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In this paper, we study the nonlinear matrix equation (NME) X+∑i=1mAi*X−1Ai=Q. We transform this equation into an equivalent zero-point equation, then we use Newton’s iteration method to solve the equivalent equation. Under some mild conditions, we obtain the domain of approximation solutions and prove that the sequence of approximation solutions generated by Newton’s iteration method converges to the unique solution of this equation. In addition, the error estimation of the approximation solution is given. Finally, the comparison of two well-known approaches with Newton’s iteration method by some numerical examples demonstrates the superiority of Newton’s iteration method in the convergence speed.

Background/Objectives: As a hyperaccumulator of selenium (Se), Cardamine violifolia (Cv) and its peptide extract could ameliorate the negative effects of a high-fat diet (HFD). However, the effects of the coaccumulation of cadmium (Cd) in Se-enriched Cv (Cv2) and the potential confounding effect on the roles of enriched Se remain unknown. We aimed to investigate whether Cv2 could alleviate HFD-induced lipid disorder and liver damage. Methods: Three groups of 31-week-old female mice were fed for 41 weeks (n = 10–12) with a control Cv-supplemented diet (Cv1D, 0.15 mg Se/kg, 30 µg Cd/kg, and 10% fat calories), a control Cv-supplemented HFD (Cv1HFD, 45% fat calories), and a Cv2-supplemented HFD (Cv2HFD, 1.5 mg Se/kg, 0.29 mg Cd/kg, and 45% fat calories). Liver and serum were collected to determine the element concentrations, markers of liver injury and lipid disorder, and mRNA and/or protein expression of lipid metabolism factors, heavy metal detoxification factors, and selenoproteins. Results: Both Cv1HFD and Cv2HFD induced obesity, and Cv2HFD downregulated Selenoi and upregulated Dio3 compared with Cv1D. When comparing Cv2HFD against Cv1HFD, Cv2 increased the liver Se and Cd, the protein abundance of Selenoh, and the mRNA abundance of 10 selenoproteins; reduced the serum TG, TC, and AST; reduced the liver TG, lipid droplets, malondialdehyde, and mRNA abundance of Mtf1 and Mt2; and differentially regulated the mRNA levels of lipid metabolism factors. Conclusions: Cv2 alleviated HFD-induced lipid dysregulation and liver damage, which was probably associated with its unique Se speciation. However, further research is needed to explore the interaction of plant-coenriched Se and Cd and its effects on health.

In recent years, the increasing frequency of extreme weather events has exacerbated the severity of geological disasters. Therefore, it is important to understand the mechanisms of geological disasters under extreme rainfall conditions. From June to October 2021, Baoji City, Shanxi Province, China, experienced some extreme and continuous heavy rainfalls, which triggered more than 30 geological disasters. Those geo-disasters threatened the lives of 831 people and the safety of 195 houses. The field investigations found that most of these geological disasters were devastating collapses that occurred in the loess layer, primarily due to the cave dwelling construction. The shear strength, montmorillonite content, disintegration degree, and plasticity index of two typical loesses, namely the Sanmen Formation stiff clay and the Hipparion red clay, were analyzed, and their water sensitivities were evaluated. The failure mechanisms of the landslides, ground fissures, and collapses were analyzed and most of them were controlled by the cave dwelling construction and the strong water sensitivity of the loess. This study provides data for understanding shallow geological disasters induced by extreme rainfall in the loess area, which are more threatening than large geological disasters. We proposed an intensity–duration (I–D) rainfall threshold as I = 90 D−0.92, which relates the rainfall intensity (I) to the rainfall event duration (D). The empirical threshold provides some useful information for the early warning of collapses or landslides in similar geological settings in the loess area.

