curve results were recorded. The effects of Cr3+ contamination on the kinetic for oxygen reduction reaction (ORR) and hydrogen oxidation reaction (HOR) were analyzed in a three-electrode system. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were used to explore the degradation mechanisms of Cr3+ contamination on fuel cell activity. The results indicate that the presence of Cr3+ increased the ohmic impedance from 0.200 Ω cm2 to 0.449 Ω cm2, resulting in a voltage attenuation rate as high as 55.21 %. Moreover, the presence of Cr3+ slowed the ORR reaction rate and changed the ORR reaction pathway. Cr3+ contamination caused irreversible damage to PEMFC performance and durability. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the model of higher education using distributed hardware tracking intervention of information technology. The MEC-based dynamic adaptive video stream caching technology model is proposed. The model dynamically adjusts the bit rate by referring to the broadband estimation and cache occupancy data to ensure users have a smooth experience effect. Simulation results show that the model has fewer transcoding times and generates lower latency than the traditional model, which is suitable for dual-teacher classroom scenarios and further improves the quality of the user's video viewing experience. The model uses an edge cloud collaborative architecture to migrate the rendering technology to an edge server closer to the user side, enabling real-time interaction, computation, and rendering, reducing the time of data transmission as well as computation time. According to the blended learning-based adaptive intervention model, three rounds of teaching practice are conducted to validate the effectiveness of the intervention model in terms of both student process performance and outcome performance, thereby improving learning adaptability and improving learning effect. Teachers' teaching has a significant impact on learning motivation (β = 0.311 , p < 0.01), which in turn affects learning adaptability. Teachers use scientific teaching methods to stimulate students' learning motivation, mobilize enthusiasm, and improve learning adaptability. Under the communication topology of the system as a directed graph, a multi-intelligent system dynamic model with grouping is established; i.e., the intragroup intelligence has the same dynamics but is different between groups, and all system dynamics are unknown. The proposed novel policy iterative algorithm is used to learn the optimal control protocol and achieve optimal consistency control. The effectiveness of the algorithm is demonstrated by the simulation experimental results. The simulation results show that the model has lower latency and energy consumption compared to the cloud rendering model, which is suitable for the safety education classroom scenario and solves the outstanding problems of network connection rate and cloud service latency. [ABSTRACT FROM AUTHOR]

This paper focuses on the high-resolution (HR) remote sensing images semantic segmentation task, whose goal is to predict semantic labels in a pixel-wise manner. Due to the rich complexity and heterogeneity of information in HR remote sensing images, the ability to extract spatial details (boundary information) and semantic context information dominates the performance in segmentation. In this paper, based on the frequently used fully convolutional network framework, we propose a boundary enhancing semantic context network (BES-Net) to explicitly use the boundary to enhance semantic context extraction. BES-Net mainly consists of three modules: (1) a boundary extraction module for extracting the semantic boundary information, (2) a multi-scale semantic context fusion module for fusing semantic features containing objects with multiple scales, and (3) a boundary enhancing semantic context module for explicitly enhancing the fused semantic features with the extracted boundary information to improve the intra-class semantic consistency, especially in those pixels containing boundaries. Extensive experimental evaluations and comprehensive ablation studies on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of BES-Net, yielding an overall improvement of 1.28/2.36/0.72 percent in mF1/mIoU/OA over FCN_8s when the BE and MSF modules are combined by the BES module. In particular, our BES-Net achieves a state-of-the-art performance of 91.4% OA on the ISPRS Vaihingen dataset and 92.9%/91.5% mF1/OA on the ISPRS Potsdam dataset. [ABSTRACT FROM AUTHOR]

The cultivation mode of planting offseason crops, such as cucumber, with drip irrigation has been widely used in greenhouses in North China to achieve high yields, obtain large economic benefits, and improve water use efficiency. The objectives of this study were to evaluate the effects of drip irrigation on the irrigation water amount, irrigation water use efficiency (IWUE), and cucumber yield and the corresponding economic benefits. These objectives were achieved using data from a seven-season experiment in a commercial greenhouse with cucumber plants in North China. Local furrow irrigation was used as the control. The results showed that the cucumber yields and income with drip irrigation were 4.3% and 3.1% higher than those under furrow irrigation, respectively. However, the seasonal total irrigation depth for drip irrigation was approximately 50% lower than that for furrow irrigation. The IWUE averaged 0.41 in furrow irrigation but increased to 0.79 in drip irrigation. The irrigation water productivity and the economic irrigation water productivity for drip irrigation were approximately 100% higher than those for furrow irrigation. The mean economic irrigation water productivity was 19.8 US$ m−3 for drip irrigation, which is 7 times higher than the 2019 value of 2.7 US$ m−3 in the agricultural sector in China, indicating potential high economic benefits. In conclusion, drip irrigation is recommended in greenhouses to reduce irrigation water by approximately 50% and increase cucumber yield and economic benefits by approximately 3–4% compared to traditional furrow irrigation in North China. [ABSTRACT FROM AUTHOR]

