With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accuracy in the PointPillars 3D target detection algorithm, this paper proposes an algorithm based on feature enhancement to improve the backbone network. The algorithm enhances preliminary feature information of the Backbone network by modifying it based on PointPillars with the aid of channel attention and spatial attention mechanisms. To address the inefficiency caused by the excessive number of subsampled parameters in PointPillars, FasterNet (a lightweight and efficient feature extraction network) is utilized for downsampling and forming different scale feature maps. To prevent the loss and blurring of extracted features resulting from the use of inverse convolution, we utilize the lightweight and efficient up-sampling modules Carafe and Dysample for adjusting resolution. Experimental results indicate improved accuracy under all difficulties of the KITTI dataset, demonstrating the superiority of the algorithm over PointPillars.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

Leak of carbon dioxide (CO2) from conventional underground carbon storage reservoirs is an increasing concern. It is highly desirable to inject CO2 into low-temperature reservoirs so that CO2 can be locked inside the reservoir in solid state as CO2-hydrates. Marine gas hydrate reservoirs and surrounding water aquifers are attractive candidates for this purpose. However, the nature of low-permeability of these marine sediments hinders injection of CO2 in commercial scale due to the low-injectivity of wells with conventional completions. This study investigates injection of CO2 into the low-permeability marine reservoirs using a new type of well namely radial-lateral wells (RLW). An analytical model was developed for predicting CO2 injectivity of the RLW to assess well injectivity improvement. A case study and sensitivity analysis were performed with field data from the South China Sea. Result of analysis reveals that the injectivity of the RLW is nearly proportional to reservoir permeability, lateral wellbore length, and number of laterals. CO2 injection rate is predicted to be 19 tons/day to 250 tons/day, which is 3 to 15 times higher than the injectivity of frac-packed wells. It is feasible to inject CO2 into the low-permeability-low-temperature marine reservoirs at commercial flow rates. This work provides an analytical tool to predict CO2-injectivity of RLW in low-temperature marine reservoirs for leak-free CO2 storage.

This paper presents the design and experimental results of a long cavity length Nd: YAG laser with large stable zone for water jet guided laser (WJGL) applications. The design is based on the light transmission matrix and resonator stability conditions, aiming to achieve a large stable zone and a short cut-off thermal focal length (CTFL). A folded concave resonator is researched to enhance the cavity length, and the influence of the tun-able cavity arm length on the oscillating beam in the resonator and in the YAG crystal is theoretically studied. Moreover, the effects of the output mirror curvature and the cavity arm length on the range of the stable area and the cut-off thermal focal length are also investigated. Experimental results show that a stable green laser output is obtained after second harmonic generation (SHG), with a pulse width ranging from 43ns to 143ns within the laser operating frequency range of 5-20kHz. At an operation frequency of 10kHz, the output power is 21.33W, and the instability of the output power within 400 minutes is 0.88%. The laser source achieves a maximum power of 25.7W at 20kHz, and the maximum single pulse energy reaches 2.7mJ at 6kHz. Finally, this is used as the laser source to couple with a water jet with a diameter of 100 microns, achieving a lossless water conductivity transmission over 60mm length with a peak power density of 0.742GW/cm2. These results demonstrate the suitability of the designed laser source for WJGL technology research.

Background Frequent presenters (FPs) define a group of individual who visit hospital emergency depart-ment (ED) frequently for urgent care. Many among the group present with main diagnosis of mental health conditions. This group of individual tend to use ED resources disproportionally and significantly affect overall healthcare outcome. No previous reviews have examined the profiles of FPs with mental health conditions. Aims This study aims to identify the key socio-demographic and clinical characteristics of patients who frequently present to ED with mental health primary diagnosis by performing systemic review from existing literature. Method PRISMA guideline was used. PubMed, PsycINFO, Scopus and Web of Science (WOS) were searched in May 2023. A manual search on reference list of included articles were conducted at same time. Covidence was used to perform extraction and screening, completed by two authors independently. Inclusion and exclusion criteria were defined. Results The abstracts of 3341 non-duplicate articles were screened with 40 full texts assessed for eligi-bility. 20 studies were included from 2004-2022 conducted in 6 countries with total patient number of 25688 (52% male, 48% female, mean age 40.7 years old). 27% were unemployed, 20% married, 41% homeless, and 17% had tertiary or above education. 44% had history of substance abuse or alcohol dependence. Top 3 diagnosis are found to be anxiety disorders (44%), depressive disorders (39%) and schizophrenia spectrum and other psychotic disorders (33%). Conclusion On average, FPs are middle aged and equally prevalent in both gender. Current data lacks rep-resentation for gender diverse group. They are significantly associated with high rate of unem-ployment, homelessness, lower than average education level, and being single. Anxiety disor-der, depressive disorder, and schizophrenia spectrum disorders are the most common clinical diagnosis associated with the group.

The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

This study examined infants' neural responses to emotional prosody in natural speech. A multi-feature oddball paradigm was used with 34 3~11 month-old infants. Results showed distinct early (100-200 ms) and late (300-500 ms) mismatch responses to different emotional prosodies. Older infants had more negative early responses with happy and angry prosodies evoking stronger responses compared to sad prosody while younger infants showed a clearer distinction between angry and sad prosodies. In the late time window, angry prosody elicited more negative responses than sad prosody with younger infants showing more distinct responses between these two prosodies. Males exhibited stronger early mismatch responses than females. These findings call for further research on the implications for socio-emotional and language development.

Due to the imbalance between the supply and demand of oxygen, the oxygen system of iron- and steel-making enterprises in China has problems with high oxygen emission and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To relieve this problem, using a large-scale iron- and steel-making mill as a case study, the research on demand forecasting and optimal scheduling of the oxygen system was carried out. The ARIMA model and the GABP model are employed to forecast oxygen demand. Based on the forecast results, an optimal scheduling model for the oxygen system was developed to conduct optimal scheduling. The case study shows that based on the oxygen demand forecast and the optimal scheduling, the oxygen emission and the pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions are obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model is better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models are 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it is found that after the scheduling, the amount of oxygen emission has decreased by 6.32%, the pipeline pressure has decreased by 0.61%, and the energy consumption of oxygen compression has decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the oxygen system is reduced by 1.38%, which saves the electricity cost of about 9.03 million RMB per year.

In order to meet the diversified rehabilitation needs of people, it is necessary to design a product for active rehabilitation of patients. Existing rehabilitation chairs use intelligent massage, which can cause problems such as large massage area, inability to local massage, large chair size, and inability to meet the continuous use of the damaged parts. In this paper, modular design method and multi-layer evaluation method are used to solve the problems related to rehabilitation chairs. The authors use the questionnaire survey method and the functional technology matrix method to determine the functional requirements of the rehabilitation chair, and then use the multilevel evaluation methods, including the AHP method, entropy weight method main and grey correlation analysis, to optimize the functional solutions of the rehabilitation chair, and finally obtain a chair for the rehabilitation of patients with upper and lower limb disorders. Problems such as generalisation of rehabilitation scope and non-durable use of components were solved, and the purpose of active exercise was achieved. This study verifies that the use of multilevel decision evaluation method can effectively improve the efficiency of programme decision-making, and provides theoretical and practical basis for the design of similar products.

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Germanium (Ge) nanostrip is embedded in polymer and studied as a waveguide. Measurement reveals that this new type of semiconductor/polymer heterogeneous waveguide exhibits strong absorption for the TE mode from 1500 nm to 2004 nm, while the propagation loss for the TM mode declines from 20.56 dB/cm at 1500 nm to 4.89 dB/cm at 2004 nm. The transmission characteristics serves as an essential tool to verify the optical parameters (n-κ) of the thin strips, addressing to the ambiguity raised by spectroscopic ellipsometry regarding highly absorbing materials. Furthermore, the observed strong absorption for the TE mode at 2004 nm is well beyond the cut-off wavelength of the crystalline bulk Ge (~1850 nm at room temperature). This redshift is modeled to manifest the narrowing of the Tauc-fitted bandgap due to the grain order effect in the amorphous Ge layer. The accurate measurement of the nanometer-scale light-absorbing strips in a waveguide form is a crucial step toward the accurate design of integrated photonic devices that utilize such components.

In order to create a smart mine, it is necessary to digitize the existing emulsion pumping station. Existing research does not achieve this task, this paper designs and manufactures a high-pressure, high-flow, high-water-based mining digital controlled unloading valve (abbreviated as HLHMDCUV), which achieves good results in both theoretical analysis and experimental testing. In order to obtain the accurate dynamic characteristics, this valve was analyzed by simulation using AMESim. Based on the dynamic performance analysis and the flow field analysis inside the valve body, the structure has been optimized to improve the performance of HLHMDCUV. The results show that the HLHMDCUV has a better dynamic characteristics, which were also demonstrated in the experiment. At the same time, the HLHMDCUV is easy to disassemble and maintain. This paper provides a theoretical basis for the design of such valves, which is conducive to the subsequent promotion of intelligent mine transformation.

Intergenerational integration has given rise to a novel aging paradigm known as all-age communities, which is garnering international attention. In China, the aging population and the implementation of the three-child policy have resulted in increased demand for retirement and childcare services among residents in older neighborhoods. Consequently, there is a pressing need to retrofit these older neighborhoods to accommodate all-age living arrangements given the high demand they generate. Therefore, this study undertakes research interviews with residents and constructs an exploratory theoretical model rooted in established theory. To assess the significance of our model, we employ Smart PLS 3.0 based on 297 empirical data points. Our findings indicate that anxiety has a significant negative effect on payment behavior; objective perception, willingness to pay, and government assistance exert significant positive effects on payment behavior. By comprehensively analyzing the mechanisms underlying residents’ payment behavior, this study provides valuable insights to the government for promoting the aging process within communities and formulating effective transformation policies.

The zero-catch problem is a key issue in CPUE(Catch Per Unit Effort) standardization, and previous studies have treated all zero-catch data uniformly, but this actually loses some correctly-labeled samples. On the other hand, for the main catches with few zero-catch samples, the problem of low performance of fisheries forecasting remains unsolved even though the forecasting model structure is updating constantly, since we cannot know whether the samples are correctly recorded. In this paper, we propose a method based on confident learning theory to detect anomalous samples in the datasets and unify zero-catch and non-zero samples as noise through an overarching framework of learning with noisy labels, which reveals the heterogeneity among zero-catch samples (as well as among non-zero samples) and the homogeneity between zero-catch samples and non-zero samples. Using three species of tuna in the tropical Atlantic Ocean with the spatial resolution of 0.5 ◦ × 0.5 ◦ and time resolution of days from 2016 to 2019 as experimental material, performance on all three classical machine learning models(Random forest, Support Vector Machine and XGBoost) is significantly improved compared to each baseline.This confirms that we propose a self-adaptive, effective method for detecting and repairing anomalous samples in the fishery dataset.

