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Vessel transplantation is currently considered the “gold standard” treatment for cardiovascular disease. However, ideal artificial vascular grafts should possess good biocompatibility and mechanical strength that match those of native autologous vascular tissue to promote in vivo tissue regeneration. In this study, a series of dynamic cross-linking double-network hydrogels and the resultant hydrogel tubes were prepared. The hydrogels (named PCO), composed of rigid poly(vinyl alcohol) (PVA), flexible carboxymethyl chitosan (CMCS), and a cross-linker of aldehyde-based β-cyclodextrin (OCD), were formed in a double-network structure with multiple dynamical cross-linking including dynamic imine bonds, hydrogen bonds, and microcrystalline regions. The PCO hydrogels exhibited superior mechanical strength, good network stability, and fatigue resistance. Additionally, it demonstrated excellent cell and blood compatibility. The results showed that the introduction of CMCS/OCD led to a significant increase in the proliferation rate of endothelial cells seeded on the surface of the hydrogel. The hemolysis rate in the test was lower than 0.3%, and both protein adsorption and platelet adhesion were reduced, indicating an excellent anticoagulant function. The plasma recalcification time test results showed that endogenous coagulation was alleviated to some extent. When formed into blood vessels and incubated with blood, no thrombus formation was observed, and there was minimal red blood cell aggregation. Therefore, this novel hydrogel tube, with excellent mechanical properties, exhibits antiadhesive characteristics toward blood cells and proteins, as well as antithrombotic properties, making it hold tremendous potential for applications in the biomedical and engineering fields.

Lightweight envelopes are increasingly utilized in temporary relief and shelter buildings, due to their cost-effectiveness and efficient construction process. However, these envelopes often result in inadequate indoor thermal conditions during the summer months, which can negatively impact the physical and psychological well-being of occupants, particularly victims and patients in disaster or epidemic situations. Addressing this challenge necessitates the use of simple yet effective materials and technologies to improve indoor thermal environments even with limited resources. This study explores the potential of shading nets to enhance indoor thermal conditions in lightweight buildings by mitigating solar radiation gain during the summer period. A comparative experiment was conducted on two lightweight buildings, one of which was equipped with standard six-needle black shading nets, to assess the impact of shading nets on the indoor thermal environment and air-conditioning energy consumption. The findings reveal that shading nets can reduce solar radiation intensity by approximately 57.86%, resulting in a significant decrease in peak indoor air temperatures by 2.3°C to 3.4°C under natural ventilation and a reduction in air-conditioning energy consumption by up to 23% under air-conditioned conditions. These findings demonstrate that shading nets are effective in improving indoor comfort and reducing energy usage, providing a practical solution for enhancing living conditions in lightweight structures during hot weather.

Online estimation for product quality is crucial for improving industrial process efficiency. However, model degradation and outliers usually challenge industrial quality estimation models. To tackle the above problems, a robust self-constructing fuzzy neural network (RSC-FNN) is developed in the article. In the RSC-FNN, the rules can be automatically created or pruned, obtaining the online self-constructing mechanism (OSCM). First, an online error compensation algorithm is developed to generate new rules. Second, the model performance and contribution of existing rules are evaluated online to delete redundant rules. Thus, the OSCM effectively improves the structural adaptability and compactness of the RSC-FNN. Moreover, the correntropy-induced criterion, which can handle complex outliers, is modified for the parameter learning algorithm. Hence, the adverse effect of outliers can be suppressed during the parameter updating process. Besides, we analyze the convergence of the RSC-FNN to ensure its feasibility in industrial applications. Finally, two industrial applications are studied to test the effectiveness of the RSC-FNN. Compared with other FNNs, the results indicate that the RSC-FNN performs better in learning efficiency, structure compactness, and robustness.

Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) and hierarchical learning method is utilized to achieve considerable modeling accuracy and efficiency. Second, to better tackle with the non-stationary characteristic, an unsupervised distribution detection method is proposed to identify the occurrence moments of concept drift actively from the perspective of probability and spatial projection. Then, by an active semi-supervised learning method, the concept drift samples with partial labels are used to build a subnetwork, guaranteeing the performance of the constructed IT2FNN models with incomplete data in non-stationary environments. Finally, the proposed ASSL-IT2FNN is verified by benchmark simulations and real industrial data, and the experimental results demonstrate its outperformance.Note to Practitioners—Concept drift is an important indicator of a non-stationary environment. It manifests as changes in the distribution of samples over time, which may reduce the accuracy of models constructed based on historical samples. This study aims to alleviate the impact of concept drift on model performance in non-stationary industrial processes while reduce the cost of sample labeling. By using the adaptive FMF and hierarchical learning method, the designed IT2FNN can effectively deal with the inherent nonlinearity of complicated industrial processes. The samples representing concept drift are actively identified by the Kullback-Leibler (KL) divergence and maximum mean difference (MMD), enabling timely detections of concept drift moments in unsupervised situations. The active semi-supervised learning is used to adapt the constructed model to handle the concept drift, wherein a subnetwork is designed based on concept drift samples and similar historical samples.

Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex tasks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which affects the performance of MNN. For this reason, a multisource transfer learning-based MNN (MSTL-MNN) is proposed in this study. First, the knowledge-driven transfer learning process is performed with domain similarity evaluation, knowledge extraction, and fusion, aiming to form an initial subnetwork in the target domain. Then, the positive transfer process of effective knowledge can avoid the cold start problem of MNN. Second, during the data-driven fine-tuning process, a regularized self-organizing long short-term memory algorithm is designed to fine-tune the structure and parameters of the initial subnetwork, which can improve the prediction performance of MNN. Meanwhile, relevant theoretical analysis is given to ensure the feasibility of MSTL-MNN. Finally, the effectiveness of the proposed method is confirmed by two benchmark simulations and a real industrial dataset of a municipal solid waste incineration process. Experimental results demonstrate the merits of MSTL-MNN for industrial applications.

Oxygen content in flue gas is a key variable in the operation of the municipal solid waste incineration (MSWI) process. However, the control performance of oxygen content could not be guaranteed due to the inherent nonlinearity and uncertainty of the MSWI process. Furthermore, frequent operations of actuators in the conventional time-triggered system may also increase the computational burden and energy consumption. In this study, an event-triggered adaptive model predictive control (ET-AMPC) scheme is developed to solve the above problems. First, an improved long short-term memory (ILSTM) neural network is designed to construct the prediction model, in which the parameters are determined by the particle swarm optimization (PSO) algorithm. Besides, during the control process, the model parameters can be adjusted adaptively by an online updating strategy to tackle the uncertainty. Second, an event-triggered strategy is proposed to reduce the computational burden and energy consumption, wherein the control laws are updated only when certain triggering conditions are satisfied. Third, the gradient descent method is applied to obtain the optimized control law. Moreover, the convergence of the prediction model and the stability of the whole ET-AMPC are analyzed. Finally, the proposed ET-AMPC scheme is evaluated by real industrial data. The experimental results demonstrate that the ET-AMPC scheme can achieve satisfactory tracking control performance with fewer triggering events. Note to Practitioners—The proposed ET-AMPC is an intelligent control scheme of oxygen content for the MSWI process. The proposed control scheme has adequate tracking performance with varied set-points and less computational burden. In practice, practitioners should obtain accurate historical input and output data, and preset event-triggered thresholds. The hyperparameters of the LSTM neural network are automatically determined by the PSO algorithm, and the uncertainty can be captured with the online updating strategy. The accurate prediction model is beneficial for improving the control quality. Moreover, the optimized control law is obtained with the intuitive gradient descent method. In addition, the event-triggered strategy can further reduce unnecessary control optimization operations with better practicality and ease of usage. The superiority and practicability of the proposed method are verified against actual industrial data.

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Jujube variety classification is a challenging task because of the difficulty in identifying discriminant features, making it difficult to find the subtle features that can fully represent the variety. Besides, jujube identification using single-organ fruit is not sufficiently reliable because different jujube varieties usually have similar fruit shape. To overcome these problems, this paper proposed an automatic jujube identification model based on attention mechanism by combining multiple organs of jujube. The model used a conventional neural network to perform feature extraction on images, and subsequently adopted some fusion techniques to further process the feature maps. By introducing the attention mechanism, the model could recalibrate channel and spatial characteristic responses adaptively so as to focus on the more discriminative regions of the images. Based on the idea of fusing multi-organ features, the network effectively obtained more significant cues for jujube recognition. Experimental results showed that the proposed network had a higher accuracy of 94.77% on jujube classification compared with other methods. It is demonstrated that the network was of great value to jujube recognition research.

Data-driven model predictive control (MPC) has been regarded as an attractive control technology for nonlinear dynamic processes. However, as a model-based approach, data-driven MPC usually suffers from an inaccurate model, process uncertainty, and calculation burden. To provide an efficient and accurate controller for nonlinear dynamic processes, an event-based data-driven adaptive MPC (EDAMPC) scheme is proposed. First, a prediction model employing a self-organizing long short-term memory (SOLSTM) neural network is developed to obtain a compact model structure and improve the generalization ability. The structure of the SOLSTM neural network is constructed dynamically by integrating the activity and significance of the neurons. Second, an error-triggered online learning mechanism is developed to update model parameters adaptively based on prediction errors. The nonlinear process dynamics can be captured in the presence of process uncertainty. Third, an event-based control strategy is introduced to reduce communication and computation resources. The convergence of the SOLSTM neural network and the nominal stability of the EDAMPC scheme is analyzed. Finally, the effectiveness and superiority of the EDAMPC scheme are demonstrated via a numerical case and an industrial application.