Background: Idiopathic pulmonary fibrosis (IPF) is an interstitial disease of unknown origin, characterized by tissue fibrosis, for which currently there is no effective treatment. Macrophages, the main immune cells in lung tissue, are involved in the whole process of pulmonary fibrosis. In recent years, intercellular transformation has led to wide spread concern among pulmonary fibrosis researchers. Macrophages with flexible heterogeneity and plasticity participate in different physiological processes in the body. Cell chemokine receptor 8 (CCR8) is expressed in a variety of cells and plays a significant chemotactic role in the induction of cell activation and migration. It can also promote the differentiation of macrophages under certain environmental conditions. The current study is intended to explore the role of CCR8 in macrophage to myofibroblast transdifferentiation (MMT) in IPF. Methods: We conducted experiments using CCR8-specific small interfering RNA (siRNA), an autophagy inhibitor (3-methyladenine, 3-MA), and an agonist (rapamycin) to explore the underlying mechanisms of macrophage transdifferentiation into myofibroblast cells in transforming growth factor-beta (TGF-β)-induced pulmonary fibrosis. Results: TGF-β treatment increased the CCR8 protein level in a time- and dose-dependent manner in mouse alveolar macrophages, as well as macrophage transdifferentiation-related markers, including vimentin, collagen 1, and a-SMA, and cell migration. In addition, the levels of autophagy were enhanced in macrophages treated with TGF-β. We found that 3-MA, an autophagy inhibitor, decreased the expression levels of macrophage transdifferentiation-related markers and attenuated cell migration. Furthermore, the inhibition of CCR8 via CCR8-specific siRNA reduced the levels of autophagy and macrophage transdifferentiation-related markers, and inhibited the cell migration. Enhancing autophagy with rapamycin attenuated the inhibition effect of CCR8-specific siRNA on macrophage migration and the increase in myofibroblast marker proteins. Conclusions: Our findings showed that the macrophages exposed to TGF-β had the potential to transdifferentiate into myofibroblasts and CCR8 was involved in the process. The effect of CCR8 on TGF-β-induced macrophage transdifferentiation occurs mainly through autophagy. Targeting CCR8 may be a novel therapeutic strategy for the treatment of IPF. [ABSTRACT FROM AUTHOR]

Evaluation of tourism competitiveness is useful for measuring the level of regional tourism development. It is of great importance to understand the advantages and disadvantages of tourism development correctly and formulate corresponding development strategies. To investigate tourism competitiveness, this paper established an evaluation index system, including tourism development competitiveness, tourism resource competitiveness, and tourism-support competitiveness, for 14 prefectures and cities in Xinjiang in China. The characteristics and laws of spatial differentiation were analyzed. Factor analysis was applied to examine the spatial differentiation of regional tourism competitiveness. The results showed an obvious spatial differentiation in tourism competitiveness among the 14 prefectures and cities. In terms of development competitiveness, Yili and Urumqi constituted the spatial center, followed by Changji, Altay, and Ba Prefecture. As the provincial capital, Urumqi has political, economic, cultural, transportation, and geographic advantages, but its competitiveness is not prominent in terms of monopoly and efficiency. In terms of resource competitiveness, Yili is the core attraction, while Urumqi, Kashgar, Altay, and Ba Prefecture are dominant attractions. With respect to supporting competitiveness, Bo Prefecture has high value, followed by Urumqi City and Aksu. Hetian and Ke Prefecture have the lowest values. The comprehensive competitiveness of tourism is centered on Yili. Urumqi and Bo Prefecture are subcenters, and Changji, Altay, Ba Prefecture, Aksu, and Kashgar are characterized as multi-polar competition areas. Using the KMO and Bartlett's sphericity tests, the cumulative contribution variance of the eigenvalues of the eight factors extracted by the maximum variance rotation method was found to be 92.714%. Socio-economic conditions, tourism resources, infrastructure construction, regional cultural influence, ecological environment carrying capacity, tertiary industry development, tourism service level, and living security system are the main driving factors affecting the spatial differentiation of tourism competitiveness in Xinjiang. Analyzing the spatial evolution characteristics and the driving factors of the regional tourism competitiveness in Xinjiang, this paper seeks to promote the optimal allocation of tourism production factors in the macro regional system, and provide theoretical guidance and an empirical basis for the comprehensive and harmonic development of regional tourism. [ABSTRACT FROM AUTHOR]

