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In order to solve the problems of low power transfer efficiency (PTE) and limited distance in magnetic coupling resonant wireless power transfer (MCR-WPT) technology. In this paper, based on the magnetic field control ability of metamaterials (MMs), the way to improve the performance of MCR-WPT systems is studied. First, the influence of MMs on the coupling of MCR-WPT system is theoretically analyzed by establishing an equivalent circuit model. Through a series of simulations and experiments, the relationships between the PTE and the array and placement of the MM slab are investigated. The results demonstrate that when one MM slab is placed in the middle, near the Tx coil or the Rx coil, the optimal PTE can be obtained by inserting one slab with $6 \times 6$ , $1 \times 1$ , and $6 \times 6$ arrays, respectively. Moreover, the systems with multilayer MM slabs are also studied. The measured PTEs on one, two, and three layers of MM slabs can increase by 20.6%, 29.3%, and 22.6%, respectively. The efficiency improvement capability of one MM slab is better than three slabs but worse than two slabs. This paper discusses the application of MMs in MCR-WPT systems, which has a certain reference significance.

In this study, representative lignin samples differing in hydroxyl content, molecular weight, linkage composition and lignin units ratio were prepared from eucalyptus wood by different isolation means and modification means: milled wood lignin (MWL), kraft lignin classified at different pH (KL), lignocresol (LC), lignoresorcinol (LR), lignopyrogallol (LP) as well as lignocresol secondary functional switched samples (LC 2ndderiv). The structure of various lignins was characterized in detail by Fourier transform infrared (FT-IR) spectroscopy, gel permeation chromatography (GPC) and nuclear magnetic resonance (NMR). In order to eliminate the interference of other factors, bovine serum albumin (BSA) with relatively simple protein structure and different kinds of lignin and their derivatives were selected as the research objects. The adsorption/desorption behavior and mechanism between lignin samples and BSA were studied by quartz crystal microbalance with dissipation monitoring (QCM-D). The results showed that the content of phenolic hydroxyl was the main factor affecting the interaction of lignin and BSA, but in addition, the molecular size, linkages composition and the types of lignin units also had important effects on its adsorption behavior. The research will provide theoretical guidance and all-round reliable basic data for the interaction of lignin on lignocellulose hydrolysis enzymes in industrial application.

Self-supervised graph-level representation learning has recently received considerable attention. Given varied input distributions, jointly learning graphs’ unique and common features is vital to downstream tasks. Inspired by graph contrastive learning (GCL), which targets maximizing the agreement between graph representations from different views, we propose an Ada ptive self-supervised framework, Ada-MIP, considering both M utual I nformation between views (unique features) and inter-graph P roximity (common features). Specifically, Ada-MIP learns graphs’ unique information through a learnable and probably injective augmenter, which can acquire more adaptive views compared to the augmentation strategies applied by existing GCL methods; to learn graphs’ common information, we employ graph kernels to calculate graphs’ proximity and learn graph representations among which the precomputed proximity is preserved. By sharing a global encoder, graphs’ unique and common information can be well integrated into the graph representations learned by Ada-MIP. Ada-MIP is also extendable to semi-supervised scenarios, with our experiments confirming its superior performance in both unsupervised and semi-supervised tasks.

We aimed to assess the cost-effectiveness and health benefits of 23-valent pneumococcal polysaccharide vaccine (PPSV23), 13-valent pneumococcal conjunctive vaccine (PCV13), and PCV13-PPSV23 sequential vaccination strategies in preventing pneumonia and other pneumococcal diseases among the senior citizens (≥ 65 years) in China. The cost-effectiveness of pneumococcal vaccines compared to no vaccination was estimated using a decision-tree Markov model from a societal perspective. Parameters including epidemiological data, vaccine efficacy and cost data were obtained from previous studies. Cases and deaths averted, quality-adjusted life years (QALYs) gained, and incremental cost-effectiveness ratios (ICERs) were presented as outcomes. Sensitivity analyses were performed to explore the uncertainty in the model. In the base-case analysis, compared with no pneumococcal vaccination, the ICERs of PPSV23, PCV13 and PCV13-PPSV23 are US$10,776.7/QALY, $9,193.2/QALY, and $15,080.0/QALY, respectively. PCV13 is the most cost-effective strategy and the only cost-effective strategy based on a threshold of the one-time national GDP per capita, whereas PPSV23 vaccine strategy is provided with the lowest cost, and PCV13-PPSV23 demonstrates the greatest impact on pneumococcal disease burden. Sensitivity analyses reveal that the results are greatly influenced by serotype coverage, vaccine efficacy, CAP incidence and vaccine prices. The PCV13 vaccination for Chinese seniors is more cost-effective than PPSV23 vaccination and PCV13-PPSV23 sequential vaccination.