The movement process of debris flow in the complex roads system is important for risk evaluation and emergency rescue. This paper presents an in-depth study of the diversion effect of debris flow in cross channels, a common branching structure in both natural and engineered environments, especially in the field of urban debris flow prevention. A mathematical model is established based on the conservation of mass, momentum, and energy, and a solid–liquid two-phase motion equation for debris flow is derived from two-phase flow theory. A numerical solution method, combining the finite difference method and finite volume method, is employed to discretize and solve the equation. The model’s validity and effectiveness are confirmed through a numerical simulation of a typical engineering case and comparison with existing experimental data or theoretical results. This study reveals that debris flow at cross channels exhibits a diversion phenomenon, with some debris flow continuing downstream along the main channel and some diverting into the branch channel. The diversion rate, defined as the ratio of outlet flow to inlet flow of the branch channel, indicates the magnitude of this effect. This research shows that the solid–liquid ratio, inflow, width ratio, height ratio, and angle of the cross channel significantly impact the diversion effect. A series of numerical simulations are conducted by altering these parameters as well as the physical properties of debris flow and boundary conditions. These simulations analyze changes in flow rate, velocity, pressure, and other parameters of debris flow at cross channels, providing insights into the factors and mechanisms influencing the diversion effect. This research offers a robust instrument for comprehending and forecasting the dynamics of urban debris flows. It contributes significantly to mitigating the effects of debris flows on city infrastructure and enhancing the safety of city dwellers.

In this study, the roles of kiwifruit soluble/insoluble dietary fiber (SDF/IDF, respectively) in the pasting characteristics and in vitro digestibility of wheat starch were explored. According to RVA and rheological tests, the IDF enhanced the wheat starch viscosity, decreased the gelatinization degree of the starch granules, and exacerbated starch retrogradation. The addition of SDF in high quantities could reduce the starch gelatinization level, lower the system viscosity, and exacerbate starch retrogradation. Through determining the leached amylose content and conducing scanning electron microscopy, the IDF and SDF added in high quantities was combined with the leached amylose wrapped around the starch granules, which reduced the leached amylose content and decreased the gelatinization degree of the starch granules. The Fourier transform infrared results showed that the addition of both the IDF and SDF resulted in an enhancement in hydrogen bonding formed by the hydroxyl groups of the system. The in vitro digestion results strongly suggested that both the IDF and SDF reduced the wheat starch digestibility. The above findings are instructive for the application of both IDF and SDF in starchy functional foods.

The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a low-cost, light-weight, small-volume and low-computation underwater integrated navigation system based on MEMS IMU/DVL/USBL. First, according to the motion formula of AUV, a five-dimensional state equation of the system was established, whose dimension was far less than that of the traditional. Second, the main source of error was considered. As the velocity observation value of the system, the velocity measured by DVL eliminated the scale error and lever arm error. As the position observation value of the system, the position measured by USBL eliminated the lever arm error. Third, to solve the issue of inconsistent observation frequencies between DVL and USBL, a sequential filter was proposed to update the extended Kalman filter. Finally, through selecting the sensor equipment and conducting two lake experiments with total voyages of 5.02 km and 3.2 km, respectively, the correctness and practicality of the system were confirmed by the results. By comparing the output of the integrated navigation system and the data of RTK GPS, the average position error was 4.12 m, the maximum position error was 8.53 m, the average velocity error was 0.027 m/s and the average yaw error was 1.41°, whose precision is as high as that of an optical fiber gyroscope and Doppler Velocity Log integrated navigation system, but the price is less than half of that. The experimental results show that the proposed underwater integrated navigation system could realize the high-precision and long-term navigation of AUV in the designated area, which had great potential for both military and civilian applications.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring.

Ghost imaging is a novel imaging technique that utilizes the intensity correlation property of an optical field to retrieve information of the scene being measured. Due to the advantages of simple structure, high detection efficiency, etc., ghost imaging exhibits broad application prospects in the fields of space remote sensing, optical encryption transmission, medical imaging, and so on. At present, ghost imaging is gradually developing toward practicality, in which ghost imaging of moving targets is becoming a much-needed breakthrough link. At this stage, we can improve the imaging speed and improve the imaging quality to seek a more optimized ghost imaging scheme for moving targets. Based on the principle of moving target ghost imaging, this review summarizes and compares the existing methods for ghost imaging of moving targets. It also discusses the research direction and the technical challenges at the current stage to provide references for further promotion of the instantiation of ghost imaging applications.

Metal matrix composites with near-zero thermal expansion (NZTE) have gained significant popularity in high-precision industries due to their excellent thermal stability and mechanical properties. The incorporation of Mn3Zn0.8Sn0.2N, which possesses outstanding negative thermal expansion properties, effectively suppressed the thermal expansion of titanium. Highly dense Mn3Zn0.8Sn0.2N/Ti composites were obtained by adjusting the fabrication temperature. Both composites fabricated at 650 °C and 700 °C exhibited NZTE. Furthermore, finite element analysis was employed to investigate the effects of thermal stress within the composites on their thermal expansion performance.