The Stoney formula is widely used to obtain residual stress in films on isotropic circular plates. However, in the case of large deformations, this formula produces significant errors because of the assumption of small deformations in its derivation. In this study, a modified Stoney formula that extends its scope of application to the nonlinear domain is proposed. A one-phase exponential decay function with a single coefficient p is used to relate the curvature of the substrate to the stress in the film. The coefficient p can be expressed as a function of the thickness, the diameter, Young's modulus, and Poisson's ratio of the circular plate. A linear fitting technique is applied to ascertain the relationship between the coefficient p and the aforementioned parameters. The modified Stoney formula is simple, yet accurate, and can be used to calculate the residual stress in the film directly from the measured curvature of isotropic circular plates with various dimensions and materials. [ABSTRACT FROM AUTHOR]

In this study, Ni/W–TiN composites were fabricated by the pulse current electrodeposition (PCE) method. The effects of TiN concentration on the microstructure, microhardness, and wear properties of the resulting composites were investigated by scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD), microhardness tester, and friction wear testing. Among the four obtained composites, Ni/W–TiN composite prepared at 8 g/L showed the densest and finest surface structure. The TiN contents in obtained Ni/W–TiN composites at 8 and 16 g/L were estimated to 8.1 and 5.4 wt%, respectively. The average Ni/W grain diameter in Ni/W–TiN composite obtained at 8 g/L TiN was recorded as 84.7 nm. The protrusion and depression heights of the composite deposited at 8 g/L were 81.8 and 45.4 nm, respectively. This composite also processed an average microhardness of 897.6 HV, with only a few shallow and narrow scratches on its worn surface, demonstrating its prominent wear resistance when compared to the other three composites. [ABSTRACT FROM AUTHOR]

Background: Motor neuron disease (MND) is a chronic and progressive neurodegenerative disorder with an unknown cause. The development of amyotrophic lateral sclerosis (ALS) is believed to be linked to an immune response. Monocytes/macrophages and T cells are key players in the disease's advancement. Monitoring levels of cytokines in the blood can help forecast patient outcomes, while immunotherapy shows promise in alleviating symptoms for certain individuals. Case presentation: A 56-year-old male patient was admitted to the hospital due to progressive limb weakness persisting for eight months. The neurological examination revealed impairments in both upper and lower motor neurons, as well as sensory anomalies, without corresponding signs. Electrophysiological examination results indicated extensive neuronal damage and multiple peripheral nerve impairments, thereby the diagnosis was ALS. One month ago, the patient began experiencing symptoms of dry mouth and a bitter taste. Following tests for rheumatic immune-related antibodies and a lip gland biopsy, a diagnosis of Sjögren's syndrome (SS) was proposed. Despite treatment with medications such as hormones (methylprednisolone), immunosuppressants (hydroxychloroquine sulfate), and riluzole, the symptoms did not significantly improve, but also did not worsen. Conclusion: It is recommended to include screening for SS in the standard assessment of ALS. Furthermore, research should focus on understanding the immune mechanisms involved in ALS, providing new insights for the diagnosis and treatment of ALS in conjunction with SS. [ABSTRACT FROM AUTHOR]