This study empirically examines the spatial spillover effect of various environmental regulations on green technology innovation using panel data from 284 county-level cities in China between 2007 and 2019. A geographical-economic spatial weight matrix is constructed, and the spatial Durbin model is employed to identify the specific characteristics of this spillover effect. The findings indicate that the spatial spillover effect of green technology innovation primarily occurs through geographical transmission. However, there is no significant spatial autocorrelation when using the economic distance weight matrix. Various types of environmental regulations influence the spatial spillover effect of green technology innovation in distinct ways. Specifically, Market-motivated environmental regulation exhibits a U-shaped relationship with the spatial spillover effect, while Command-controlled environmental regulation demonstrates an inverted U-shaped relationship, suggesting a complementary effect. Additional research shows that the upgrading of industrial structure acts as a mediator between command environmental regulation and the spatial spillover effect of green technology innovation. Government departments should comprehensively coordinate Market-motivated environmental regulation and Command-controlled environmental regulation, accurately assess the intensity of Command-controlled measures, and prevent the migration of green technology innovation elements caused by excessive regulatory measures within enterprises.

Patient value is an important factor in clinical decision-making, but conventionally, it is not incorporated in the decision processes. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. Categorized values and candidate therapies are used in combination as filtering keywords to build this evidence. Then a formalism model is put forward to integrate the value-based evidence with clinical evidence. The relative importance of two evidence can be adjusted dynamically by decision-makers during the decision-making processes. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness.

Isokinetic testing of the knee joint is the “gold standard” in strength assessment in sport especially in soccer, where it is part of pre-season screening to establish norms in case of injuries. There are many devices on the market that make it difficult to use of normative data. Isokinetics literature in females’ soccer players is scarce in comparison to males. Furthermore, evaluation protocols strongly differ from each other, using different ranges of speeds, movement patterns and output data. Thus, it is difficult for the researcher and the clinician to choose a correct protocol for isokinetic evaluation in female soccer players. The indexes of isokinetic performance that are widely accepted are the hamstring/quadriceps (H/Q) ratio and dominant/non-dominant (D/ND) ratio as reference. In this review are highlighted some common features of isokinetic testing in female soccer players of different levels of qualification. From this study, it emerges that, for a reliable test, 3 reps at the speed of 60°/sec in knee flexion/extension is preferable due to the high reliability and that an optimal H/Q ratio in healthy female soccer players of different level of qualification and age is 50%, while the interlimb differences (D/ND) should be below 10%.

The ever deepening population aging has brought opportunities and challenges to the continued expansion of tourism consumption in China. In this article, the impact of population aging on household tourism consumption was studied from both theoretical and empirical aspects. First, the micro mechanisms of the impact of population aging on household tourism consumption were analyzed, which include economic condition mechanism, intergenerational care mechanism, health status mechanism and Internet use mechanism. Subsequently, the impact of population aging on household tourism consumption and its impact mechanisms were tested, and the heterogeneity analysis was conducted, by using the data of China Family Panel Studies (CFPS) from 2014, 2016 and 2018. The main conclusions of the article are as follows: (1) Population aging has a significant negative impact on household tourism consumption, and a series of robustness test results such as endogenous treatment support this finding. (2) The results of the impact mechanism test show that population aging has a negative mediating effect on household tourism consumption by reducing household economic conditions. The moderating effect of intergenerational care on the effect of population aging on household tourism consumption is insignificant. Health status of the elderly and Internet use have a significant positive moderating effect on the impact of population aging on household tourism consumption. (3) The results of the heterogeneity analysis show that the negative impact of population aging on household tourism consumption in urban areas is greater than that in rural areas, and the negative impact of population aging on household tourism consumption of families with the old-old elderly is greater than that of falimies with the young-old elderly. The conclusions of this article provide important references for activating tourism consumption in China in the context of population aging.

Purpose: Emotional voice conveys important social cues that demand listeners’ attention and timely processing. This event-related potential study investigated the feasibility of a multi-feature oddball paradigm to examine adult listeners’ neural responses to detecting emotional prosody changes in non-repeating naturally spoken words. Method: Thirty-three adult listeners completed the experiment by passively listening to the words in neutral and three alternating emotions while watching a silent movie. Previous research documented pre-attentive change-detection electrophysiological responses (e.g., MMN, P3a) to emotions carried by fixed syllables or words. Given that the MMN and P3a have also been shown to reflect extraction of abstract regularities over repetitive acoustic patterns, the current study employed a multi-feature oddball paradigm to compare listeners’ MMN and P3a to emotional prosody change from neutral to angry, happy, and sad emotions delivered with hundreds of non-repeating words in a single recording session. Results: Both MMN and P3a were successfully elicited by the emotional prosodic change over the varying linguistic context. Angry prosody elicited the strongest MMN compared to happy and sad prosodies. Happy prosody elicited the strongest P3a in the centro-frontal electrodes, and angry prosody elicited the smallest P3a. Conclusions: The results demonstrated that listeners were able to extract the acoustic patterns for each emotional prosody category over constantly changing spoken words. The findings confirm the feasibility of the multi-feature oddball paradigm in investigating emotional speech processing beyond simple acoustic change detection, which may potentially be applied to pediatric and clinical populations.

In recent years, neural networks based on attention mechanisms have been increasingly widely used in speech recognition, separation, enhancement, and other fields. In particular, the convolution-augmented transformer has achieved good performance as it can combine the advantages of convolution and self-attention. Recently, the gated attention unit (GAU) has been proposed. Compared with the traditional multi-head self-attention, approaches with GAU are effective and computationally efficient. In this article, we propose a network for speech enhancement called CGA-MGAN, a kind of Metric GAN based on convolution-augmented gated attention. CGA-MGAN captures local and global correlations in speech signals at the same time through the fusion of convolution and gated attention units. Experiments on Voice Bank + DEMAND show that the CGA-MGAN we propose achieves an excellent performance (3.47 PESQ, 0.96 STOI, and 11.09dB SSNR) at a relatively small model size (1.14M).

There have been relatively few researches that determine the role of bricolage in mediating the empirical relationship between strategic flexibility and product innovation. However, most of the research has studied strategic flexibility to engage the absorptive capacity and facilitate product innovation. The Bricolage approach emphasizes utilizing existing resources in product innovation and explores the mechanisms behind bricolage. A resource-based approach to product innovation involves combining resources in the development process in a concrete manner. This is a groundbreaking study since it is the first to examine empirically how absorptive capacity affects product innovation through bricolage and the serial mediation of strategic flexibility. As a consequence of presenting our results, we concluded that absorptive capacity positively and significantly influences product innovation through the serial mediation of strategic flexibility and bricolage. For strategic flexibility to play a role in supporting product innovation, bricolage is one of the mechanisms that can be utilized. This study contributes to the literature on strategic flexibility by examining the effect of strategic flexibility on bricolage and product innovation from the standpoint that absorptive capacity enhances the strategic flexibility of high-tech SMEs in China. Furthermore, this research offers new insights into the relationship between absorptive capacity and product innovation. In addition, it also provides insight into the economic opportunities that may result from product innovation in transition economies such as China.

Few studies have examined the role of mediating mechanisms played by bricolage in the relationship between strategic flexibility and new product development, even though most of the studies view strategic flexibility as a capability to cope with environmental turbulence and promote product innovation. The bricolage approach embraces the resource-based understanding of the product development process as a concrete process of recombining resources in the process and exploring the mechanisms behind the bricolage approach. This study is the first of the kind that has examined the empirical effect of environmental turbulence on new product development through serial mediation of strategic flexibility and bricolage. Based on our results, environmental turbulence positively and significantly affects new product development through the serial mediation of strategic flexibility and bricolage. Bricolage is one of the mechanisms by which strategic flexibility may play a part in supporting product innovation. Our contribution to strategic flexibility research comes from examining the effect of strategic flexibility on bricolage and new product development from the point that environmental turbulence compels the high-tech industries to remain strategically flexible for their sustainability and growth. Besides providing new evidence on the relationship between environmental turbulence and product innovation, this research also provides new insight into the economic opportunity of new product development in transition economies such as China.

The age of information (AoI) metric was proposed to measure the freshness of messages obtained at the terminal node of a status updating system. In this paper, the AoI of discrete time status updating system with probabilistic packet preemption is investigated by analyzing the steady state of a three-dimensional discrete stochastic process. Assuming the queue used in system is Ber/Geo/1/2*/η, which represents that the system size is 2 and the packet in buffer can be preempted by fresher packet with probability η. Instead of considering system’s AoI separately, we use a three-dimensional state vector (n,m,l) to simultaneously track the real time changes of the AoI, the age of packet in server, and the age of packet waiting in buffer. We give the explicit expression of system’s average AoI, and show that the average AoI of system without packet preemption is obtained by letting η=0. When η is set to 1, the mean of AoI of system having Ber/Geo/1/2* queue is obtained as well. Combining the results we have obtained and comparing them with corresponding average continuous AoIs, we propose a possible relationship between average discrete AoI with Ber/Geo/1/c queue and the average continuous AoI with M/M/1/c queue. For each of two extreme cases where η=0 and η=1, we also determine the stationary distribution of AoI using the probability generation function (PGF) method. The relations between average AoI and the packet preemption probability η, as well as AoI’s distribution curves in two extreme cases are illustrated by numerical simulations. Notice that the probabilistic packet preemption may occur, for example, in an energy harvest (EH) node of wireless sensor network, where the packet in buffer can be replaced only when the node collects enough energy. In particular, to exhibit the usefulness of our idea and methods and highlight the merits of considering discrete time systems, in this paper we give much more explanations showing that how the results about continuous AoI is derived by analyzing the corresponding discrete time system, and how the discrete age analysis is generalized to the system with multiple sources. In terms of packet service process, we also propose our idea to analyze system’s AoI when the service time distribution is relaxed to be arbitrary.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

Data independent acquisition - mass spectrometry (DIA-MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA-MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on Arabidopsis sample with spike-in of known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400-1200 m/z. When combining several DIA-MS analyses with narrow precursor ranges (i.e., 400-650, 650-900 and 900-1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 59.4% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the 6-protein mix spike-in. In cDIA only 5 out of the 6-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

What is the ideal soil-grass combination for maximum photosynthesis? In this study, we investigated how soil and grassland types affect photosynthesis in the grasslands of Gannan, China. We divided the grasslands of Gannan into 166 study sites, each with a unique soil-grass combination by intersecting the soil and ecoregion maps using ArcGIS. We obtained 19 years of data on the Net Photosynthesis (PsnNet) of grasslands in the area from 2000 to 2018 and then divided them into “growing season” (June to September) and “non-growing season” (October top May). Between 2000 and 2018, PsnNet of grasslands showed a gradually increasing trend. The effect of soil type on PsnNet was not significant during the growing season. However, it was highly significant during the non-growing season. Among the soil types, grasses that grew in Mollic, Gelic, and Haplic soils had the highest rate of photosynthesis. The difference in PsnNet among the various grass types was highly significant during both seasons. However, Tropical and Subtropical succulent evergreen broad-leaf shrubs and Temperate meadows had the highest rate of photosynthesis. Additionally, there was a highly significant difference in PsnNet among the various soil-grass interactions. In the growing season, TStEgBLS growing Eutric soils had the highest PsnNet. However, SaDBLS growing in Gelic soils had the the highest.