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

This paper presents the theory and implementation of a balanced dual-core inverse-class-F (class-F $^{\mathrm{ -1}}$ ) voltage-controlled oscillator (VCO). The class-F $^{\mathrm{ -1}}$ topology supports high-quality-factor (high- $Q$ ) differential switched-capacitors (SCs) for both fundamental and 2 $^{\mathrm{ nd}}$ -harmonic frequency tuning, which is beneficial for improving the phase noise (PN). However, the unequal parasitic capacitors from the NMOS and PMOS negative ${g}$ textsubscript m transistors make it impossible to minimize their flicker noise upconversions simultaneously, especially at high operating frequencies. The mechanism of this effect is analyzed qualitatively with the model of coupled oscillators and verified using the impulse-sensitivity function (ISF) approach. To address this issue, we propose a dual-core class-F $^{\mathrm{ -1}}$ VCO that leverages a balanced coupling scheme to minimize the flicker noise upconversions of NMOS and PMOS transistors simultaneously and still keep the advantage of tuning the 2 $^{\mathrm{ nd}}$ -harmonic frequency with differential SCs offered by the class-F $^{\mathrm{ -1}}$ topology. Additionally, the symmetrical circuit topology aids in improving the differential output balancing. Prototyped in a 28-nm CMOS process without ultra-thick metal, the balanced dual-core class-F $^{\mathrm{ -1}}$ VCO dissipates 19.7-mW and achieves a PN of $\!-\!113.9/\!-\!135.8$ -dBc/Hz at 1/10-MHz offset from an 18.23-GHz carrier. Tuned from 15.22 to 18.23-GHz, the proposed VCO exhibits superior figure-of-merits (FoMs) at 1/10-MHz offset from 185.3/187.0 to 186.2/188.1-dBc/Hz.

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx emissions prediction. First, the neighborhood rough set is introduced to determine the structure of this fuzzy neural network automatically, including the number of fuzzy rules and their corresponding consequent parameters. Second, an adaptive shape factor is added to the fuzzy membership function to better cope with the uncertainty, which can help to improve the generalization ability of network. Furthermore, to reduce the computational complexity, the Begian–Melek–Mendel method is utilized as the defuzzification method in this article. Then, by integrating the linear least square estimation and gradient decent, a hierarchical learning algorithm is applied to adjust the network parameters to further enhance the learning efficiency and accuracy. Finally, after being evaluated by a benchmark simulation, the proposed CI-IT2FNN demonstrates its effectiveness and superiority on NOx emissions prediction.

Jujube is one of the important members of the Rhamnaceae family, which has high economic and nutritional value. The automatic classification of jujube varieties is of great importance for the development of the jujube industry. The task of jujube variety classification based on image data belongs to fine-grained visual classification, which is relatively more difficult. Recently, some deep learning approaches have been available, but capabilities remain limited. In this paper, we present a fine-grained classification model for jujube varieties, named JLN. It is based on MobileNetV3 and multi-task learning. JLN performs shape classification and fine-grained classification simultaneously through a multitask framework and learn more phenotypic differences among different varieties by mining the relationship between the two tasks. In order to further improve the classification performance of JLN, we let the fine-grained features involved in the task of shape classification but prohibited this process in the model's backward passing stage. Finally, we used Reduce Focal Loss to train the shape classification task and jointly optimize the variety classification task through Arcface and Softmax Loss. To further facilitate the study, we established an image dataset of 20 jujube varieties and divided them into 7 categories according to their shapes under the advice of experts. The proposed method achieves 91.79% accuracy on the jujube variety data, and the number of model parameters is only 3.56 M, which is better than the other four models. The experimental results show that the method is more suitable for jujube variety classification tasks in the natural environment.

Exploration of the optoelectronic memristor is required to investigate the photoelectric properties of materials. The traditional memristor material GeAs2Te4 is hopeful to be developed into a new type of optoelectronic memristor. However, acquiring high-quality single crystals remains challenging, and the electrical properties of single crystals of GeAs2Te4 need to be explored. Herein, a controlled method is introduced to grow reliable quality GeAs2Te4 single crystals, and the electrical and optoelectronic properties are studied. The photodetector based on GeAs2Te4 exhibits acceptable optoelectronic performance at designed low temperatures. The responsivity and detectivity of the GeAs2Te4-based photodetector reached the value of about 0.137 A W-1 and 6.9×107 Jones, respectively. It is promising to introduce this family of materials into the field of photodetector and also maybe further in the area of optoelectronic memristors.