We investigated the impact of estrogen receptor (ER) expression in renal tubular epithelial cells on serum uric acid (UA) levels in premenopausal patients with systemic lupus erythematosus (SLE). Thirty patients underwent renal biopsy: 18 with SLE (LN group) and 12 with IgA nephritis (IgAN group). ERs (ERα and ERβ) in renal tubular epithelial cells were measured using immunohistochemistry. The ER expression levels of the two groups were compared, and the relationship between the expression of ERs and serum UA levels was analyzed. Mean serum UA levels in the LN group were significantly higher than those of the IgA nephropathy group, while the mean creatinine levels and GFRs of the two groups were similar. Pathological changes in the LN group were significantly more severe than those in the IgAN group. ERβ was expressed in renal tubular epithelial cells in both groups, but not in the glomeruli. ERβ expression in the LN group was significantly lower than that in the IgAN group. ERβ expression scores significantly negatively correlated with serum UA levels. These findings suggest that the expression of ERβ in premenopausal female SLE patients may cause hyperuricemia, and may subsequently promote glomerular damage, suggesting that ERβ may be involved in UA excretion. [ABSTRACT FROM AUTHOR]

The main goal of this study is to improve the dispersion stability of Al2O3 nanoparticles in polyalphaolefin oil to overcome the sedimentation problem of nanoparticles using the addition of oleic acid as a surfactant. Based on the previous single or insufficient research on the preparation conditions of nanolubricating oil, the effects of temperature, ultrasonic duration, ultrasonic power, and nanoparticle/surfactant (oleic acid) concentration on the dispersion stability of Al2O3 nanoparticles (size range of about 50nm) in oil-based solutions were systematically and comprehensively studied in this work. Herein, the preparation conditions were optimized and the stable nanofluids were obtained. The visual observation, UV–Vis spectroscopy, and dynamic light scattering (DLS) analysis were used to evaluate the dispersion stability of the Al2O3 nanoparticles. Moreover, the dispersion state of Al2O3 in PAO6 solution was demonstrated through transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) at a microscopic level, and the mechanism of dispersion stability was analyzed. The results reveal that the thermal method during the synthesis of nanofluids using 50°C temperature improves the dispersion of nanoparticles. The results also exhibited that there is an optimal match between the ultrasonication power and the ultrasonication duration during the synthesis of nanofluids, which affects its dispersion stability. The results showed that 0.4%wt% oleic acid was needed for the nanolubricants containing 0.005wt% and 0.01wt% to achieve excellent dispersion stability, and the stabilization time exceeds 160 days. [ABSTRACT FROM AUTHOR]

Microbial community shifts of a freshwater wetland with crude oil pressure were investigated fully as a function of cultivation time, using the enrichment procedure and high-throughput sequencing. Bacterial structures and biodegradation capabilities were apparently altered over different cultivation periods. Flavobacterium was the dominant genus in the original water sample, followed by hgcl_clade and Limnobabitans. However, during a long-term successional cultivation with crude oil, a potential microbial consortium was successfully rebuilt that was mainly composed of hydrocarbon-degrading genera, such as Pseudomonas, Rhodococcus, and Nocardia. Furthermore, the cultivation time was a significant factor that contributed to the complexity and relative stability of the bacterial community. Additionally, the majority of crude oil fractions were degraded by the enriched consortium. For example, n-alkanes (m/z 85) in the crude oil were fully depleted, and polycyclic aromatic hydrocarbons, such as naphthalenes, decalins, fluorenes, phenanthrenes, dibenzothiophenes, and chrysenes, were partially degraded from 10% to 100%. Moreover, the degree of biodegradation varied during different cultivation periods and within compound families. The microbial consortium of hydrocarbon degradation can be constructed from the freshwater wetland and this finding is useful when considering bioremediation of petroleum contamination in natural wetlands. [ABSTRACT FROM AUTHOR]

Wheat production in the North China Plain (NCP) is critical to food security in China; however, it is affected by climate change. Understanding the spatiotemporal distribution of climate suitability of wheat in NCP may help to rationally use climatic resources as well as reduce natural disasters. Out of 30 general circulation models in the Coupled Model Intercomparison Project Phase 5, the 5 best GCMs were selected for each meteorological factor to project daily data in 2021–2100 under representative concentration path 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations in NCP. Based on agricultural climatic suitability theory and the fuzzy mathematics method, the suitability of water, temperature, and solar radiation was analyzed. The results showed that the water suitability increases from 0.30 at present to 0.40 for 2021–2100 under both RCP4.5 and RCP8.5 with the lowest mostly distributed in the northwest. The temperature suitability varies from 0.80 at present to 0.75 for 2021–2100 and generally increases from north to south; however, at stage 2 (from overwintering to greening), it drops from 0.67 to 0.50 under RCP4.5 and 0.20 under RCP8.5 for 2081–2100. The decreases of water suitability at stage 5 (from heading to milking) and temperature suitability at stage 2 show negative effect of climate change to winter wheat. The solar radiation suitability shows an increasing trend under two scenarios in the future. Finally, the integrated climate suitability shows a little increasing trend, indicating climate condition of winter wheat in NCP will be slightly improved. [ABSTRACT FROM AUTHOR]