A model of the influence of cultural differences on English learning from the perspective of the linguistic and cultural community based on data feature decomposition and fusion clustering is proposed. The new knowledge and the old knowledge are connected through the optimal control of the learning model. By decomposing relevant data through data features and fusion clustering algorithm, the impact of cultural differences on English learning can be modeled and analyzed from the perspective of language and cultural community. The simulation results show that the data fusion performance of the impact model of this method on English learning is good, and the quantitative analysis results are accurate and reliable. According to the results of visual data, targeted interventions were carried out for teachers, learners, and teaching managers.

Based on the rapid development of 5G networks, the wider the bandwidth, the more limited the coverage. The problem of site selection is becoming more and more prominent. According to the coverage of the existing 5G network, this problem gives the weak coverage area of the existing network. It requires selecting a certain number of locations so that after the new base stations are built at these points, the weak coverage of the existing network can be solved. Network coverage issues in the coverage area. This paper establishes a related multi-objective programming model, and a heuristic algorithm is used to solve it. We first cleaned the data, judged the Euclidean distance between the existing base station and the weak coverage point according to the required minimum threshold of 10, and eliminated the points that did not meet the requirements. Due to the vast amount of data, the business volume was huge. Small dots are blurred. Set the coverage area of ​​the base station as a sector. By adjusting the relationship between the calculation coverage radius and the angle, the angle between the three main directions is added as a constraint, and the planning is carried out again based on problem one location selection. The angle change is used as the decision variable, and the greedy algorithm is used to solve the problem. Moreover finally, the coordinates of each base station and the optimal sector angle that satisfy more than 90% of the traffic volume are: (232.6573, -111.668, -29.7508). Using the k-means clustering algorithm, the weak coverage points are clustered according to the requirements of less than 20, and the frequency and percentage of clusters are analyzed. The time complexity is calculated by testing the DVI index and the number of iterations. On this basis, Optimize our model. Finally, the evaluation and promotion of our model are carried out, and the detailed distribution map and clustering effect map of various base stations are given in the appendix.

The study aims to solve the problem that the traditional scheduling optimization model does not apply to the multimicrogrid systems in the 5th generation mobile networks (5G). First, the response characteristics of the 5G base station energy storage demand are analyzed. Second, a microgrid hybrid power supply system is proposed. Third, a multimicrogrid demand response hierarchical optimal scheduling model based on the game is constructed, and the game interaction mechanism between various stakeholders in the demand response is studied. Finally, the strategy of each microgrid in the IEEE-9 bus system in the game bidding is simulated by a genetic algorithm (GA), and the simulation experiment is carried out by Matlab. The results show that (1) after the optimization of the multimicrogrid system, the overall load of the microgrid is reduced, the independent energy optimization is carried out according to the energy access of each microgrid, and renewable energy is fully used. (2) Based on the game-based time-sharing bidding strategy, the system gives priority to the low-cost microgrid for power supply and saves the cost. (3) The load transfer capacity of the three microgrids obtained by the model are 132.7 kW, 47.1 kW, and 46.4 kW, which is within the actual load range of each microgrid; their electricity sales are 665.8 kWh, 2053.3 kWh, and 1668.2 kWh; and their income is 132.45 CNY, 411.72 CNY, and 354.63 CNY, respectively. This shows that the game equilibrium index makes the power sales of each microgrid proportional to its income, which ensures the fair and effective scheduling in the bidding process. The study provides technical support for the construction of a 5G base station and the active demand response scheduling optimization of distribution networks.

Reported evidence has increasingly indicated that exposure to phthalates can cause adverse pregnancy outcomes. However, phthalate exposure levels among pregnant women remains unclear. We aimed to evaluate the concentrations and predictors of phthalate metabolites in urine samples of the ongoing Zunyi cohort of pregnant women from Southwest China. The urine samples were collected from 1003 pregnant women during their third trimester of pregnancy. The concentrations of nine phthalate metabolites in urine samples were then determined. Data on socio-demographic profiles of the participants, lifestyle during pregnancy, parity, and sampling season were collected using questionnaires. The detectable rate of phthalate metabolites ranged from 76 to 100%. On average, mono-butyl phthalate exhibited the highest median concentration (62.45 μg/L), while mono-benzyl phthalate exhibited the lowest median concentration (0.04 μg/L). Urine concentrations of phthalate metabolites were significantly higher in older, multiparous, higher body mass index, higher income, and passive smoking during pregnancy participants. The levels of low-molecular-weight phthalate metabolites were highest during the summer. The findings indicate the health of pregnant women and fetuses in Zunyi may be generally harmed by the high exposure of phthalate metabolites, especially by mono-n-butyl phthalate. In addition, phthalate metabolites present a demographic and seasonal differential distribution among the study population. Targeted measures to reduce phthalate exposure for high-risk pregnant women and during high-exposure seasons may have potential benefits for maternal and fetal health protection.