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Due to the large uncertainty in the municipal solid waste incineration (MSWI) process, the furnace temperature of the MSWI process is difficult to control and the controller is updated frequently. To improve the accuracy and reduce the number of controller updates, a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network (ET-CSTSFNN) is proposed. Firstly, the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating condition. Meanwhile, the performance index is designed based on the correntropy of tracking errors, and the parameters of the controller are adjusted by gradient descent algorithm. Secondly, a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or not. The stability of the control system is proved based on the Lyapunov stability theory. Finally, the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in Beijing. The experimental results show that the proposed ET-CSTSFNN controller shows a better control performance, which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

Selective non-catalytic reduction (SNCR) is a commercially available technology that can effectively reduce the nitrogen oxides ( ${\mathrm {NO}}_{x}$ ) emissions in municipal solid waste incineration (MSWI) processes. Real-time measurement of NOx emissions is important to improve the denitration efficiency of SNCR. However, there are commonly time delays to obtain the measurement results from the traditional continuous emission monitoring system (CEMS). In this study, a prediction model based on multitask learning (MTL) is proposed for real-time measurement of NOx emissions. First, time delays are analyzed by using the maximum average cross-correlation function (MACCF) method, and sequences are adjusted according to the time delays, thereby recovering the original data pattern. Second, input variables are selected using a maximal information coefficient (MIC) method to reduce redundant information. Then, an MTL model is constructed for the prediction of NOx emissions. By using the MTL mechanism to share information of related prediction tasks, the potential correlations of industrial time-series data are mined, therefore improving the generalization performance of the model. Also, a self-organizing radial basis function neural network is designed as the module of the MTL model to further improve the prediction accuracy of the model. A strategy for incrementally constructing the MTL model is developed to guarantee the computational efficiency of the model. Finally, the established model is tested using a benchmark dataset and the real industrial data from an MSWI plant. The experimental results show that the proposed MTL model outperforms other state-of-the-art models on computational efficiency and prediction accuracy, demonstrating the potentiality of the MTL model in real-time measurement of NOx emissions in MSWI processes.

Sweet tea is made from the leaves of Lithocarpus litseifolius(Hance) Chun, which is an herbal tea widely consumed in Chinese folk. In this study, five drying techniques were applied to dry sweet tea leaves, and the structural characteristics, antioxidant activities, α-glucosidase inhibitory activities, and antiglycation activities of sweet tea polysaccharides (STPs) were systematically evaluated. The extraction yields of STPs ranged from 2.37 to 4.09%, and STPs prepared by hot-air drying had the highest extraction yield. The five polysaccharides exhibited the same constituent monosaccharides, similar preliminary structural characteristics, different molecular weights (Mw), and different molar ratios of constituent monosaccharides. Critically, five STPs solutions exhibited non-Newtonian behavior, and STPs obtained by hot-air drying had the highest Mwand apparent viscosity. Compared with other drying technologies, relatively high total phenolic content was measured in the STPs obtained by freeze-drying (31.32 ± 1.77 mg GAE/g) and hot-air drying (29.13 ± 1.36 mg GAE/g). Furthermore, five STPs exhibited excellent biological properties, including antioxidant activities, α-glucosidase inhibitory activities, and antiglycation activities. Ultimately, the heat map analysis found that the bioactivities of STPs were related to their high Mwand high content of total phenolic. All results suggested that STPs, especially polysaccharides prepared by hot-air drying and freeze-drying, had good development prospects and application values as functional ingredients for the antidiabetic functional food development.

Chiral thermally activated delayed fluorescence (TADF) molecules showing circularly polarized luminescence (CPL) have great potential in 3D displays. However, the relationships among CPL property, device performance and molecule structure are still not clear. In this article, we develop a strategy to promote dissymmetry factors without sacrifice in device performance and study the impact of molecule structures towards CPL property. Three novel TADF enantiomers are synthesized and studied. (R/S)-SCN with diminutive cyano group as an acceptor shows dissymmetry factor ∣gPL∣ ≈ 1.4×10−3and noticeable organic light-emitting diode (OLED) performances with a maximum external quantum efficiency (EQEmax) of 23.0%. For (R/S)-SPHCN, the prolonged electron withdrawing group benzonitrile enhances ∣gPL∣ up to 3.6×10−3with decreased device EQEmaxof 15.4%. By further replacing benzonitrile with (trifluoromethyl)pyridine, the enantiomers of (R/S)-SCFPY show similar ∣gPL∣ factors of 3.5×10−3and device EQEmaxup to 23.3%, which represents the highest efficiency among sprio-type TADF materials based OLEDs. Furthermore, the OLEDs also show obvious circularly polarized electroluminescence with gELfactors of −1.4/1.8×10−3, −3.6/3.6 ×10−3and −3.7/3.6×10−3, respectively. These results indecate by delicate functional group engineering, high gfactor can be achieved while maintaining decent device performances. Besides, (R/S)-SCFPY represents an impressive TADF emitter, which shows promoted gfactor and recorded high device EQEmaxamong similar molecules.