With increasing availability of alternative mobility options for city transportation system, it is necessary to better understand how emerging mobility are impacting the travel demand and consumer-social surplus. However, few study has been conducted to evaluate the social welfare effects of the range of vacant trip in mobility oriented development(MOD) mode. This paper identified the vacant trip and loading rate evaluation model under mobility oriented development mode. recommended solutions to enhance the effective operation of the different mobility services under numerical illustrations, which can offer some beneficial guidance and theoretic basis to the efficiency of MOD in planning and management aspects.

The purpose of this study is to examine the driving model of determinant factors that affects the performance of small and micro enterprises empirical evidence from Amhara Region, Ethiopia. The study used primary and secondary data from manufacturing, construction, urban agriculture, trade, and service entrepreneurs found in Bahirdar, Dessie & Gonder Cities using a purposive sampling technique. It also used the descriptive research design with a self-administered survey questionnaire. The Statistical analysis tools, SPSS and Amos, software were applied to analyze the data. Multiple regression model results revealed that access to credit, initial capital, working premises, industry category, market linkage, ICT adoption have a positive relationship and a major impact on the Amhara region's enterprise performance. Hence, the outcome variable, enterprise performance, is highly affected by all predicted variables. As the study finding shows, a lack of access to credit, , market linkage, working premises), initial capital, and information communication Technology problems are critical challenges of SMEs. The Regional Government, TVED Bureau, Micro Finance Institutions, , should take corrective action to provide sufficient credit and long-term loans to protect inflation. Moreover, this study proposed a sustainable cluster management approach that solves their challenges in an integrated way.

With the rapid development of urbanization and industrialization in China, environmental issues have become a knotty problem, especially issues related to air, water, and solid-waste pollution. These pollutants pose threats to the health of the population and to that of communities and have a vicious influence on the healthcare system. Additionally, pollution also exhibits spill-over effects, which means that pollution in the local region could affect the healthcare services in a neighbouring region. Therefore, it is necessary to explore the relationship between pollution and healthcare. A spatial autocorrelation analysis was conducted and spatial panel econometric models were constructed to explore the characteristics of pollution and healthcare services in China and the relationship between them using data on all 31 provinces over twelve consecutive years (2006-2017). The results showed that the utilization of healthcare services and environmental pollution were not randomly distributed; unsurprisingly, air pollution and solid-waste pollution were mainly found in parts of northern China, while water pollution was highest in southern and coastal China. In addition, environmental pollution exhibited spill-over effects on healthcare services. For example, a 1% increase in solid waste in one specific geographical unit was estimated to increase the inpatient visits per capita in adjacent counties by 0.559%. Specifically, pollution showed different degrees of influence on healthcare services, which means that the impact of environmental pollution on the number of outpatient visits is greater than on the number of inpatient visits. Our results provide the government with evidence for effectively formulating and promulgating policies, especially policies aimed at tackling spill-over effects among different regions.

From a scientific standpoint, both temporal and spatial variables must be examined when developing programs for training various soccer scoring techniques (SSTs), but a review of current literature reveals that existing scientific studies have overlooked this combinatory influence. Consequently, there is no reliable theory on temporal-spatial identification when evaluating scoring opportunities. Quantified by using biomechanical modeling, anthropometry, and SSTs found in FIFA Puskás Award (121 nominated goals between 2009 and 2020), it is found that players’ proprioceptive/effective shooting volume (i.e. players’ attack space) could be sevenfold the currently-practiced shooting volume. The ignorance of some SSTs’ training leads to the underuse of the potential shooting volume. These overlooked SSTs are airborne and/or acrobatic techniques, perceived as high-risk and low-reward. Relying on the talent of an athlete to improvise on the fly can hardly be considered as a viable coaching strategy. Therefore, for developing science-based SST training regimes, groundbreaking studies are needed to: 1) expand the perception of shooting volume, and 2) entrain one-touch-shot techniques (airborne/acrobatic) within this volume, in short, Focusing-on-Time-in-Space. Whence, the new temporal-spatial theory could guide future researches and develop novel training programs. An increase of airborne/acrobatic goals would ultimately further enhance the excitement of the game.

In this paper, we study the Parisian time of a reflected Brownian motion with drift on a finite collection of rays. We derive the Laplace transform of the Parisian time using a recursive method, and provide an exact simulation algorithm to sample from the distribution of the Parisian time. The paper is motivated by the settlement delay in the real-time gross settlement (RTGS) system. Both the central bank and the participating banks in the system are concerned about the liquidity risk, and are interested in the first time that the duration of settlement delay exceeds a predefined limit, we reduce this problem to the calculation of the Parisian time. The Parisian time is also crucial in the pricing of Parisian type options; to this end, we will compare our results with the existing literature.

This article explores the impact of farmers’ vocational training on their income. Survey data concerning agricultural product quality and agricultural product sales were collected by the research team in Yantai City, China. An endogenous transformation regression model (ESR) was then used to analyze the impact of training on farmers’ family income, and the income mechanism was further tested. The results from the empirical analysis suggest that (1) Participating in training can significantly increase farmers’ income; (2) Farmers who pay more attention to the quality of their agricultural products during the production process are more likely to see their incomes increase after participating in the training.

The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the HWRF model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

For the problem of poor accuracy of the existing multi-rate simulation methods, this paper proposes a multi rate real-time simulation method based on the Norton equivalent, compared with multi-rate simulation method based on the ideal source equivalent. After the Norton equivalence of the fast subsystem and the slow subsystem, they are obtained simultaneously at the junction nodes. In order to reduce the amount of simulation calculation, the Norton equivalent circuit is obtained by incremental calculation. The data interface between the fast subsystem and the slow subsystem is realized by extrapolation method. For ensuring the real-time performance of the simulation, the method that the slow subsystem calculates ahead of the fast subsystem is given for the slow subsystem with a large amount of calculation. Finally, the AC/DC hybrid power system was simulated on the real-time simulation platform (FRTDS), and the simulation results were compared with the single-rate simulation, which verified the correctness and accuracy of the method.

During an epidemic outbreak it is useful for planners and responsible authorities to be able to plan ahead to estimate when an outbreak of an epidemic is likely to ease and when the last case can be predicted in their area of responsibility. Theoretically this could be done for a point source epidemic using epidemic curve forecasting. The extensive data now coming out of China makes it possible to test if this can be done using MS Excel a standard spreadsheet program available to most offices. The available data is divided up for whole China and the different provinces. This and the high number of cases makes the analysis possible. Data for new confirmed infections for Hubei, Hubei outside Wuhan, China excluding Hubei as well as Zhejiang and Fujian provinces all follow a log-normal distribution that can be used to make a rough estimate for the date of the last new confirmed cases in respective areas. In the version 2 continuation work, 9 additional days were added for the Chinese data to evaluate the previous predictions. The extra data then available from China follows the previous predicted trend supporting the usefulness of this simple technique. In the version 2 we also tested the feasibility for a non-specialist to make similar predictions using additional data from S Korea now available. In this third continuation the predictions for Version 2 are evaluated for S Korea and fits well the beginning of the decline but it seems to be difficult to bring down numbers of cases per day under about 100 new cases per day, potential reasons for this is discussed. To further evaluate when in a prediction becomes reliable the Chinese data was used to evaluate to make predictions for each day around the peak in number of cases and after2-3 consecutive days of decreasing new cases per day the prediction becomes reliable. In version 3 data for Italy just reaching this point was used to make further predictions for that country. A second new analysis was also added to use the fitted equation to detect when the acceleration of new cases per day stopped increasing exponentially. In the Chinese case this measured point coincides with the date of the complete Hubei lockdown and in the new Italian analysis it coincides with the mandatory Italian lockdown. Predicted dates for the end of the Italian outbreak is also added. In version 4 we expand the analysis to selected European countries, USA and the World as a whole and try to predict the end of the outbreak. We further discuss the apparent success of the used techniques that might work to introduce a “new normal” not very different to the previous to stop secondary outbreaks of COVID19 and future COVIDs that are sure to come.

Starting from December 2019, coronavirus disease 2019 (COVID-19) has emerged as a once-in-a-century pandemic with deadly consequences, which urgently calls for new treatments, cures and supporting apparatuses. Remdesivir was reported by World Health Organization (WHO) as the most promising drug currently available for the treatment of COVID-19. Here, we use molecular dynamics simulations and free energy perturbation methods to study the inhibition mechanism of remdesivir to its target SARS-CoV-2 virus RNA-dependent RNA polymerase (RdRp). In the absence of a crystal structure of the SARS-CoV-2 RdRp, we first construct the homology model of this polymerase based on a previously available structure of SARS-CoV NSP12 RdRp (with a sequence identify of 95.8%). We then build the putative binding mode by aligning the remdesivir + RdRp complex to the ATP bound poliovirus RdRp. The putative binding structure is further optimized with molecular dynamics simulations and demonstrated to be stable, indicating a reasonable binding mode for remdesivir. The relative binding free energy of remdesivir is calculated to be -8.28 ± 0.65 kcal/mol, much stronger than the natural substrate ATP (-4.14 ± 0.89 kcal/mol) which is needed for the polymerization. The ~800-fold improvement in the K_d from remdesivir over ATP indicates an effective replacement of APT in blocking of the RdRp binding pocket. Key residues D618, S549 and R555 are found to be the contributors to the binding affinity of remdesivir. These findings demonstrate that remdesivir can potentially act as a SARS-CoV-2 RNA-chain terminator, effectively stopping its RNA reproduction, with key residues also identified for future lead optimization and/or drug resistance studies.