Due to the structural stability and high adjustability of perovskite, lead-free perovskite ceramics are widely thought to be one of the most promising functional materials. In this work, an abnormal negative thermal expansion behavior with a linear expansion coefficient of −54.95 ppm/K is achieved in the (1-x)NaNbO3-xCaZrO3 system by driving the antiferroelectric phase transition from orthorhombic phase and tetragonal phase. The NTE mechanism is verified by temperature-dependent high-energy synchrotron X-ray diffraction, dielectric spectra, and Raman scattering spectroscopy. The relationship between the antiferroelectric phase transition and negative thermal expansion behavior is systematically revealed by analyzing the evolution of the phase structure with temperature. This novel negative thermal expansion feature caused by the antiferroelectric phase transition provides new guidance for designing more negative thermal expansion materials.

Many researchers have introduced blockchain into the Internet of Vehicles (IoV) to support trading or other authentication applications between vehicles. However, the traditional blockchain cannot well support the query of transactions that occur in a specified area which is important for vehicle users since they are bound to the geolocations. Therefore, the querying efficiency of the geolocation attribute of transactions is vital for blockchain-based applications. Existing work does not well handle the geolocation of vehicles in the blockchain, and thus the querying efficiency is questionable. In this paper, we design a rapid query method of regional transactions in blockchain for IoV, including data structures and query algorithms. The main idea is to utilize the Geohash code to represent the area and serve as the key for transaction indexing and querying, and the geolocation is marked as one of the attributes of transactions in the blockchain. To further verify and evaluate the proposed design, on the basis of the implementation of Ethereum, which is a well-known blockchain, the results show that the proposed design achieves significantly better-querying speed than Ethereum.

Selenoprotein P (SELENOP) is an extracellular antioxidant, selenium transporter, and hepatokine interfering with glucose and lipid metabolism. To study the association between the circulating SELENOP concentration and glucose and lipid metabolic diseases (GLMDs), including gestational diabetes (GD), metabolic syndrome (MetS), non-alcoholic fatty liver disease, obesity, and type 2 diabetes, as well as the individual markers, a meta-analysis was conducted by searching multiple databases from their establishment through March 2022 and including 27 articles published between October 2010 and May 2021, involving 4033 participants. Participants with GLMDs had higher levels of SELENOP than those without GLMDs (standardized mean difference = 0.84, 95% CI: 0.16 to 1.51), and the SELENOP levels were positively correlated with the markers of GLMDs (pooled effect size = 0.09, 95% CI: 0.02 to 0.15). Subgroup analyses showed that the SELENOP concentrations were higher in women with GD and lower in individuals with MetS than their counterparts, respectively. Moreover, SELENOP was positively correlated with low-density lipoprotein cholesterol, but not with the other markers of GLMDs. Thus, the heterogenicity derived from diseases or disease markers should be carefully considered while interpreting the overall positive association between SELENOP and GLMDs. Studies with a larger sample size and advanced design are warranted to confirm these findings.

In order to improve the anti-collision capacity of existing reinforced concrete (RC) columns under vehicle impact load, an experimental study on the anti-collision performance of RC columns strengthened with micro-expansive concrete filled steel tube was carried out. By combining an experiment and finite element calculation, the influence of steel tube strength, micro-expansive concrete strength and steel tube wall thickness on the anti-collision performance of strengthened RC columns were studied. The results showed that the peak displacement of an SM-RC column is 25.9% lower than that of an RC column, due to the increased stiffness, the peak impact force increased by 138.2%. The micro-expansive concrete filled steel tube reinforcement method can significantly improve the anti-collision performance of RC columns and reduce the lateral deformation. Increasing the strength grade of steel tube and thickness of steel tube can greatly improve the impact resistance of strengthened RC columns and reduce the lateral deflection deformation of short columns, compared with steel tube strength Q235, the displacement of Q690 is reduced 47.4%. the displacement of the 8-mm thick steel tube is reduced by 48.1% compared with the 4-mm one. while the strength grade of micro-expansive concrete has little effect on the impact resistance and lateral deflection deformation of strengthened RC columns.