The overwhelming parameters and computation consumption of deep neural networks limit their applicability to a single computing node with poor computing power, such as edge and mobile devices. Most previous works leverage model pruning and compression strategies to reduce DNN parameters for resource-constrained devices. However, most model compression methods may suffer from accuracy loss. Recently, we find that combine many weak computing nodes as a distributed system to run large and sophisticated DNN models is a promising solution for the issue. However, it is essential for the distributed system to design distributed DNN models and inference schemes, one of the great challenges of distributed system is how to design an efficient distributed DNN model for data parallelism and model parallelism, and communication overhead is also another critical performance bottleneck for distributed DNN model. Therefore, in this article, we propose DFSNet framework (Dividing-Fuse neural Network with Searching Strategy) for distributed DNN architecture. Firstly, the DFSNet framework includes a joint ”dividing-fusing” method to convert regular DNN models into distributed models that are friendly for distributed systems. This method divides the conventional DNN model in the channel dimension, and sets a few special layers to fuse feature-map information from different channel groups for accuracy improvement. Since the fusion layers are sparse in the network, they do not increase too much extra inference time and communication overhead on the distributed nodes, but they can maintain the accuracy of distributed neural networks significantly. Secondly, considering the architecture of distributed computing nodes, we propose a parallel fusion topology to improve the utilization of different computing nodes. Lastly, the popular weight-sharing neural architecture search (NAS) technique is leveraged to search the position of fusion layers in the distributed DNN model for high accuracy and finally generate an efficient distributed DNN model. Compared with the original network, our converted distributed DNN achieves better performance (e.g. 1.88% precision boosting in ResNet56 on CIFAR-100 dataset, and 1.25% precision improving in MobileNetV2 on ImageNet dataset). In addition, most layers of DNN have been divided into different distributed nodes on channel dimension, which is particularly suitable for distributed DNN architecture with very low communication overhead.

In this study, the effect of inlet concentration of pollutants, empty bed residence time (EBRT) and liquid recycling velocity on the performance of the BTF was explored. The results showed the best operating characteristics of the biotrickling filter for this study were at an inlet concentration of 500 mg/m3�Cthe EBRT of 300s and liquid recycling velocity of 25.46 m h-1. At this condition, the total removal efficiency of pollutants can reach more than 90%. Among them, the removal efficiency of toluene and ethylbenzene was above 95%, and the xylene (m-xylene, o-xylene) could reach more than 70%, corresponding to elimination capacities (ECs) of 3.02 �} 0.33 g m-3 h-1, 1.81 �} 0.2 g m-3 h-1, 0.60 �} 0.07 g m-3 h-1. Through the fatty acid identification system and 16S rDNA identification, it could be found that the four dominant bacteria (Bacillus cereus, Bacillus subtilis, Acinetobacter calcoaceticus, Belem Bacillus) had a significant effect on the removal of acrylic paint thinner. According to the detection of organic compounds by gas chromatography and the corresponding enzymes produced by four dominant bacteria, the pathways for microorganisms to degrade pollutants could be inferred. The absorption-biofilm theory and the adsorption-biofilm theory were used to simulate and verify the degradation process of exhaust gas at different concentrations respectively.

Anaerobic digestion (AD) is one of the most widely used processes to stabilize waste sewage sludge and produce biogas as renewable energy. This study investigated the effects of Fe3O4 NPs at different concentrations (0,100 and 300 mg/L) on AD. 100mg/L Fe3O4 NPs could increase gas production by 21.9% and reach the highest methane content of 66.75%. In addition, 100 mg/L Fe3O4 kept pH at an appropriate level. Scanning electron microscope (SEM) was used to study the changes in surface morphology in sludge. The dynamics of cumulative biogas production and daily biogas production were studied by using the Logistic model and modified Gompertz model respectively. Other indices also had changed in the whole process, which in the end lead to the variation of biogas production. These results suggested that Fe3O4 NPs could increase microbial activity and relevant enzyme activities, caused changes and transitions between different phases in the system.

A green method for lignin quantitation based on the completely dissolved solution of lignocellulosic biomass in LiCl/DMSO and UV spectrophotometry was developed. The method is accurate, sensitive, mild, and does not use hazardous reagents.

In this study, CuO quantum dots were encapsulated in a MIL-125(Ti) MOF via a simple oxidation method.