Landfast ice plays an important role in atmosphere‒ocean interactions and ecosystems in the near coast area of Antarctica. Understanding the characteristics and variations of landfast ice is crucial to the study of climates and field activities in Antarctic. In this study, a high-resolution thermodynamic snow-ice (HIGHTSI) model was applied to simulate the seasonal changes of landfast ice along the Mawson Coast, East Antarctica, through ERA-Interim reanalysis data. Four ocean heat-flux (Fw) values (10, 15, 20 and 25 W m−2) were used in sensitivity experiments. The results showed that it is reasonable to simulate landfast ice using the HIGHTSI model, and the simulation of landfast ice thickness matched best well with field measurements when Fw was 20 W m−2. Then, 2-D distributions of landfast ice from 2006 to 2018 were modeled by HIGHTSI with 2-D ERA-Interim reanalysis data in a 0.125° × 0.125° cell grid as external forcing. The results showed that fast ice was thicker along the coast and thinner near open water, and usually reaches its maximal thickness in October, varying from 1.2 to 2.0 m through the study area. There was no statistical trend for the thickness during the study period.

The application efficiency of the Dynamic Rotating Latent-Energy-Storage Envelope (DRLESE) system is highly contingent upon dynamic rotation timings. To gain the optimal rotation timings, six different timings were examined by employing the liquid fraction, thermal storage and release, surface temperature and heat flow. The numerical heat transfer method was employed and verified an experiment. Results indicated that the optimal initial rotation occurs in the forenoon, when the inner surface temperature aligns with the sol-air temperature. Subsequently, achieving optimal secondary rotation is possible in the afternoon when the sol-air temperature equals the liquid temperature of PCM (Phase Change Material). Under these optimized initial and secondary rotation timings, the significant enhancements in thermal performance of the DRLESE system were observed. By optimizing rotation timings, indoor effective heat release can reach up to 3182.9 kJ/Day with an effectiveness percentage exceeding 99.99%, and inner surface heat flow was increased by 5.86%–12.26%.

Solar drying systems are extensively employed in agriculture for the dehydration of crops, fruits, and vegetables due to their ability to generate higher temperatures, thereby resulting in enhanced drying efficiency and product quality. However, the excessive air temperature often encountered in solar drying systems can potentially compromise the quality of agricultural products. Moreover, these systems rely solely on solar energy and thus cannot operate during nighttime. To control the drying temperature and prolonging the operation period are important to improving solar drying systems. The efficient heat storage capacity of phase change material (PCM) makes it a potential choice to store the daytime heat to lower excessive air temperature and then release this heat during nighttime to prolong the drying time. To explore the operation efficiency of PCM, integrating the PCM with the Traditional Solar Dry System (TSDS-PCM) was built with referring to a Traditional Solar Drying System (TSDS). The experimental results demonstrated that the PCM presence had adjusted the air temperatures in the drying chamber by reducing the peak air temperature by 14.8 °C and increasing the average air temperature by 2.1 °C. Meanwhile, the efficient operation time of the solar drying system was prolonged by 3.25 h, which led to a shortening of the drying period by 35.29 %. Although TSDS-PCM has lower collector efficiency than TSDS, it can output the heat energy of 1967.23 kJ at night, which was 1743.45 kJ higher than TSDS.

Due to the high thermal resistance, limited ventilation efficiency, and substantial weight of mascot costumes, staffs wearing mascot costumes in summer are subjected to significant thermal stress, resulting in physiological strain and posing a grave threat to their health. To improve the thermal comfort of staff wearing mascot costumes, this study employed a phase change material (PCM) vest with a phase-change temperature of 26 °C as a cooling measure. The 32 participants were recruited and randomly divided into two groups to analyze the effect of the PCM vest. The results showed that, participants wearing mascot costumes along with an additional PCM vest underneath (MC-PCM) experienced a decrease in oral temperature by 0.06 °C and mean skin temperature by 0.63 °C, compared to those wearing common mascot costumes (CMC). However, there was an increase in body-mass loss relative to initial weight by 0.045 % and heart rate by 5.08 bpm. Wearing the additional PCM vest can improve humidity sensation and thermal sensation, In addition, the average thermal comfort voting score of participants wearing MC-PCM was 0.24 lower than that of participants wearing CMC. It was showed that employing a PCM vest reduced heat stress evidently in staff wearing mascot costumes in high-temperature environments and improved their thermal comfort.