High-performance extruded aluminum alloys with complex textures suffer significant dimension variation under environmental temperature fluctuations, dramatically decreasing the precision of navigation systems. This research mainly focuses on the effect of the texture of extruded pure aluminum on its dimensional stability after various annealing processes. The result reveals that a significant increment in the area fraction of recrystallized grains with orientation and a decrement in the area fraction of grains with orientation were found with increasing annealing temperature. Moreover, with the annealing temperature increasing from 150 °C to 400 °C, the residual plastic strain after twelve thermal cycles with a temperature range of 120 °C was changed from −1.6 × 10−5 to −4.5 × 10−5. The large amount of equiaxed grains with orientation was formed in the microstructure of the extruded pure aluminum and the average grain size was decreased during thermal cycling. The area fraction of grain with crystallographic orientation of the sample annealed at 400 °C after thermal cycling was 30.9% higher than annealed at 350 °C (23.7%) or at 150 °C (18.7%). It is attributed to the increase in the proportion of recrystallization grains with direction as the annealing temperature increases, provided more nucleation sites for the formation of fine equiaxed grains with orientation. The main orientation of the texture was rotated from parallel to to parallel to after thermal cycling. The change in the orientation of grains contributed to a change in interplanar spacing, which explains the change in the dimension along the extrusion direction during thermal cycling.

In this paper, six-layer AlN/Al gradient composites were prepared by a spark plasma sintering process to study the influences of sintering temperature and holding time on the microstructure and mechanical properties. The well-bonded interface enables the composite to exhibit excellent thermal and mechanical properties. The hardness and thermal expansion properties of the composite exhibit a gradient property. The hardness increased with the volume fraction of AlN while the CTE decreased as the volume fraction of AlN. The thermal expansion reaches the lowest value of 13–14 ppm/K, and the hardness reaches the maximum value of 1.25 GPa, when the target volume fraction of AlN is 45%. The simulation results show that this gradient material can effectively reduce the thermal stress caused by the mismatch of the thermal expansion coefficient as a transmitter and receiver (T/R) module. This paper attempts to provide experimental support for the preparation of gradient Al matrix composites.

A greater understanding of the dynamic processes inside the stack is urgently needed to optimize the PEMFC (proton exchange membrane fuel cell). In this study, we examined the gas, water and electrochemical processes inside the stack, studied the physical dynamics of system accessories such as gas supplement, flow and pressure-regulating devices, then used Simulink to build a mathematical model of a complete PEMFC system; a segmented testing platform was built to test the spatial distribution of RH (relative humidity) and pressure, which was used to verify the simulation model; based on this model, the complicated phenomena occurring inside the stack during fluctuating operating states were calculated. Our findings showed that the pressure in the gas channel and exhaust manifolds decreased when the external load increased, changing sharply at the moment of load change. The transient pressure difference between the cathode and anode sides (several kPa) had a huge impact on the MEA (membrane electrode assembly); when the load current increased, RH in cathode and cathode channel increased gradually, and the increasing rate of anode side was bigger than that in cathode side. The influence of variance magnitude and change interval of external load were also studied based on the model.

Selenium (Se) homeostasis is tightly related to carbohydrate and lipid metabolism, but its possible roles in obesity development and in adipocyte metabolism are unclear. The objective of the present study is to review the current data on Se status in obesity and to discuss the interference between Se and selenoprotein metabolism in adipocyte physiology and obesity pathogenesis. The overview and meta-analysis of the studies on blood Se and selenoprotein P (SELENOP) levels, as well as glutathione peroxidase (GPX) activity in obese subjects, have yielded heterogenous and even conflicting results. Laboratory studies demonstrate that Se may modulate preadipocyte proliferation and adipogenic differentiation, and also interfere with insulin signaling, and regulate lipolysis. Knockout models have demonstrated that the selenoprotein machinery, including endoplasmic reticulum-resident selenoproteins together with GPXs and thioredoxin reductases (TXNRDs), are tightly related to adipocyte development and functioning. In conclusion, Se and selenoproteins appear to play an essential role in adipose tissue physiology, although human data are inconsistent. Taken together, these findings do not support the utility of Se supplementation to prevent or alleviate obesity in humans. Further human and laboratory studies are required to elucidate associations between Se metabolism and obesity.