Maize production on the North China Plain (NCP) is critical to food security in China; however, currently, it is affected by climate change. Understanding the spatiotemporal distribution of the climate suitability for maize on the NCP in the present and future may help sustainably use climate‐related resources to ensure food security in China. In this study, 30 general circulation models from the Coupled Model Intercomparison Project Phase 5 and a statistical downscaling model (NWAI‐WG) were used to project meteorological data in 2021–2100 under representative concentration pathway 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations on the NCP. Based on agricultural climate suitability theory and the fuzzy mathematics method, the suitability of temperature, precipitation, and solar radiation on summer maize were analysed. The results showed that temperature suitability is decreasing for 2021–2100, especially during the stages from jointing to maturity; temperature suitability is the lowest in the southwest and increases to the northeast under both scenarios. Compared to 1996–2015, the precipitation suitability in 2021–2100 increases greatly under both scenarios, especially in the central part under RCP4.5 and in the north part under RCP8.5. Solar radiation suitability shows a decreasing trend for 1996–2015, however, an increasing trend for 2021–2100 under both scenarios. At spatial scale, the solar suitability increases from southwest to northeast. The integrated climate suitability under RCP4.5 in 2021–2100 is averaged approximately 0.8 and varies slightly, indicating climate change may do small effect on maize growth, though the high values shifting from the central part in 2021–2040 to the northern part in 2081–2100; however, under RCP8.5, the integrated climate suitability shows a downward trend, indicating that climate change will make many regions less suitable for maize growth. These results could provide basic information for agriculture to adapt to climate change and ensure food security for China. [ABSTRACT FROM AUTHOR]

Existing person re-identification has achieved great progress in the visible domain, capturing all the person images with visible cameras. However, in a 24-hour intelligent surveillance system, the visible cameras may be noneffective at night. In this situation, thermal cameras are the best supplemental components, which capture images without depending on visible light. Therefore, in this paper, we investigate the visible-thermal cross-modality person re-identification (VT Re-ID) problem. In VT Re-ID, there are two knotty problems should be well handled, cross-modality discrepancy and intra-modality variations. To address these two issues, we propose focusing on enhancing the discriminative feature learning (EDFL) with two extremely simple means from two core aspects, (1) skip-connection for mid-level features incorporation to improve the person features with more discriminability and robustness, and (2) dual-modality triplet loss to guide the training procedures by simultaneously considering the cross-modality discrepancy and intra-modality variations. Additionally, the two-stream CNN structure is adopted to learn the multi-modality sharable person features. The experimental results on two datasets show that our proposed EDFL approach distinctly outperforms state-of-the-art methods by large margins, demonstrating the effectiveness of our EDFL to enhance the discriminative feature learning for VT Re-ID. [ABSTRACT FROM AUTHOR]

Primary cardiac lymphomas display a low frequency, sudden onset, swift progression of illness and elevated mortality rates. The current study presents a unique instance of primary cardiac diffuse large B-cell lymphoma and examines its clinical manifestations, pathological characteristics and differential diagnosis. A 64-year-old male patient sought medical attention due to cardiac debility and exertional dyspnea persisting for >10 days. Chest enhanced computed tomography revealed a moderately enhancing irregular mass in the ventricular area, exhibiting limited demarcation from the pericardium and left atrium, accompanied by irregular thickening of the interventricular septum. The postoperative specimen showed the presence of yellow fish-like tumor tissue. Immunohistochemical analysis revealed the presence of lymphocytes positive for CD20, BCL-2, BCL-6, c-Myc-binding protein, mutated melanoma-associated antigen 1 and CD79a, along with a high Ki-67 proliferation index of 80%. Conversely, CD10, CD30, CD3, pan cytokeratin, cyclin D1, desmin and vimentin marker results were found to be negative. Additionally, in situ hybridization demonstrated a lack of Epstein-Barr virus-encoded small RNA expression. The present case report emphasizes the significance of conducting a thorough analysis of the clinical manifestations of diffuse large B-cell lymphoma to assist clinicians in establishing a diagnosis and determining an effective treatment approach, thereby enhancing the patient's prognosis. [ABSTRACT FROM AUTHOR]