Fifteen patients with uveal melanoma with conbercept injections and 30 patients with uveal melanoma without injections after plaque radiotherapy were observed by optical coherence tomography angiography in this study. Our results indicated that intravitreal conbercept treatment could increase superficial retinal vascular density in early stage.

Jujube is a popular fruit with a long cultivation history and numerous varieties in China. It is necessary to develop an automatic visual identification system of jujube classification in the natural environment. However, practical success in this area is still limited. In this paper, we propose a deep convolutional neural network model for the fine-grained classification of jujube, which exploits a two-stream network to effectively learn discriminative features for each image from both shape level and fine-grained level simultaneously. Specifically, it can also learn the contrastive discrepancies from jujube image pairs. To further facilitate the research, we create a rich jujube image dataset in the natural environment. The dataset consists of more than 1700 images of 20 jujube varieties, and these images have a large degree of variations including angles, background and illumination conditions. The proposed model achieves an average accuracy of 84.16% on this dataset, which outperforms the other four models, including SVM, AlexNet, VGGNet-16 and ResNet-18. The feasibility of this method is demonstrated by the experiment results.

Plasma-activated water (PAW) is a new sanitizer that has received considerable attention for application in food industries. This research aimed to evaluate the effect of PAW on the inactivation of Shewanella putrefaciensand quality attributes of Yellow River carp (Cyprinus carpio) fillets. The carp fillet samples were immersed in sterile deionized water (SDW) or SDW activated by plasma discharge for 120 s (PAW120) for 1.5, 3.0, 4.5, or 6.0 min. After being treated by PAW120 for 6 min, the population of S. putrefacienson carp fillets was significantly (P< 0.05) decreased by 1.03 log CFU/g. Compared with SDW-treated samples, the L* value of PAW120–treated carp fillets was increased and the a* value was decreased after PAW120 treatment, whereas there was no significant (P> 0.05) difference in the b* value. Compared with SDW, PAW120 caused no significant (P> 0.05) changes in sensory properties and texture attributes of carp fillets including hardness, springiness, gumminess, and cohesiveness. However, 6-min PAW120 treatment caused a significant increase in the lipid oxidation level and a decrease in the pH value of the carp fillets. This work provides a basis for the potential application of PAW in the preservation of aquatic products.

The shortage of water has become a serious problem in the development of the steel industry in China. Therefore,it is important for research to be done in environment management, estimating the potential improvement of steel industry. This study propose an improved water footprint model of steel production based on the water footprint network methodology and the life cycle assessment framework. The result shows that the water footprint of steel production is 41.09m3/t, which requires larger amounts of direct blue water. The case of China highlights the relevance of clean production, energy efficiency measures, and grading utilization on reducing the water footprint of steel production.

Based on the Monte Carlo method, the functional function under the normal use limit state given by the specification introduces the concrete tensile strength of each temperature gradient under ultra-low temperature, and the coefficient of change of concrete elastic modulus. By changing the temperature of the member, the thickness of the protective layer, the bending moment effect ratio, the reinforcement size,the concrete grade and the length of the bending beam. Analyze the reliable index of the deflection control of the flexural member under ultra-low temperature to obtain: When the reinforced concrete flexural member is reduced from normal temperature 20°C to-160°C, the reliable deflection index of the component increases non-linearly, reaches the maximum value at-130°C, and then decreases slightly, the concrete strength grade and the thickness of the protective layer under each temperature gradients have the greatest influence on the deflection control of the bending beam under ultra-low temperature, followed by beam length, steel bar size, load effect ratio,which is different from normal temperature.

Integrated partial denitrification-anammox (PD/A) process was considered as a novel strategy for low ammonia wastewater treatment, but the potential roles of the functional bacteria were still unclear. In this study, metagenomic binning analysis coupling with metatranscriptomic analysis was used to reveal the metabolic patterns in a stable operating PD/A system driven by glycerol. The cross-feeding between Bacillusand Candidatus Brocadia, the two dominant functional bacteria that contributed 81.1 % of the total gene expression, was constructed. Bacillusexhibited high nitrite production potential in the assimilatory nitrate reduction pathway instead of partial denitrification. And Bacilluswas the main glycerol utilizer capable of synthesizing intracellular glycogen and polyhydroxybutyrate, which was also the potential branched-chain amino acids and B-vitamins supplier. Candidatus Brocadiaas an anammox bacteria surprisingly expressed the activity of 3-phosphate glycerol (obtained from Bacillus) degradation. This study provided a new understanding of the metabolic patterns and interactions in PD/A system.