Physical model testing can replicate the deformation process of landslide stabilizing piles and analyze the pile-landslide interaction with multiple field information, thoroughly demonstrating its deformation and failure mechanism. In this paper, an integrated monitoring system was introduced. The instrumentation used included soil pressure cells, thermal infrared (TIR) imagery, 3D laser scanner, and digital photography. In order to precisely perform field information analysis, an index was proposed to analyze thermal infrared temperature captured by infrared thermography; the qualitative relationship among stress state and deformation as well as thermal infrared temperature is analyzed. The results indicate that the integrated monitoring system is expected to be useful for characterizing the deformation process of a pile-reinforced landslide. Difference value of TIR temperature ( T I R m ) is a useful indicator for landslide detection, and its anomalies can be selected as a precursor to landslide deformation.

Platycodon grandiflorus is a widely used edible, traditional Chinese medicinal herb. It is rich in saponins, flavonoids, phenolic acids, and other compounds. It contains a large number of fatty acids such as linoleic acid (up to 63.24%), a variety of amino acids, vitamins, and multiple essential trace elements. P. grandiflorus has several biological applications, such as in hypotension, lipid reduction, atherosclerosis, inflammation, relieving cough and phlegm, promoting cholic acid secretion, and as an antioxidant. Further, P. grandiflorus is often used in the development of cold mixed vegetables, canned vegetables, preserved fruit, salted vegetables, and cosmetics in northeast China, South Korea, Japan, and Korea. In this paper, the active chemical components and the health benefits of P. grandiflorus have been reviewed, providing new ideas for the further development of nutraceutical products to prevent and manage chronic diseases.

Network security is one of the most important issues in mobile sensor networks (MSNs). Networks are particularly vulnerable in hostile environments because of many factors, such as uncertain mobility, limitations on computation, and the need for storage in mobile nodes. Though some location-based security mechanisms can resist some malicious attacks, they are only suitable for static networks and may sometimes require large amounts of storage. To solve these problems, using location information, which is one of the most important properties in outdoor wireless networks, a security mechanism called a moving security area (MSA) is proposed to resist malicious attacks by using mobile nodes’ dynamic location-based keys. The security mechanism is layered by performing different detection schemes inside or outside the MSA. The location-based private keys will be updated only at the appropriate moments, considering the balance of cost and security performance. By transferring parts of the detection tasks from ordinary nodes to the sink node, the memory requirements are distributed to different entities to save limited energy.

Tobacco mosaic virus (TMV) causes significant losses in many economically important crops. Contaminated soils may play roles as reservoirs and sources of transmission for TMV. In this study we report the development of an immunocapture real-time RT-PCR (IC-real-time RT-PCR) assay for direct detection of TMV in soils without RNA isolation. A series of TMV infected leaf sap dilutions of 1:101, 1:102, 1:103, 1:104, 1:105 and 1:106 (w/v, g/mL) were added to one gram of soil. The reactivity of DAS-ELISA and conventional RT-PCR was in the range of 1:102 and 1:103 dilution in TMV-infested soils, respectively. Meanwhile, the detection limit of IC-real-time RT-PCR sensitivity was up to 1:106 dilution. However, in plant sap infected by TMV, both IC-real-time RT-PCR and real-time RT-PCR were up to 1:106 dilution, DAS-ELISA could detect at least 1:103 dilution. IC-real-time RT-PCR method can use either plant sample extracts or cultivated soils, and show higher sensitivity than RT-PCR and DAS-ELISA for detection of TMV in soils. Therefore, the proposed IC-real-time RT-PCR assay provides an alternative for quick and very sensitive detection of TMV in soils, with the advantage of not requiring a concentration or RNA purification steps while still allowing detection of TMV for disease control.

Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.

Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

Tungsten oxide (WO3) is prepared by a low-temperature ultrasonic spray pyrolysis method in air atmosphere, and it is used as an anode buffer layer (ABL) for organic solar cells (OSCs). The properties of the WO3 transition metal oxide material as well as the mechanism of ultrasonic spray pyrolysis processes are investigated. The results show that the ultrasonic spray pyrolysized WO3 ABL exhibits low roughness, matched energy level, and high conductivity, which results in high charge transport efficiency and suppressive recombination in OSCs. As a result, compared to the OSCs based on vacuum thermal evaporated WO3, a higher power conversion efficiency of 3.63% is reached with low-temperature ultrasonic spray pyrolysized WO3 ABL. Furthermore, the mostly spray-coated OSCs with large area was fabricated, which has a power conversion efficiency of ~1%. This work significantly enhances our understanding of the preparation and application of low temperature-processed WO3, and highlights the potential of large area, all spray coated OSCs for sustainable commercial fabrication.