Multivariate distribution models for geo-material parametric data play a crucial role in reliability-based design. Currently, two major models are commonly used: the conventional multivariate normal distribution model and the copula model (including subtypes like the elliptical copula model and Vine copula model). This study aims to assess the performance of different models including three specific copula models (Gaussian copula, t copula, and D-Vine copula models) and the conventional normal distribution model. To this end, this study employs an incomplete database comprising 693 data samples related to lacustrine soils near Dianchi Lake in Kunming city, China. Results indicate that copula models generally exhibit lower model uncertainty compared to the conventional multivariate normal distribution model. Using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the elliptical copula ranks first, followed by Gaussian copula, with D-Vine copula ranking third. For a balanced evaluation of performance and complexity, the conventional multivariate normal distribution model is favored. This study provides valuable insights for selecting appropriate models for geo-material parametric data, aiding reliability-based design in geotechnical engineering. Additionally, it is demonstrated that sampling with replacement in the Bootstrapping technique has only a slight effect on the estimation of correlation matrices for the incomplete database. [ABSTRACT FROM AUTHOR]

Polyaniline is a promising material for the development of high performance flexible supercapacitor devices. However, the loose structure of polyaniline materials and the high intermolecular transport impedance between molecular chains remain significant technical barriers, particularly at high loading density. In this study, we developed a copper ion doped polyaniline electrode (Cu-PANI@CC) on carbon cloth using a simple one-step in-situ electrochemical deposition method. The structure and electrochemical properties of this electrode were investigated via different testing techniques. Benefiting from the catalysis and regulation of copper ions between PANI molecular chains, the electrode demonstrated a relatively low internal resistance at high loading densities (> 10 mg cm−2). Moreover, this electrode exhibited specific capacitance of 3.49 F cm−2 at 5 mA cm−2 and capacitance retention of 83.31% at 50 mA cm−2. Two Cu-PANI@CC electrodes were used to fabricate a symmetrical capacitor, which exhibited remarkable cycling stability and wide operating temperature range. The excellent electrochemical performance of the Cu-PANI@CC electrode highlights the potential for use in high-performance supercapacitor devices. • High loading density of Cu2+ doped PANI electrode is achieved with low internal resistance. • This electrode exhibits an excellent specific capacitance (3.14 F cm−2 at 5 mA cm−2) and rate capability. • The preparation of Cu-PANI material is capable of ultrahigh energy density devices. [ABSTRACT FROM AUTHOR]

Uniform dispersion of pristine graphene in polymers has always been a difficulty in the research and industry. In this paper, a simple but effective method was used to uniformly disperse the pristine graphene in NR matrix to prepare an NR/pristine graphene composite. The conductivity of this composite with 5 wt% graphene loading fraction was ∼0.005 S/m, around 4 orders of magnitude higher than that prepared by other methods with a similar loading fraction. The gauge factor (GF) of the strain sensor using the as-prepared composite as the component could be as high as 46, taking a lead positon in the reported strain sensors. Owing to the high sensibility, the sensor was suitable to be used for the detection of very subtle motions such as breathing or heartbeat. In a wrist-pulse testing, premature beats, less forceful heartbeats, and the "P""T""D″ waves of the heartbeats could be precisely monitored and distinguished. Swallowing and vocalization could also be efficiently detected by attaching the sensor on the subject's throat. Image 1 • Convenient method to uniformly disperse pristine graphene in natural rubber matrix was proposed. • Pressing treatment affected the conductivity and sensitivity of the composite. • The conductivity of composite with 5 wt% graphene loading fraction was around 0.005 S/m. • The GF of the strain sensor was as high as 46. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Ultrasound-assisted supercritical CO 2 was used to prepare graphene infused rubber. • Rubber can be fully swollen after scCO 2 penetration and rapid decompression. • Graphene was infused in NR after ultrasound-assisted scCO 2 and rapid decompression. • The surface conductivity of the as-prepared graphene/NR film could reach to 0.2 S/m. • Graphene/infused NR film was used as strain sensor for detecting of fine movements. Abstract In recent years, graphene has become the most popular conductive filler in conductive polymers which can be used in many practical applications. A proper selection of the mixing method is the decisive factor to maximize the excellent conductivity of graphene in a polymer matrix. In this work, ultrasound-assisted supercritical CO 2 (scCO 2) method was used for the first time to prepare graphene infiltrated conductive natural rubber (NR). Many interesting phenomena were discovered. First, it was found that NR was fully swollen after an ultrasound-assisted scCO 2 penetration and a following rapid decompression process. The degree of expansion of NR in scCO 2 was even larger than in toluene. Secondly, ultrasound-assisted scCO 2 method coupled with a rapid decompression process successfully inserted graphene particles in the swollen NR to prepare conductive NR/graphene materials. The impacts of the decompression method, vulcanization degree, and ultrasonication duration on graphene infusion were studied and analyzed. Under an optimal condition, the surface conductivity of as-prepared unvulcanized graphene/NR film or vulcanized film with a low graphene loading fraction (0.34 vol%) could reach to 0.2 S/m or 0.02 S/m, respectively, taking a leading position in the reported graphene/NR composites. Our conductive NR was shown to be satisfactory as a strain sensor for the measurement of fine movements of human body such as heartbeat, finger bending, and vocalization. Since only scCO 2 , one of the most environmentally benign solvents, was used in the whole process, this routine is not only efficient but also environmentally friendly. We believe this method is versatile and can be applied to many other polymers that can be swollen in scCO 2. [ABSTRACT FROM AUTHOR]