Unmanned surface vehicles have the advantages of maneuverability, concealment, wide activity area and low cost of use. Therefore, they have broad application prospects. This makes unmanned surface vehicles a research hotspot at home and abroad, and the sensing technology is the basis for the unmanned surface vehicles to perform tasks. The perception technology based on optical vision has the advantages of convenient application, relatively low cost, easy data acquisition and large amount of information, and has been widely studied by scholars at home and abroad. This paper mainly discusses the research of optical vision in unmanned surface vehicles from five aspects: Firstly, the water surface image preprocessing based on unmanned surface vehicles, mainly including water surface image stabilization research and defogging enhancement research; two water boundary detection; It is the use of light vision target detection; the fourth is the surface target tracking method. Finally, the light vision research of unmanned surface vehicles is summarized and forecasted.

Firstly, a new set of Quasi-Cubic Trigonometric Bernstein basis with two tension shape parameters is constructed, and we prove that it is an optimal normalized totally basis in the framework of Quasi Extended Chebyshev space. And the Quasi-Cubic Trigonometric Bézier curve is generated by the basis function and the cutting algorithm of the curve are given, the shape features (cusp, inflection point, loop and convexity) of the Quasi-Cubic Trigonometric Bézier curve are analyzed by using envelope theory and topological mapping; Next we construct the non-uniform Quasi-Cubic Trigonometric B-spline basis by assuming the linear combination of the optimal normalized totally positive basis have partition of unity and continuity, and its expression is obtained. And the non-uniform B-spline basis is proved to have totally positive and high-order continuity. Finally, the non-uniform Quasi Cubic Trigonometric B-spline curve and surface are defined, the shape features of the non-uniform Quasi-Cubic Trigonometric B-spline curve are discussed, and the curve and surface are proved to be continuous.

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change and even viewpoint change.

Resource Description Framework(RDF) is a data representation format of the Semantic Web, and its data volume is growing rapidly. Cloud-based systems provide a rich platform for managing RDF data. However, the distributed environment has performance challenges when it is processing with RDF queries that contain multiple join operations, such as network reshuffle, memory overhead. To get over these challenges, this paper proposed a spark-based RDF query architecture, which is based on Semantic Connection Set (SCS). First of all, this spark-based query architecture adopts the mechanism of re-partitioning class data based on vertical partitioning, which can reduce memory overhead and fast index data. Secondly, a method for generating query plans based on semantic connection sets is proposed in this paper. In addition, statistics and broadcast variable optimization strategies are used to reduce shuffling and data communication costs. The experiment of this paper is based on the latest SPARQLGX on the spark platform RDF system, two synthetic benchmarks are used to evaluate the query. The experiment result illustrates that the proposed approach in this paper is more efficient in data search than SPARQLGX.

Project complexity is usually considered as one of main causes of cost overruns, resulting in poor performance and thus project failure. However, empirical studies focused on evaluating its effects on project cost remain lacking. Given this circumstance, this study attempts to develop the relationships between project cost and the multidimensional project complexity elements. We establish complexity as a multidimensional factor including the task, organization, market, legal, and environment complexities. This study uses an empirical evidence-based structural model to account for the relationships between project cost and project complexity. By doing so, a quantitative assessment of multi-dimensional project complexity has been developed. The findings suggest that task and organization complexities have direct effects on project cost, while market, legal and external environment complexities have indirect effects on project cost. The practical contribution is that the findings can improve the understanding of which dimension of complexity significantly influence project cost, and the need to focus efforts on strategically addressing that complexities.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

Detailed built-up area information is valuable for mapping complex urban environments. Although a large number of classification algorithms about built-up areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore we launched a unique investigation to provide a full test of the OLI imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we propose a method based on the ESA's 38-meter global built-up area data of 2014, Open Street Map and MOD13Q1-NDVI to achieve rapid and automatic generation of a large number of sample points. Our aim is to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape and texture as the input features, and SVM, random forest (RF) and AdaBoost as the classification algorithms. In feature learning, the convolution neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. Experimental results show that: (1) the approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers e.g., RF, is comparable to that of CNN. Two dimensional CNN and the 7 neighborhood RF have the highest classification accuracy of nearly 91%. (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (for example, PanTex and EMBI) can help improve classification accuracy.

Recent advances in open data initiatives allow us to free access to a vast amount of open LiDAR data in many cities. However, most of these open LiDAR data over cities are acquired by airborne scanning, where the points on façades are sparse or even completely missing due to the viewpoint and object occlusions in the urban environment. Integrating other sources of data, such as ground images, to complete the missing parts is an effective and practical solution. This paper presents an approach for improving open LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two different sources of data. Firstly, the façade point cloud generated from terrestrial images is initially geolocated by matching the SFM camera positions to their GPS meta-information. Next, an improved Coherent Point Drift algorithm with normal consistency is proposed to accurately align building façades to open LiDAR data. The significance of the work resides in the use of 2D overlapping points on the outline of buildings instead of limited 3D overlap between the two point clouds and the achievement to a reliable and precise registration under possible incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façades details of buildings in open LiDAR data and improving registration accuracy from up to 10 meters to less than half a meter compared to classic registration methods.

Metastasis is a major cause factor for breast cancer (BC)-associated mortality. During the metastatic process, disseminated tumor cells (DTCs) detach from the primary sites, and enter the bloodstream and establish the secondary colonies. Recent studies have provided substantial evidence for the importance of Notch signaling in BC metastasis. Therefore, this review focuses on the mechanisms by which Notch contributes to the origin of BC DTCs, increases their motility, regulates their intravasation and extravasation, protects them from host surveillance, and finally facilitates colonization. Identification of the mechanisms underlying Notch-related BC metastasis will lead to the development of novel Notch-targeted therapeutic strategies to reduce metastasis and significantly improve outcomes.

Transparent electrodes (TEs) made of metallic nanowires, such as Ag, Au, Cu, and Ni, have attracted rising attention for several reasons: 1) they can act as a substitute for tin oxide-based TEs such as indium-tin oxide (ITO) and fluorine-doped tin oxide (FTO); 2) various methods exist for fabricating such TEs such as filtration, spraying and meyer bar coating; 3) greater compatibility with different substrates can be achieved due to the variety of fabrication methods; and 4) extra functions in addition to serving as electrodes, such as catalytic abilities, can be obtained due to the metals that compose the TEs. There are a large number of applications for TEs, ranging from electronics and sensors to biomedical devices. This short review is a summary of recent progress, mainly during the past five years, on silver nanowire-based TEs. The focus of the review will be on theory development, mechanical, chemical and thermal stability and optical properties. The many applications of TEs are outside the scope of this review.

Mode locked pulse generation has been reported using both active and passive mode locking schemes. Active mode locking technique has been proven to be an effective way to generate high-repetition-rate pulses by incorporating a modulator inside the laser cavity. Compared to actively mode locked lasers, passively mode locked lasers can generate pulse train at ultrashort pulse width but with relatively lower repetition rate. Thus, it is a brilliant idea to build a hybrid mode locked system combining both active and passive mode lockers. In this review, several hybrid mode locked fiber ring laser systems are discussed and summarized. Hybrid mode locking is a promising method to generate high speed ultrashort optical pulses for fiber-optic telecommunication system.

Beef potentiator is an important flavor enhancer in food industry, while it is prone to generating insufficient taste compounds and off-odor compounds during enzymatic hydrolysis of beef, resulting in poor flavor of beef potentiator. It has been extensively reported that sonication is capable of improving food flavor. However, the effect of sonication on the flavor of enzymatically hydrolyzed beef liquid (EHBL) was scarcely reported. Herein, we investigated the effect of sonication on the flavor of EHBL using quantitative descriptive analysis (QDA), physicochemical analysis and SPME-GC-olfactometry/MS. QDA showed that sonication had a significant effect on taste improvement and off-odor removal of EHBL. Compared with the control, sonication (40 kHz, 80 W/L) increased the contents of total nitrogen, formaldehyde nitrogen, total sugar, reducing sugar, free amino acids (FAAs) and hydrolysis degree of EHBL by 19.25%, 19.80%, 11.83%, 9.52%, 14.37% and 20.45%. Notably, sonication markedly enhanced the contents of sweet FAAs, umami FAAs and bitter FAAs of EHBL by 19.66%, 14.04% and 9.18%, respectively, which contributed to the taste improvement of EHBL. SPME-GC-olfactometry/MS analysis showed that aldehydes and alcohols were the main contributors to aroma compounds of EHBL, sonication significantly increased the contents of key aroma compounds and 115.88% of alcohols content in EHBL. Notably, sonication decreased contents of the fishy odorants, hexanoic acid and nonanal, markedly by 35.29% and 26.03%, which was responsible for the aroma improvement of EHBL. Therefore, sonication could become a new potential tool to improve the flavor of EHBL.

In order to understand the response mechanism between plant stress, physiological indicators and hyperspectral indices, pot experiments were conducted on Suaeda salsa seedlings collected from a coastal wetland area to reveal the effects of salt stress on the physiological indicators and reflectance spectra of Suaeda salsa at the canopy and leaf level. The Suaeda salsa seedlings were exposed to seven salt treatments of different concentrations (0 mmol/L (control), 50 mmol/L, 100 mmol/L, 200 mmol/L, 300 mmol/L, 400 mmol/L, and 600 mmol/L) in natural conditions. The physiological indicators of plant height, fresh weight, dry weight, leaf succulence, chlorophyll content, and carotenoid content were measured, in addition to the reflectance spectra of Suaeda salsa at both the canopy and leaf level. Firstly, the effects of salt stress on the physiological indicators and reflectance spectra were analyzed by the qualitative and quantitative methods. Then, physiological indicators sensitive to salt stress were further retrieved. Afterwards hyperspectral indices such as a/b and ((a-b)/(a+b) ) sensitive to salt stress were also extracted by one-way analysis of variance (ANOVA) and Student-Newman-Keuls (S-N-K) comparison test. Our results showed that plant height, root length, leaf succulence, biomass, Chl-a, and Chl-b were sensitive to salt stress, while carotenoids (Car) and relative water content on the root were not significantly affected by salt stress. At the salt concentration of 200 mmol/L, plant height, biomass, relative water content, leaf succulence peaked. With enhanced salt stress, physiological indicators decreased. The first-order derivative spectral reflectance has the highest correlation with salt stress, compared to the control. The spectral index most sensitive to the salt stress at the canopy level is (D₉₀₃−D₈₅₁)/(D₉₀₃+D₈₅₁), for which the multiple determination coefficient (r²) is 0.9216. While the most sensitive spectral index to the salt stress is (D₄₄₂−D₆₆₇)/(D₄₄₂+D₆₆₇) at the leaf level, for which the r² is −0.898. In summary, the results indicated that there exists the quantitative relationship between the physiological indicators and spectra reflectance under salt stress and hyperspectral plant indices can effectively estimate the degree of salt stress. The inconsistency between the diagnostic hyperspectral plant indices at the canopy and leaf levels may be caused by the observation conditions, canopy structure.