Microsatellites have been widely applied in the fields of communication, remote sensing, navigation and science exploration due to its characteristics of low cost, flexible launch mode and short development period. However, conventional solid-propellant have difficulties in starting and interrupting combustion because combustion is autonomously sustained after ignition. Herein, we proposed a new type of solid-propellant named laser-controlled solid propellant, which is sensitive to laser irradiation and can be started or interrupted by switching on/off the continuous wave laser. To demonstrate the feasibility and investigate the controllable combustion behaviors under different laser on/off conditions, the combustion parameters including burning rate, ignition delay time and platform pressure were tested using pressure sensor, high-speed camera and thermographic camera. The results showed that the increase of laser-on or laser-off duration both will lead to the decrease of propellant combustion performance during re-ignition and re-combustion process. This is mainly attributed to the laser attenuation caused by the accumulation of combustion residue and the change of chamber ambient temperature. Simultaneously, the multiple ignition tests revealed that the increased chamber ambient temperature after combustion can make up for the energy loss of laser attenuation and expansion of chamber cavity. However, the laser-controlled combustion performance of solid propellant displayed a decrease trend with the addition of ignition times. Nevertheless, the results still exhibited good laser-controlled agility of laser-controlled solid propellant and manifested its inspiring potential in many aspects of space missions.

A novel procedure for the synthesis of dibenzo[b,d]pyran-6-ones via Rh(iii)-catalyzed C–H activation and C–C bond cleavage is described.

Video Question Answering (VideoQA), which explores spatial-temporal visual information of videos given a linguistic query, has received unprecedented attention over recent years. One of the main challenges lies in locating relevant visual and linguistic information, and therefore various attention-based approaches are proposed. Despite the impressive progress, two aspects are not fully explored by current methods to get proper attention. Firstly, prior knowledge, which in the human cognitive process plays an important role in assisting the reasoning process of VideoQA, is not fully utilized. Secondly, structured visual information (e.g., object) instead of the raw video is underestimated. To address the above two issues, we propose a Prior Knowledge and Object-sensitive Learning (PKOL) by exploring the effect of prior knowledge and learning object-sensitive representations to boost the VideoQA task. Specifically, we first propose a Prior Knowledge Exploring (PKE) module that aims to acquire and integrate prior knowledge into a question feature for feature enriching, where an information retriever is constructed to retrieve related sentences as prior knowledge from the massive corpus. In addition, we propose an Object-sensitive Representation Learning (ORL) module to generate object-sensitive features by interacting object-level features with frame and clip-level features. Our proposed PKOL achieves consistent improvements on three competitive benchmarks (i.e., MSVD-QA, MSRVTT-QA, and TGIF-QA) and gains state-of-the-art performance. The source code is available at https://github.com/zchoi/PKOL.

The effect of hydrothermal pretreatment (HTP) of dewatered sewage sludge (DSS) under optimized hydrothermal conditions and the fate of carbon and phosphorus (P) during anaerobic digestion (AD) of hydrochar and process water were studied. The results showed that the methane yield reached 241 mL CH4/g COD when the hydrothermal conditions were 200°C-2 h-10% (A4), which was 78.28% higher than that without hydrothermal pretreatment (A0) and 29.62% higher than that of unoptimized A1 (140°C-1 h-5%). Protein, polysaccharides, and volatile fatty acids (VFAs) were the main hydrothermal products of DSS, and polysaccharides were first absorbed and utilized by microorganisms. By 3D-EEM analysis, tyrosine, tryptophan proteins, and fulvic acids decreased after HTP, but the content of humic acid-like substances increased, and this phenomenon was more obvious after AD. A lag phase in protein degradation occurred during AD while polysaccharides are degraded first. The solid-organic P is converted into liquid-P in the hydrothermal process, and non-apatite inorganic P is converted to organic P during AD. A lower methane production rate occurred in the early AD, suggesting that the inoculum needed some time to adapt to the substrate. Microbial analysis showed that the composition of the microbial community of anaerobic degradation was different due to the various organic components.

Iron-based catalysts are regarded as promising candidates for the ammonia selective catalytic reduction reaction (NH3-SCR) which show good catalytic activity at medium and high temperatures, whereas SAPO-34 molecular sieves have a micro-pore structure and are ideal catalyst carriers. In this paper, four FeOx/SAPO-34 molecular sieve catalysts with different iron contents (Fe = 1%, 2%, 3%, 4%) were prepared using an impregnation method. The effect of iron content on the surface properties and catalytic activity was investigated by a series of characterization techniques including XRD, SEM, BET, XPS, H2-TPR and NH3-TPD. Iron species in the FeOx/SAPO-34 catalysts exist in the form of isolated iron ions or well-dispersed small crystals and iron oxide species clusters. With the addition of iron content, the integrity of CHA (chabazite) zeolite structure remained, but the crystallinity was affected. The FeOx/SAPO-34 catalyst with 3% Fe loading showed a relatively flat surface with no large-diameter particles and strong oxidation-reduction ability. Meanwhile, more acidic sites are exposed, which accelerated the process of catalytic reaction. Thus, the FeOx/SAPO-34 catalyst with 3% Fe showed the best NO conversion performance among the four catalysts prepared and maintained more than 90% NO conversion efficiency in a wide temperature range from 310 °C to 450 °C.