The effective management of enterprise resources in internet of things is a growing research trend in modern era. The big data concept influences more opportunities to the researchers to exhibit their innovations in various application oriented fields. The academic and organizational researches suggested that big data provides more benefits on sustained basis for the committed resource management environment. The study shows positive and active enterprise culture is positively correlated to effective internal control. In the enhancement of these resource management initiatives, the dynamic capability of the data and its assumptions are considered. In the case of enterprise culture the implication of practical and business limitations and advantages are discussed in this article. The intellectual contributions and the poles of excellence on the field of big data is a common platform upon which several communication components in practice on every day use which are interfaced and interconnected. In general IoT reduces the distance between the real world and internet by incorporating and integrating different perspectives of technology such as pervasive computing, data communication, networking, sensor node communication etc., [ABSTRACT FROM AUTHOR]

Abstract Recently more and more attention is paid to the re-ranking step for person re-identification in computer vision community, especially those fully automatic, unsupervised solutions. Among them, k -reciprocal re-ranking (KR) method achieved big success. However, when the heavy cross-camera discrepancy exists between query and gallery datasets, it may degrade the performance. To alleviate the heavy cross-camera discrepancy between query and gallery datasets, we propose a gallery based k -reciprocal-like re-ranking (GKR) method. GKR adopts graph matching to construct the matching correspondence between query and gallery datasets. Then the proposed k -reciprocal-like neighbors are computed only on gallery dataset instead of on the union of query and gallery datasets like KR does. Moreover, to perform unsupervised video-based person re-identification, we incorporate our proposed GKR method into the dynamic label graph matching (DGM) framework, which can improve the cross-camera labels estimating in training step but also can improve the re-identification accuracy by re-ranking in testing step. Experimental results by supervised and unsupervised solutions on some benchmarks, demonstrate the effectiveness of our GKR method to handle the cross-camera discrepancy problem for person re-identification. [ABSTRACT FROM AUTHOR]

Abstract In this work, a novel and facile technique, epoxy-water-inorganic filler suspended emulsion polymerization, was proposed to fabricate 3D network porous graphene nanoplatelet (GNP)/Fe 3 O 4 /epoxy nanocomposites with low density of 0.34–0.73 g/cm3. The obtained porous nanocomposite with 7 wt% graphene nanoplatelets and 7 wt% Fe 3 O 4 nanoparticles loading exhibited satisfactory specific electromagnetic interference (EMI) shieling effectiveness of ∼37.03 dB/(g/cm3), which was much higher than that of the solid counterparts (28.30 dB/(g/cm3)). In addition, the porous GNP/Fe 3 O 4 /epoxy nanocomposites also possessed outstanding thermal stability and mechanical property, which makes it a potential EMI shielding material in the fields of aerospace and electronic industries. [ABSTRACT FROM AUTHOR]