We investigated the effect of C. psittaci and H9N2 coinfection on HD11 cells in vitro, expecting to find the potential pathogenesis of airsacculitis caused by co-infection of C. psittaci and H9N2. HD11 cells were infected with C. psittaci and/or H9N2 in a different order, and effects of the co-infection on iNOS expression and activity, NO synthesis, cell phagocytosis, and cytokines levels in HD11 cells were determined. Results showed that C. psittaci and H9N2 can significantly aggravate the mortality of HD11 cells compared to the effects of infection with one pathogen alone. In addition, infection with C. psittaci can increase the replication of H9N2 in HD11 cells, whereas decrease the iNOS level and enzyme activity as well as NO concentration of HD11 cells by H9N2 infection. We also found that C. psittaci infection alone can significantly decrease the phagocytosis of HD11 cells, compared to H9N2 infection alone. Furthermore, infection with C. psittaci can increase the mRNA expressions of type Th2 cytokines IL-6 and IL-10 of HD11 cells by H9N2 infection. All the above data indicated that primary C. psittaci infection is able to aggravate H9N2 invasion by down-regulating functions of HD11 cells

Soybean (Glycine max L.) is an important crop that serves as a source of edible oil and protein. However, little is known about its molecular mechanism of adaptation to extreme environmental conditions. Based on the Arabidopsis thaliana sequence database and Phytozome, a soybean gene that was highly homogenous with the reduced induction of the non-photochemical quenching2 (AtRIQ2) gene, GmRIQ2-like (accession NO.: Glyma.04G174400), was identified in this study. The gene structure analysis revealed that GmRIQ2-like encoded a transmembrane protein. Elements of the promoter analysis indicated that GmRIQ2-like participated in the photosynthesis and abiotic stress pathways. The subcellular localization results revealed that the protein encoded by GmRIQ2-like was located in chloroplasts. The quantitative real-time (qRT)-PCR results revealed that GmRIQ2-like-overexpression (OE) and -knock-out (KO) transgenic soybean seedlings were cultivated successfully. The relative chlorophyll (Chl) and zeaxanthin contents and Chl fluorescence kinetic parameters demonstrated that GmRIQ2-like dissipated excess light energy by enhancing the non-photochemical quenching (NPQ) and reduced plant photoinhibition. These results suggested that GmRIQ2-like was induced in response to strong light and depressed Chl production involved in soybean stress tolerance. These findings indicate that the transgenic seedlings of GmRIQ2-like could be used to enhance strong light stress tolerance and protect soybean plants from photoinhibition damage. This study will serve as a reference for studying crop photoprotection regulation mechanisms and benefits the research and development of new cultivars.

Most recently, an outbreak of severe pneumonia caused by the infection of 2019-nCoV, a novel coronavirus first identified in Wuhan, China, imposes serious threats to public health. Many important aspects about 2019-nCoV remain largely unknown, among which, the limitation of antiviral therapies represents one of the most critical problems. More recently, it was confirmed that human ACE2 is the receptor for the entry of 2019-nCoV into lower respiratory tract epithelial cells. Give this observation, it is thus expected that the virus could be inhibited if we decrease the expression of ACE2. Here by screening two databases, Connectivity Map (CMap) and our JeaMoon Map (JMap), we identified a number of candidate agents that decrease ACE2 expression. CMap analysis identified 5 compounds, among which, Azathioprine is a possible therapeutic strategy for anti-2019-nCoV. Moreover, JMap analysis revealed a number of comounds, biologics, and traditional Chinese medicine, among which, Andrographis, Urtica, Sambucus, Astragalus, valproic acid, butyrate, and epoxomicin represent the most significant and possible strategies for anti-2019-nCoV therapies. This study provides a number of clues and possible therapeutic strategies for 2019-nCoV prevention and treatment.

In this paper, the nanoscale dissipative mechanisms of a Cu pad in a Ball Grid Array (BGA) packaging structure during isothermal ageing and uniaxial tension were investigated by the molecular dynamics (MD) method and experiments. From the result of the isothermal ageing test, a nonuniform consumption of Cu and large amount of Kirkendall voids were observed at the interface of Cu and Cu₃Sn. To study the effect of pressure and orientation on this phenomenon, MD simulations were conducted on four types of Cu-Cu₃Sn interface structures with different orientations of Cu. By comparing the diffusion coefficients of atoms in those cases, it was found that the tensile stress would inhibit the diffusion of atoms, whereas compressive stress would accelerate it, and this would be more significant under a larger magnitude of stress and temperature. Note that, in the model with the (101) surface Cu at the interface, both Cu and Cu₃Sn have a higher diffusion coefficient compared with the model with (001) surface Cu. Thus, the orientation of Cu will also contribute to the uniform consumption of the pad. Uniaxial tension simulation combined with DXA and CSP analyses on those models also shows the model with (001) surface Cu has a greater mechanical reliability in our simulations and related experiments.

Fiber-based flexible sensors have promising applications potential in human motion and healthcare monitoring, owing to their merits of lightweight, flexibility, and easy processing. Now, high-performance elastic fiber-based strain sensors with high sensitivity, large working range and excellent durability are in great demand. Herein, we have easily and quickly prepared a highly-sensitive, durable fiber-based strain sensor by dip-coating of highly-stretchable polyurethane (PU) elastic fiber in MXene/ waterborne polyurethane (WPU) dispersion solution. Benefiting from the electrostatic repulsion force between the negatively charged WPU and MXene sheets in the mixed solution, very homogeneous and stable MXene/WPU dispersion was successfully obtained and the interconnected conducting networks were correspondingly formed in coated MXene/WPU shell layer, which makes as-prepared strain sensor exhibit a gauge factor of over 960, a large sensing range of over 90% and detection limit as low as 0.5% strain. As elastic fiber and mixed solution have the same polymer constitute, tight bonding of MXene/WPU conductive composite on PU fibers was achieved, enabling the as-prepared strain sensor endure over 2500 stretching-releasing cycles and thus show good durability. Full-scale human motion detection was also performed by the strain sensor, and a body posture monitoring, analysis, and correction prototype system was developed via embedding the fiber-based strain sensors into sweater, strongly indicating the great application prospect in exercise, sports and healthcare.

African swine fever virus (ASFV) is a large double stranded DNA arbovirus that is highly contagious and seriously endangers the lives of domestic and wild pigs. In the past decade, African swine fever (ASF) has spread in many countries in the Caucasus, Russian Federation, Eastern Europe and Asia, causing significant losses to the pig industry. At present, there is a lack of effective vaccine and treatment for ASF. Therefore, the rapid and accurate detection is crucial for ASF prevention and control. In this study, we have developed a portable lateral flow strip (LFS) detection method mediated by recombinase polymerase amplification (RPA) and DNA enzyme (LwCas13a), which is performed at 37 ℃ and visualized by eyes without the need for complex instruments. This RPA-LwCas13a-LFS is based on the ASFV structural protein p17 gene (D117L), with a detection sensitivity up to 2 gene copies. This method is highly specific and has no cross reactivity to 7 other pig viruses. In the detection of 33 clinical samples, the p17 (D117L) RPA-LwCas13a-LFS had 100% coincidence with conventional quantitative PCR (qPCR). These findings demonstrate the potential of this simple, rapid, sensitive, and specific ASFV detection method for on-site ASFV detection.

Subchondral bone loss is an important pathological feature of early-stage temporomandibular joint (TMJ) osteoarthritis (OA). Previous studies focused mainly on the bone resorption by osteoclasts in early-stage OA, but the bone formation feature has not drawn enough attention. Sema4D/Plexin-B1 is a pair of molecules expressed by osteoclast/osteoblast, which is capable of inhibiting bone formation by osteoblasts. The present study found that subchondral bone loss in early-stage TMJ OA was accompanied by up-regulated expression of Sema4D in cartilage and subchondral bone and Plexin-B1 in subchondral bone. Reducing Sema4D level could inhibit the subchondral bone loss and cartilage degeneration of early-stage TMJ OA. In vitro, results revealed that Sema4D could reduce the expression of osteocalcin (OCN) and alkaline phosphatase (ALP), and increase the migrating capability of Plexin-B1-positive osteoblasts. Our results revealed that elevated Sema4D expression in early-stage TMJ OA might decrease the bone formation activity of osteoblasts in the subchondral bone by binding to Plexin-B1 expressed by osteoblasts. Inhibiting Sema4D/Plexin-B1 signaling in the early-stage OA holds promise as a strategy for new therapeutic approaches to osteoarthritis.

An on-site, ultra-sensitive, and quantitative sensing method was developed based on quantum dot nanobeads (QDNBs) and test strip for the determination of total aflatoxins (AFTs) in rice and peanut. The monoclonal antibody against AFT (mAbAFT) was home-made and labeled with QDNB. After the pre-coating of the AFT antigen on the test line (T line), the competitive immunoreactions were conducted between AFT and AFT antigen on the T line with QDNBs-mAbAFT. Under optimal conditions, this approach allowed a rapid response towards AFT with a considerable sensitivity of 1.4 pg/mL and 2.9 pg/mL in rice and peanut matrices, respectively. The put-in and put-out duration were within 10 min. The recoveries for AFT in rice and peanut samples matrices were recorded from 86.25–118.0% with the relative deviations (RSD) below 12%. The assay was further validated via the comparison between this QDNB strip and the conventional HPLC method using spiked samples. Thus, the design provided a potential alternative for on-site, ultra-sensitive, and quantitative sensing of AFT that could also be expanded to other chemical contaminants for food safety.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

Accurate forecast of tail water level (TWL) is of great importance for the safe and economic operation and management of hydropower stations. The predictive performance is significantly influenced by the backwater effect of downstream hydropower stations and tributaries, but the explicit quantification method of the backwater effect is lacked. In this study, a deep learning model based forecasting framework for TWL predictions is established and applied to forecast TWL of Xiangjiaba (XJB) hydropower stations, which is influenced by the backwater effect of downstream tributaries including Hengjiang River (HJR) and Minjiang River (MJR). Firstly, the lag time of the backwater effect of HJR and MJR is analyzed based on the permutation importance. The results demonstrate that the lag time of backwater effect on the TWL of XJB is 5-7 hours for the HJR and 1-2 hours for the MJR. Then, the runoff thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained by scenario comparison, and the results show that the thresholds of HJR and MJR are 700 m3/s and 7000 m3/s respectively. Finally, the deep learning methods based TWL forecasting model is established based on the lag time and threshold analysis. The model is used to forecast the TWL in future 48 hours. The results show that the forecasting model has a good predictive performance with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm and the maximum absolute error is 63.35 cm.