Obesity is defined as abnormal or excessive accumulation of body fat, which always causes serious harm to body health. In this work, an anti-obesity strain, Lactiplantibacillus plantarumBHP03 (L. plantarum), was explored. This strain showed good reproductive and acid production abilities. And its simulated intestinal fluid tolerance was better than that of Lacticaseibacillus rhamnosusGG（LGG). The in vitrolipid-lowering indicators (inhibition rate of pancreatic lipase, bile-acid salt binding capacity) of L. plantarumBHP03 were at the same level as LGG, but L. plantarumBHP03 exhibited better ability to clear cholesterol. Further, L. plantarumBHP03 was used to intervene high-fat diet mice by oral administration. The results showed that high (109CFU/day) and low (108CFU/day) doses of L. plantarumBHP03 could alleviate excessive weight gain caused by high-fat diet and improve the symptoms of hepatorenal enlargement and liver fatty lesions. In addition, high dose L. plantarumBHP03 significantly reduced the content of the serum total cholesterol and low-density lipoprotein cholesterol in mice. Moreover, high dose L. plantarumBHP03 enhanced the species richness of intestinal flora in mice fed with high-fat diet, decreasing the abundance of harmful bacteria such as Staphylococcusand increasing the abundance of beneficial bacteria such as Dubosiella, Akkermansia, Bifidobacteriumand Lactobacillus. Therefore, it could be concluded that high dose L. plantarumBHP03 was more effective in preventing obesity and balancing gut microbiota. These findings may provide a theoretical basis for the application of lactobacilli to develop functional foods related to anti-obesity.

The accurate application of the Dynamic Rotating Latent-Energy-Storage Envelope (DRLESE) system necessitates careful consideration of outdoor climate conditions, which significantly impacted latent heat operation of Phase-Change Materials. The effects of climate parameters were explored by considering five convective heat transfer coefficients ranging from 10W/(m·K) to 30W/(m·K), six daily solar radiation intensities ranging from 15.0 MJ/Day to 30.0 MJ/Day, six air average temperatures ranging from -10.0°C to 15°C, and six fluctuation ranges of air temperature ranging from 5°C to 20°C. The thermal performance of the DRLESE system was evaluated by employing the liquid fraction of Phase-Change Materials (PCM), the thermal quantity of DRLESE, and the inner surface heat flow. The numerical results demonstrated that climate parameters have a profound effect on the thermal performance of the DRLESE system. Enhancing convective heat transfer coefficient or lowering outdoor air temperature can significantly attenuate thermal performance by promoting convective thermal dissipation, while increasing daily solar radiation intensity can provide ample solar radiation for absorption by the DRLESE system. With convective heat transfer coefficient increased from 10W/(m2·K) to 20W/(m2·K), the daily solar radiation intensity increased from 15MJ/Day to 30MJ/Day, outdoor air temperature increased from -10°C to 15°C, the fluctuation range increased from 5°C to 20°C, indoor effective thermal release was increased -55.38%, 204.66%, 241.00% and 8.11%, respectively. In addition, employing the transparent covers and selecting the appropriate PCM were recommended to enhance thermal performance of the DRLESE system.

Spin-crossover (SCO) coordination cages are at the forefront of research for their potential in crafting next-generation molecular devices. However, due to the scarcity of SCO hosts and their own limited cavities, the interplay between the SCO host and the multiple guests binding has remained elusive. In this contribution, we present a family of pseudo-octahedral coordination cages (M6L4, M = ZnII, CoII, FeII, and NiII) assembled from a tritopic tridentate ligand Lwith metal ions. The utilization of FeIIion leads to the successful creation of the Fe6L4-type SCO cage. Host–guest studies of these M6L4cages reveal their capacity to encapsulate four adamantine-based guests. Notably, the spin transition temperature T1/2of Fe6L4is dependent on the multiple guests encapsulated. The inclusion of adamantine yields an unprecedented T1/2shift of 54 K, a record shift in guest-mediated SCO coordination cages to date. This drastic shift is ascribed to the synergistic effect of multiple guests coupled with their optimal fit within the host. Through a straightforward thermodynamic cycle, the binding affinities of the high-spin (HS) and low-spin (LS) states are separated from their apparent binding constant. This result indicates that the LS state has a stronger binding affinity for the multiple guests than the HS state. Exploring the SCO thermodynamics of host–guest complexes allows us to examine the optimal fit of multiple guests to the host cavity. This study reveals that the T1/2of the SCO host can be manipulated by the encapsulation of multiple guests, and the SCO cage is an ideal candidate for determining the multiple guest fit.