To improve the lithium storage capacity, electrochemical kinetics and stability of TiO 2 nanostructured electrode, a hybrid electrode of TiO 2 nanotubes coated with nitrogen-doped carbon (TiO 2 @NC) was rationally designed and prepared. With the unique coaxial nanotube structure, the porous TiO 2 nanotubes would offer short paths for Li + diffusion and numerous active sites, while the N-doped carbon matrix would prefer fast charge transportation and buffer the volume change of TiO 2 during lithiation/delithiation processes. In particular, a high reversible specific capacity of 480 mA h g −1 at a current density of 100 mA g −1 was achieved when evaluated as anodes for lithium ion batteries. The high capacity, outstanding cycling stability, and remarkable rate performance may attributed to its unique morphology and the synergetic effect of porous TiO 2 nanotube and conductive N-doped carbon nanoshell on the basis of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). [ABSTRACT FROM AUTHOR]

Background/Aims: MicroRNAs (miRNAs) have been shown to participate in the development of pancreatic ductal adenocarcinoma (PDAC) by modulating multiple cellular processes. Increased miR-224 expression enhances proliferation and metastasis in human cancers. This study aimed to investigate the role of miR-224 and its underlying mechanism of action in PDAC. Methods: BrdU, MTT, and cell migration assays were performed to determine cell proliferation, viability, and migration, respectively. The binding sites of miR-224 were identified using a luciferase reporter system, whereas protein expression of target genes was determined by immunoblotting and immunofluorescence analyses. A BALB/c nude mouse xenograft model was used to evaluate the role of miR-224 in vivo. Results: We demonstrated that miR-224 expression was enhanced in PDAC cells and tissues, and was related to migration and proliferation. Noticeably, miR-224 overexpression promoted the proliferation, migration, and metastasis of Panc1 cells, while miR-224 inhibition had the reverse effect on PDAC cells. Moreover, we found that thioredoxin-interacting protein (TXNIP) is a target of miR-224. The results also indicated that miR-224 inversely regulated TXNIP by binding directly to its 3′-untranslated region, which resulted in the activation of hypoxia-inducible factor 1α (HIF1α). Further, either TXNIP re-expression or HIF1α depletion abolished the effects of miR-224 on the proliferation and migration of PDAC cells in vitro and in vivo. Regarding the relationship of TXNIP and HIF1α, we found that TXNIP mediated the nuclear export of HIF1α and its degradation by forming a complex with HIF1α. Conclusion: The miR-224-TXNIP-HIF1α axis may be useful in developing novel therapies for PDAC. [ABSTRACT FROM AUTHOR]

In this paper, a new momentum of stock in distribution is proposed and applied in real investment. Firstly, assuming that a stock behaves as a multi-particle system, its share-exchange distribution and cost distribution are introduced. Secondly, an estimation of the share-exchange distribution is given with daily transaction data by 3 σ rule from the normal distribution. Meanwhile, an iterative method is given to estimate the cost distribution. Based on the cost distribution, a new momentum is proposed for stock system. Thirdly, an empirical test is given to compare the new momentum with others by contrarian strategy. The result shows that the new one outperforms others in many places. Furthermore, entropy of stock is introduced according to its cost distribution. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus is becoming increasingly prevalent worldwide, and has become one of the greatest threats to global health. Bariatric surgery was initially designed to achieve weight loss, and subsequently was noted to induce improvements or remission of type 2 diabetes. Currently, these bariatric operations, such as Roux-en-Y gastric bypass and sleeve gastrectomy, are the most effective procedures for the treatment of obesity and type 2 diabetes mellitus worldwide. However, the specific mechanism mediating the beneficial effects of metabolic surgery has remained largely unknown. Those mechanical explanations, such as restriction and malabsorption, are challenged by accumulating evidence from human and animal models of these procedures, which points to the weight-independent factors, such as hormones, bile acids, gut microbiota, nervous system and other potential underlying mechanisms. A growing body of evidence suggests that gut microbiota are associated with the development of several metabolic disorders, and bile acids and FXR signaling are important for the metabolic benefits of bariatric surgery. Given the close relationship between bacteria and bile acids, it is reasonable to propose that microbiota-bile acid interactions play a role in the mechanisms underlying the effects of metabolic surgery. [ABSTRACT FROM AUTHOR]

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. [ABSTRACT FROM AUTHOR]