Understanding the thermal cracks of rock caused by high temperature is of great help to the development of underground engineering, such as geothermal mining, underground coal gasification. Although there has been many study on thermal cracking, it is not systematic and in-depth enough. In order to deeply study the thermal crack mechanism of rock, this paper conducted thermal cracking experiments on granite at different temperatures and heating/cooling paths, and qualitative and quantitative analysis was conducted on the crack initiation characteristics, propagation paths, and crack network morphology of rock thermal crack under different test conditions. The thermal crack mechanism was also analyzed from the perspectives of mineral petrology, fracture mechanics, thermodynamics, and other aspects. The research results show that there are two obvious mutation points in the study temperature range for samples with fast cooling paths (SF path: slow heating and fast cooling; FF path: fast heating and fast cooling), around 200-300 ℃ and 600 ℃, respectively, while for samples with slow cooling paths (SS path: slow heating and slow cooling; FS path: fast heating and slow cooling), there is only one mutation point around 600 ℃. The initiation positions of thermal cracking under all temperature paths are relatively similar, with intergranular crack located between particles at the edge of the sample or intercrystalline crack in the middle of feldspar or quartz aggregates. The initiation temperature of SF and FF path specimens is relatively low compared to SS and FS path specimens, and the number and size of cracks is small. The crack network structure formed by the SF path is the most complex, with the largest crack ratio and cumulative crack length; The crack network structure formed by the FF path is relatively complex, with a larger main crack size but relatively fewer secondary cracks; The FS and SS paths do not form a complex network with good connectivity. The process of thermal crack development can be divided into three stages: the development of small cracks, the joint development of main cracks and small cracks, and the connection of cracks into a network structure. The mechanism of thermal crack propagation is that the expansion and thermal conductivity between different crystals are different. The thermal stress caused by temperature gradient and the tension or shear stress caused by the inconsistent deformation of crystals form stress concentration in weak areas such as particles boundary, cleavage, and original cracks. Firstly, it causes some crystals with smaller strength or less rounded shape to crack, and when the combined stress is large, the cracks will gradually expand along the existing fine cracks. This also explains that the most main cracks of the SF and FF path specimens mainly surround some large mineral aggregates or between particles.

In this study, we design a new refractive index sensor based on a metal–insulator–metal waveguide coupled with a notched ring and vertical rectangular resonators. We use the finite element method to study the propagation characteristics of the sensor. According to the calculation results, the transmission spectrum exhibits a typical Fano resonance shape. The phenomenon of Fano resonance is caused by the coupling between the wide spectrum and narrow spectrum. In the design, the wide spectrum signal is generated by the vertical rectangular resonator, while the narrow spectrum signal is generated by the notched ring resonator. In addition, we varied the structural parameters of the resonators and filled the structure with media of different refractive indices to study the sensing properties. The maximum achieved sensitivity of the sensor reached 1071.4 nm/RIU. The results reveal potential applications of the coupled system in the field of sensors.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

In wideband radar systems, the performance of motion parameters estimation can significantly affect the performance of object detection and the quality of inverse synthetic aperture radar (ISAR) imaging. Although the traditional motion parameters estimation methods can reduce the range migration (RM) and Doppler frequency migration (DFM) effects in ISAR imaging, the computational complexity is high. In this paper, we propose a new fast non-searching motion parameters estimation method based on cross-correlation of adjacent echoes (CCAE) for wideband LFM signals. A cross-correlation operation is carried out for two adjacent echo signals, then the motion parameters can be calculated by estimating the frequency of the correlation result. The proposed CCAE method can be applied directly to the stretching system, which is commonly adopted in wideband radar systems. Simulational results demonstrate that the new method can achieve better estimation performances, with much lower computational cost, compared with existing methods. The experimental results on real radar data sets are also evaluated to verify the effectiveness and superiority of the proposed method compared to the state-of-the-art existing methods.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

In this paper, we propose an efficient numerical method to solve the diffusive logistic model with a free boundary which is often used to simulate the spreading of the new or invasive species. The boundary movement is tracked by the level-set method, where the Hamilton-Jacobi weighted essentially nonoscillatory (HJ-WENO) scheme is utilized to capture the boundary curve embedded by the Cartesian grids via the embedded boundary method. Then the radial basis function-finite difference (RBF-FD) method is adopted to the spatial discretization and the IMEX scheme is considered for time integration. A variety of numerical examples are carried out to demonstrate the evolution of diffusive logistic model in different initial boundaries.

The container relocation problem (CRP) is one of the important topics in container terminals, which aims to find the optimal sequence of movements to minimize the total number of relocations needed to retrieve all the containers within a bay according to a given specific layout and retrieval order. However, determining such an order may be difficult due to uncertainty. Various CRP solution methods proposed so far show a common pitfall concerning the complexity of the solving process, whose scale grows exponentially with the number of containers. Considering the retrieval operation process of the inbound containers in a bay consists of several operation rounds, this paper focuses on the real-time operation scenario of each round to minimize the actual number of relocations during the pick-up process, i.e., starting from the initial layout of the bay, we minimize the sum of the incremental number of blocking containers due to the layout change and the number of reallocated containers unrelated to the target container. We present a heuristic algorithm (called SPFH) based on the three rules (LL, MSS, and FSS), considering the adjustment of the pick-up order of containers. Numerical experiments on a set of instances generated from the literature are performed. The proposed methodology excels in both execution efficiency and solution quality and is capable of solving instances that were impossible to solve before. This also makes our methods appealing for being applied in practice. The source codes are available at https://github.com/zhou-sf/rt-scrp.

Diabetic kidney disease (DKD) is the major cause of chronic kidney disease (CKD) and increases the risk of cardiovascular events. Hemodynamic, inflammatory and metabolic factors, which share the convergent pathway of fibrosis, are considered to involve in the pathogenesis of DKD. In spite of the emerging of angiotensin receptors blockers (ARBs)/ angiotensin converting enzyme inhibitor (ACEI), sodium-glucose cotransporter 2 (SGLT2) inhibitors, and nonsteroidal mineralocorticoid receptors antagonists (NS-MRAs), current therapies still couldn’t arrest the progression of DKD. Glucagon like peptide 1 receptor agonists (GLP-1RAs) are promising agents to play renoprotective roles in slowing the progression of DKD. Based on the treatment of heart failure, combined use of drugs is recommended for DKD rather than single use. Unearthing the mechanisms underlying DKD is urgent to investigate the management of DKD. Here, we elaborate on the potential mechanisms and the current therapies of DKD. We also discuss the additional value of the combined use of these drugs for DKD to establish novel concepts of treatment.

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

To achieve user retention through multifactor synergy, Internet enterprises must reduce costs and increase efficiency and sustainable development. In response to the dilemma that Internet compa-nies are experiencing increasingly high user acquisition costs and serious user churn, this paper uses a sample of 46,695 user reviews of nine product series from Xiaomi Ecological Chain. Case studies and qualitative comparative analysis are used to explore the influence mechanisms of quality of experience, brand trust, and brand attachment on users' retention intentions. Our find-ings are as follows. (1) Brand attachment alone is not necessary for high user retention intention, but user perception, cognition, and brand trust are necessary. (2) Quality of experience positively impacts brand trust, attachment, and user retention intention. Therefore, improving user perception and cognition is critical in generating high user retention intention. (3) Five configuration paths can achieve high user retention intention, three configuration paths lead to low user retention intention, and there is an asymmetric relationship between these paths. Among them, the role of quality of experience-driven configuration paths in generating user retention intention is the most prominent. (4) In the substitution relationship between configuration paths, user perception, and cognition can substitute with brand trust and attachment. Our findings have important theoretical and practical implications for revealing the realization paths of high user retention intention in Internet companies and provide a new perspective for future research.

Fractures are present in hydrocarbon reservoirs of different porosity ranges. Seismic anisotropy has been widely used in fracture characterization and shown to be sensitive to background matrix porosities in theoretical studies. An understanding of the effects of background porosity on seismic anisotropy could improve seismic characterization in different fractured reservoirs. Based on synthetic rocks with controlled fractures, we conducted laboratory experiments to investigate the influence background porosity has on P wave anisotropy and shear wave splitting. A set of rocks containing the same fracture density (0.06) with varying porosities of 15.3%, 22.1%, and 30.8% were constructed. P and S wave velocities were measured at 0.5 MHz as rocks were water saturated. Results show that when porosity increased from 15.3% to 22.1%, P wave anisotropy and shear wave splitting exhibit slight fluctuations. However, when porosity continued to increase to 30.8%, P wave anisotropy declined sharply while shear wave splitting stayed nearly constant. The measured results were compared with predictions from equivalent medium theories. Qualitative agreements were found between theoretical predictions and measured results. In Eshelby-Cheng model, the increase of porosity reduces the fracture-induced perturbation in the fracture normal direction, resulting in a lower P wave anisotropy. In Gurevich(2003) model, the increase of porosity can reduce the compressional stiffness in parallel direction to a larger extent than that in perpendicular direction, thus leading to lower P wave anisotropy.