BRI1-EMS-SUPPRESSOR 1 (BES1) transcription factor is closely associated with the brassinosteroid (BR) signaling pathway and plays an important role in plant growth and development. SLB3is a member of BES1 transcription factor family and its expression was previously shown to increase significantly in tomato seedlings under drought stress. In the present study, we used virus-induced gene silencing (VIGS) technology to downregulate SLB3expression to reveal the function of the SLB3gene under drought stress further. The downregulated expression of SLB3weakened the drought tolerance of the plants appeared earlier wilting and higher accumulation of H2O2and O2−·, decreased superoxide dismutase (SOD) activity, and increased proline (PRO) and malondialdehyde (MDA) contents and peroxidase (POD) activity. Quantitative real-time PCR (qRT-PCR) analysis of BR-related genes revealed that the expression of SlCPD, SlDWARFand BIN2-related genes was significantly upregulated in SLB3-silenced seedlings under drought stress, but that the expression of TCH4-related genes was downregulated. These results showed that silencing the SLB3gene reduced the drought resistance of tomato plants and had an impact on the BR signaling transduction which may be probably responsible for the variation in drought resistance of the tomato plants.

A combined dam structure using different concrete materials offers many practical benefits. There are several real-world cases where large-volume heterogeneous concrete materials have been used together. From the engineering design standpoint, it is crucial to understand the deformation coordination characteristics and mechanical properties of large-volume heterogeneous concrete, which affect dam safety and stability. In this study, a large dam facility was selected for a case study, and various design schemes of the combined dam structure were developed by changing the configurations of material zoning and material types for a given dam shape. Elastoplastic analysis of the dam-foundation-reservoir system for six schemes was carried out under dynamic conditions, in which the concrete damaged plasticity (CDP) model, the Lagrangian finite element formulation, and a surface-to-surface contact model were utilized. To evaluate the mechanical properties of zoning interfaces and coordination characteristics, the vertical distribution of the first principal stress at the longitudinal joint was used as the critical index of deformation coordination control, and the overall deformation and damage characteristics of the dam were also investigated. Through a comparative study of the design schemes, an optimal scheme of the combined dam structure was identified: large-volume roller-compacted concrete (RCC) is recommended for the dam body upstream of the longitudinal joint, and high-volume fly ash conventional concrete (CC) for the dam body downstream of the longitudinal joint. This study provides engineers with a reference basis for combined dam structure design.

Background: Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells. Objective: To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer. Methods: In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds. Results: The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids. Conclusion: Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Chiral metal-organic frameworks (CMOFs) have shown great promises in the applications of asymmetric catalysis with highly enantioselective. Herein, we briefly overview recent processes of MOF-based asymmetric catalysts based on a classification of reaction types. And we mainly focus on the structures and compositions of the active sites in these catalysts and their performances in specific reactions. In addition, some of their important unique features are critically emphasized alongside. Challenges of the future research are discussed also at the end of this review.

We investigate the produced superlattice in electronic systems when coupled with metal nanoparticle arrays. The periodic potential is formed by the induced plasmonic excitations in the metal nanoparticles, with the period changing from several to hundreds of nanometers for either one- or two-dimensional cases. The theoretical method to calculate the periodic potential is presented analytically. Landau–Zener tunneling is studied in the one-dimensional case. The results show that the energy band structures of the superlattice could be designed or modified in a variety of ways, such as changing the structure parameters, combining different kinds of metal nanoparticles, and tuning the strength of the excitation field. These advantages make it a prospective platform to simulate condensed matter physics.

Efficient detection of aberrant glycoproteins in serum is particularly important for biomarker discovery. However, direct quantitation of glycoproteins in serum remains technically challenging because of the extraordinary complexity of the serum proteome. In the current work, we proposed a straightforward and highly efficient strategy by using the nonglycopeptides releasing from the specifically enriched glycoproteins for targeted glycoprotein quantification. With this so-called nonglycopeptide-based mass spectrometry (NGP-MS) strategy, a powerful and nondiscriminatory pipeline for hepatocellular carcinoma (HCC) glycoprotein biomarker discovery, verification, and validation has been developed. First, a data set of 234 NGPs was strictly established for multiple-reaction monitoring (MRM) quantification in serum. Second, the NGPs enriched from 20 HCC serum mixtures and 20 normal serum mixtures were labeled with mTRAQ reagents (Δ0 and Δ8, respectively) to find the differentially expressed glycoproteins in HCC. A total of 97 glycoprotein candidates were preliminarily screened and submitted for absolute quantitation with NGP-based stable-isotope-labeled (SID)-MRM in the individual samples of 38 HCC serum and 24 normal controls. Finally, 21 glycoproteins were absolutely quantified with high quality. The diagnostic sensitivity results showed that three glycoproteins, β-2-glycoprotein 1 (APOH), α-1-acid glycoprotein 2 (ORM2), and complement C3 (C3), could be used for the discrimination between HCC patients and healthy people. A novel glycoprotein biomarker panel [APOH, ORM2, C3, and α-fetoprotein (AFP)] has proven to outperform AFP, the known HCC serum biomarker, alone, in this study. We believe that this strategy and the panel of glycoproteins might hold great clinical value for HCC detection in the future.