The study of fractures in the subsurface is very important in unconventional reservoirs since they are the main conduits for hydrocarbon flow. For this reason, a variety of equivalent medium theories have been proposed for the estimation of fracture and fluid properties within reservoir rocks. We experimentally investigated the feasibility of applying the Galvin model in fractured tight stones. For this proposal, three artificial fractured tight sandstone samples with the same background porosity (11.7% ± 1.2%) but different fracture densities of 0.00, 0.0312, and 0.0624 were manufactured. The fracture thickness was 0.06 mm and the fracture diameter was 3 mm in all the fractured samples. Ultrasonic P- and S-wave velocities were measured at 0.5 MHz in a laboratory setting in dry and water-saturated conditions in directions at 0°, 45°, and 90° to the fracture normal. The results were compared with theoretical predictions of the Galvin model. The comparison showed that model predictions significantly underestimated P- and S- wave velocities as well as P-wave anisotropy in water-saturated conditions, but overestimated P-wave anisotropy in dry conditions. By analyzing the differences between the measured results and theoretical predictions, we modified the Galvin model by adding the squirt flow mechanism to it and used the Thomsen model to obtain the elastic moduli in high- and low-frequency limits. The modified model predictions showed good fits with the measured results.

The purpose of this study is to examine the driving model of determinant factors that affects the performance of small and micro enterprises empirical evidence from Amhara Region, Ethiopia. The study used primary and secondary data from manufacturing, construction, urban agriculture, trade, and service entrepreneurs found in Bahirdar, Dessie & Gonder Cities using a purposive sampling technique. It also used the descriptive research design with a self-administered survey questionnaire. The Statistical analysis tools, SPSS and Amos, software were applied to analyze the data. Multiple regression model results revealed that access to credit, initial capital, working premises, industry category, market linkage, ICT adoption have a positive relationship and a major impact on the Amhara region's enterprise performance. Hence, the outcome variable, enterprise performance, is highly affected by all predicted variables. As the study finding shows, a lack of access to credit, , market linkage, working premises), initial capital, and information communication Technology problems are critical challenges of SMEs. The Regional Government, TVED Bureau, Micro Finance Institutions, , should take corrective action to provide sufficient credit and long-term loans to protect inflation. Moreover, this study proposed a sustainable cluster management approach that solves their challenges in an integrated way.

Evolutionary medicine argues that disease can arise because modern conditions do not match those in which we evolved. For example, a decline in exposure to commensal microbes and gastrointestinal helminths in developed countries has been linked to increased prevalence of allergic and autoimmune inflammatory disorders (the hygiene hypothesis). Accordingly, microbial and helminthic probiotic therapies have been explored as Darwinian treatments for these disorders. A further possibility is that loss of old friend commensals also increases the sterile, aging-associated inflammation known as inflammaging, that contributes to a range of age-related diseases, including cardiovascular disease, dementia and cancer. Interestingly, Crowe et al. (2020) recently reported that treatment with a secreted glycoprotein from a parasitic nematode can protect against murine aging by induction of anti-inflammatory mechanisms. Here, we explore the hypothesis that restorative helminth therapy would have anti-inflammaging effects. Could worm infections provide broad-spectrum protection against age-related disease?

The Capon algorithm can be applied to reprocess SAR images, resulting in super-resolution super-resolution reconstructed scenes with lower sidelobe levels. Base on this idea, we have proposed a processing chain of Super-Resolution Persistent Scatterer Interferometry (SR-PSI), to increase PS density. In this paper, we review the main aspect of SR-PSI. We also propose a revised robust Capon algorithm. The result of real-life Sentinel-1 (S-1) data is shown.

The identification of homogeneous flood regions is essential for regional flood frequency analysis. Despite the type of regionalization framework considered (e.g., region of influence or hierarchical clustering), selecting flood-related attributes to reflect flood generating mechanisms is required to discriminate flood regimes among catchments. To understand how different attributes perform across Canada for identifying homogeneous regions, this study examines five distinctive attributes (i.e., geographical proximity, flood seasonality, physiographic variables, monthly precipitation pattern, and monthly temperature pattern) for their ability to identify homogeneous regions at 186 gauging sites. We add an automatic component to enhance identification of homogeneous regions is proposed as an addition to the region of influence framework. Results are presented spatially for Canada to assess patterning of homogeneous regions. Memberships of two selected regions are investigated to provide insight into membership characteristics. Sites in eastern Canada are highly likely to identify homogeneous flood regions, while the western prairie and mountainous regions are not. Overall, it is revealed that the success of identifying homogeneous region is relevant to local hydrological complexities, to whether considered attribute reflects primary flooding mechanism, and to whether catchment sites are clustered in small geographic region. Formation of effective pooling groups affords the extension of record lengths across the Canadian domain (where gauges typically have <50 years of record), facilitating more comprehensive analysis of higher return periods floods need for climate change assessment.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Drought is among the most important abiotic stressors influencing food-crop production worldwide. Currently, drought-tolerant maize materials are rarely used for actual breeding because corn production primarily focuses on heterosis to generate desired varieties. In this article, we reviewed current work on assessing maize drought tolerance. We suggested that the development of enhanced screening techniques must clearly consider the connection between theory and application. We strongly recommend that agricultural scientists focus on translating the results of laboratory experiments into practical methods for improving crop productivity.

There are abundant coal, oil and natural gas resources in "Golden Triangle" energy region. Energy resources development, coal chemical industry, coal power and other industries play an important role in promoting regional economic growth, but facing the insufficient water supply. Under the condition of marginal equilibrium of water resources, the paper firstly analyzes the water utilization, and calculates the potential water-saving amount. Then, the paper constructs a cooperative game model and allocates the newly increased GDP by using the weight distribution method. Finally, the paper puts forward some policy suggestions to reduce the efficiency gap of water utilization among regions.

This paper studies the impact of fiscal subsidies on the sustainability of China’s rural pension system. We first provides an overview of China’s rural pension system and explains the formulas used to calculate the pension payments. We then examines how fiscal subsidies, in forms of basic pension, incentive pension, and matching subsidy, affect participation rates and individual contributions. Our study shows that the rural residents’ participation rates can be improved significantly by increasing basic pension or by providing incentive pension, but not by matching subsidy. However, none of these fiscal subsidies has significant effects on the amount of individual contributions. Overall, our results imply that incentive pension is an effective mechanism in encouraging rural residents to participate in the pension programs, but current level of matching subsidies are not sufficient enough to improve participation or increase contributions. Our study suggests the needs to increase the fiscal subsides in China’s rural pension system, and can provide useful implications in designing the effective pension system for rural residents.

Circulating tumor cells (CTCs) in the peripheral blood are the precursors to distant metastasis but the underlying mechanisms are poorly understood. This study aims at understanding the molecular features within CTCs in relation to their metastatic potential. Using in vitro CTC models, in which breast cancer cell lines are cultured in non-adherent conditions simulating the microenvironment in the blood stream, we found that suspension culture resulted in resistance to TNF-related apoptosis inducing ligand (TRAIL)-mediated cell death. Such a resistance was directly correlated with a reduction in surface and total levels of DR5 protein. In the non-adherent state, cells underwent rapid autophagic flux characterized by an accumulation of autophagosome organelles. Notably, DR5 was translocated to autophagosomes and underwent lysosomal degradation. Our data suggest that CTCs may evade TNF cytokine mediated immune surveillance through downregulation of DR expression. The data warrants further studies in cancer patients to find the status of DRs and other molecular features within primary CTCs in relation to disease progression or chemoresistance.

The major aim of this study was to investigate and explores the linkage between economic growth, electricity access, energy use and population growth in Pakistan. To check the variables stationarity, Augmented Dickey-Fuller (ADF) and Phillips-Perron unit root test was applied and an Autoregressive Distributed Lag (ARDL) bounds testing approach to co-integration was applied to investigate the dynamic causality link among the study variables. These tests shed light on the long-run connection among the variables; further, the results revealed that electricity access to population, electricity access to urban population, energy usage, population growth, and urban population growth had a significant impact on economic growth, while the electricity access to rural population and rural population growth has a negative impact on the economic growth in Pakistan. According to these findings, study commends that government of Pakistan pay further attention to increase its electricity production from different sources including, hydroelectric, solar, oil and gas and nuclear in order to fulfill the country’s demands. By using ARDL bounds testing approach, this study filled the literature gap regarding economic growth, electricity access, energy use and population growth in Pakistan.

In this paper we study the normal bases for Galois ring extension ${{R}} / {Z}_{p^r}$ where ${R}$ = ${GR}$(p^r, n). We present a criterion on normal basis for ${{R}} / {Z}_{p^r}$ and reduce this problem to one of finite field extension $\overline{R} / \overline{Z}_{p^r}=F_{q} / F_{p} (q=p^n)$ by Theorem 1. We determine all optimal normal bases for Galois ring extension.

The aim of this paper is to use the elementary method and the properties of the second kind Stirling numbers to study the congruence properties of a new sequence, which is closely related to Fubini polynomials and Euler numbers, and give some interesting congruences for it. This solves a problem proposed by the first author at an unpublished paper.

In this paper we investigate the optimal investment strategy for a defined contribution (DC) pension plan during the decumulation phrase which is risk-averse and pays close attention to inflation risk. The plan aims to maximize the expected constant relative risk aversion (CRRA) utility from the terminal wealth by investing the wealth in a financial market consisting of an inflation-indexed bond, an ordinary zero coupon bond and a risk-free asset. We derive the optimal investment strategy in closed-form using the dynamic programming approach by solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation. Our theoretical and numerical results reveal that under some rational assumptions, an inflation-indexed bond do has significant advantage to hedge inflation risk.

Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than simple moving average, which enables accurate reconstruction of high-frequency details such as sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D containing both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as opensource1 for other researchers to reproduce and verify our results.

Steel slag and GBFS are wastes generated during the steel and iron smelting process, characterized by their considerable production rates and extensive storage capacities. After grinding, they are often used as supplementary cementitious materials. However, the intrinsic slow hydration kinetics of steel slag-GBFS cementitious material (SGM) when exposed to a pure water environment result in prolonged setting times and diminished early-age strength development. The incorporation of modifiers such as gypsum, clinker, or alkaline activators can effectively improve the various properties of SGM. This comprehensive review delves into existing research on the utilization of SGM, examining their hydration mechanisms, workability, setting time, mechanical strengths, durability, and shrinkage. Critical parameters including the performance of base materials (water-to-cement ratio, fineness, and composition) and modifiers (type, alkali content, and dosage) are scrutinized to understand their influence on the final properties of the cementitious materials. The improvement mechanisms of various modifiers on properties are discussed. This the promotes resource utilization of industrial solid wastes and provides theoretical and technical support for the application of SGM.

To study the oil and gas distribution law in the slope area of the hydrocarbon-bearing basin, the distribution of the effective dominant path of sand bodies transporting oil and gas is a fundamental subject. In this paper, we introduced an effective way to predict such transport paths and applied the method to study the sand bodies of the lower first member of the Shahejie Formation (E3s1L) in the southern area of the Qikou Depression in the Bohai Bay Basin (Qinan). We first define the dominant hydrocarbon transport paths of the sand body as the superimposed area of the contiguously distributed area of the sand body and the paleotectonic ridges at the top of the formation where the sand body is located. Then we define the oil and gas supply area of the oil source fault as the overlapped area of the favorable transport path of oil source faults and the width of the associated fracture zone. Stacking the two areas, we can delineate the distribution of the effective dominant transport paths of the sand bodies. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the E3s1L Formation in the Qinan area are distributed throughout the region with better development in the west than in the east. The paths in the eastern part extend from north to south, while the paths in the western part extend from northeast to southwest, which is favorable for the convergence of oil and gas generated from the source rocks of the third member of Shahejie Formation (Es3) in the Northeastern main depression of Qikou and the formation of oil and gas accumulations in the l E3s1L Formation. The results are consistent with the fact that oil and gas discovered in the E3s1L Formation in the Qinan region are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible to predict the effective dominant path of sand bodies transporting oil and gas.

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC) and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA) and variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC+VCPA-IRIV+SVR (R2C=0.995, R2P=0.986, RPD=8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

The flower of Syringa pubescens Turcz. is a Chinese folk medicine and also used as flower tea for healthcare. The effects of five drying methods on the active compound contents, the antioxidant abilities, anti-inflammatory properties and enzyme inhibitory activities were evaluated. The plant materials were treated using shade-drying, microwave drying, sun-drying, infrared drying and oven drying. The seven active compounds were simultaneously determined by an HPLC method. Furthermore, the chemicals differences were assessed using Scanning electron microscopy, UV spectroscopy and infrared spectroscopy. The antioxidant capacities and protective effects on L02 cells induced by hydrogen peroxide were measured. The anti-inflammatory effects on lipopolysaccharide-induced RAW264.7 cells were investigated. The enzyme inhibitory activities were determined against α-amylase, α-glucosidase cholinesterases and tyrosinase. The results indicated that drying methods had significantly influences on the active compound contents and biological properties. In consideration of the contents of bioactive compound, the suitable drying methods were SHD, SD, OD and MD. The SHD sample exhibited stronger radical scavenging ability on DPPH, ABTS, hydroxyl and superoxide anions and higher reducing power compared with other samples prepared by SD, OD, MD and IRD. Moreover, SHD sample had strong protective effects against L02 cells damage induced by H2O2. SPF samples, especially SHD sample, significantly inhibited inflammatory factor secretion in LPS-stimulated RAW 264.7 cell. All studied SPF sample exhibited evidently inhibitory effects on the four enzymes. SHD sample possessed the highest inhibitory activity on α-glucosidase and α-amylase. While SHD and SD samples had the potential inhibitory effects on cholinesterases and tyrosinase. In consideration of active compound contents and biological activities, it was recommended that SHD and SD were applied for drying SPF at an industrial scale.

This paper proposes an active-reactive power collaborative scheduling model with cluster division for the flexible distributed energy resources (DERs) of smart building system to resolve the high complexity of centralized optimal scheduling of massive dispersed DERs in the distribution network. Specifically, the optimization objective of each cluster is to minimize the operational cost, the power loss cost, and penalty cost for the flexibility deficiency, and the second-order cone-based branch flow method is utilized to convert the power flow equations into the linearized cone constraints, reducing the nonlinearity and heavy computation burden of the scheduling model. Customized virtual battery models for the building-integrated flexible DERs are developed to aggregate the power characteristics of flexible resources while quantifying their regulation capacities with time-shifting power and energy boundaries. Moreover, a cluster division algorithm considering the module degree index based on the electrical distance and flexible balance contribution index is formulated for cluster division to achieve the information exchange and energy interaction in the distribution network with high proportion of building-integrated flexible DERs. Comparative studies have demonstrated the superior performance of the proposed methodology on the economic merits and voltage regulation.

The theory of electron spin has been proposed for a century, but the study of quantum effects in biological molecules is still in its infancy. Chirality-induced spin selectivity (CISS) is a very modern theory that can explain many biochemical phenomena. In this paper, we propose a new theoretical model based on CISS theory and quantum chemistry theory, which can well explain the theoretical explanation of the chiral selectivity of chiral proteins. Moreover, this theory can predict the spin state of corresponding chiral molecules. Taking L-DOPA and AADC enzyme as examples, this theoretical model solves the theoretical explanation of AADC enzyme's chiral catalysis selectivity problem and successfully predicts the spin state of L-DOPA and D-DOPA's valence electrons.

In this paper, a sensitivity sensor of dual-core photonic crystal fiber (DC-PCF) filled with magnetic liquid is introduced and investigated by the finite element method (FEM). To regulate the energy coupling involving the two cores, the magnetic fluid is filled into the pore between the two cores. To adjust the coupling between the supermodes in the DC-PCF, the refractive index of the magnetic fluid-filled air hole may change due to the external magnetic field. This specifically created a magnetic fluid-filled DC-PCF, the magnetic fluid-filled hole is not used as the core for energy transmission, thus avoiding transmission loss. The dip wavelength and the magnetic field displayed an excellent linear connection between 80 and 260 Oe, depending on the numerical data. The detection sensitivity of the magnetic field reached 515.75 pm/Oe at a short fiber length of 482 m. The designed magnetic fluid-filled DC-PCF has high sensitivity and small volume and has great application prospects in medical and industrial field magnetic field detection.

Acanthopanax senticosus(rupr. rt. Maxim.; AS) is a medicinal plant used in the clinical treatment of cerebrovascular diseases and central nervous system disorders, significantly improving blood lipid levels and endothelial cell function in patients with acute cerebral infarction. Isofraxidin, one of the active ingredients of AS, is the core of the medical effects, and its extraction and purification depend on organic solvents. Deep eutectic solvents (DESs) is a new green solvent, synthesized by intermolecular hydrogen bonding between hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA), which has non-toxic, high separation and purification efficiency, and environmentally friendly compared with traditional organic solvents. In this paper, DES was used for the extraction of isofraxidin from AS. The primary findings demonstrate that the DESs had a viscosity higher than that of ethanol, and even adding a tiny amount of water (about 10%) would trigger solvent redistribution, leading to a considerable reduction in solvent viscosity. In comparison to ethanol, the extraction rate of isofraxidin by DESs was 2-3 times higher. Thus, this work developed a new technique for using green extraction of isofraxidin that has some practical implications.

Objective: Identify factors associated with excessive day time sleepiness (EDS) in individuals with obstructive sleep apnea (OSA), and analyze the effects that obesity and gender have on excessive daytime sleepiness in such individuals. Methods: A total of 160 people were selected for this study. All the people have completed a clinical evaluation, and whose apnea-hypopnea index (AHI) > 10 events/hour of sleep on polysomnography were included in the study from the Department of otolaryngology. Results: The Mean age was 43.87±11.34 years, mean EDS score was 14.09± 4.91, and mean AHI was 43.88±20.66 events/hour of sleep. Male presented lower mean age, higher EDS scores, and more time in apnoea, whereas females presented with higher mean age, lower EDS scores, and less time in sleep apnea. The EDS score showed best correlation with duration of apnoea (r = 0.448; p < 0.01), peripheral oxygen saturation (SpO₂; r = -0.458; p < 0.01) and AHI (r = 0.484; p < 0.01). The mean body mass index (BMI) was 27.41±3.86 kg/m². Normal, Overweight, obese, and morbidly obese were observed in 20%, 61%, 18%, and 0.6% of cases, respectively. Severity of the disease best correlated with BMI (r = 0.421; p < 0.01). Conclusions: OSA is predominant in males (M/F 5:1), and obese population. Females diagnosed with OSA have higher mean age. However, EDS scores and time spent in sleep apnoea is lower in females. Higher BMI is associated with EDS, irrespective of gender.

Background: Strong indicators of inflammation, such as C-reactive protein (CRP), hypersensitive CRP (hs-CRP), and a series of hematological indices, including platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), hematocrit (HCT) and red blood cell distribution width (RDW), are regarded related with the incidence of contrast induced nephropathy (CIN) closely. Whereas, it remains unclear whether they can function as predictors of CIN onset. The objective of this meta-analysis was to determine the relationship between above indicators and CIN incidence among patients receiving coronary intervention. Methods: Clinical studies were retrieved from the electronic databases of PubMed, EMBASE, Google Scholar, Clinical Trials, and science direct from their inception to June 3rd, 2020. Meta-analysis was performed on pool eligible studies. Two reviewers screened all titles and abstracts and independently assessed all articles. Results: A total of 26 studies involving 29,454 patients were included in the meta-analysis. Pooled analysis results revealed that patients with higher CRP (odds ratio [OR]=1.06, 95% confidence interval [CI]: 1.01–1.12, P=0.02), hs-CRP (OR=1.03, 95% CI: 1.01–1.06, P=0.004), NLR (OR=1.11, 95% CI: 1.01–1.20, P=0.02), RDW (OR=1.35, 95% CI: 1.19–1.53, P<0.00001), and lower HCT (OR=0.94, 95% CI: 0.92–0.97, P=0.0003) all exhibited significantly higher CIN rates, but there was no significant association between PLR and CIN risk (OR=1.12, 95% CI: 0.99–1.26, P=0.07). Conclusion: The meta-analysis reported here demonstrates that pre-angiography CRP/hs-CRP and some hematological indices are associated with CIN.

This paper investigates the problem of the optimal arrangement for both UAVs’ caching contents and service locations in UAV-assisted networks based on the context awareness, which considers the influence between users and environment. In the existing work, users within the coverage of UAVs are considered to be served perfectly, which ignores the communication probability caused by line-of-sight (LOS) and non- line-of-sight (NLOS) links. However, the links are related to UAV deployment. Moreover, the transmission overhead should be taken into account. To balance the tradeoff between these two factors, we design the ratio of users’ probability and transmission overhead as the performance measure mechanism to evaluate the performance of UAV-assisted networks. Then, we formulate the objective for maximizing the performance of UAV-assisted networks as a UAV-assisted caching game. It is proved that the game is an exact potential game with the performance of UAV-assisted networks serving as the potential function. Next, we propose the log-linear caching algorithm (LCA) to achieve the Nash equilibrium (NE). Finally, related simulation results reflect the great performance of the proposed algorithm.