Search

Complex mapping relationships between high-entropy alloy compositions and performances struggle to precisely elucidate by traditional machine learning models, hindering the accurate and efficient design of high-performance alloys. In this work, a novel alloy design strategy combined self-decision deep neural network, data reconstruction with network structure optimization was proposed, which effectively enhanced the model's ability to predict the mapping relationships between high-dimensional composition spaces and mechanical properties of alloy. Compared to other machine learning methods, significantly improves model prediction accuracy (hardness model: 97.98%; strength model: 94.40%). Through above model, the mechanical properties of 308 new (CuNiMn)-X high-entropy copper alloys was studied added other elements such as Al, Ti, Cr, and Fe, which designed and produced a novel (CuNiMn)60Al20Cr20 alloy with high hardness of 474 HV, high compressive strength of 2322 MPa, and high fracture strain of 25.20%. The primary factors influencing alloy performance and their respective thresholds were also quantitatively revealed (hardness: electronegativity deviation (0.02, 0.1) and nuclear charge deviation (0, 0.25); compressive strength: mean nuclear charge (25, 30) and mean atomic radius (134 p.m.)). These findings provide a new perspective and approach for the design and mechanism understanding of high-performance alloys with complex compositions.

Twins play a crucial role in elucidating the structure and deformation characteristics of materials. However, in materials with low-symmetry crystal structures, solving the twin relationships remains a challenging task. In this study, we propose a general method for determining twin elements K1, η1, K2, and η2, which are essential for twin analysis. The proposed method involves three main steps: (1) representing misorientation data measured by electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM) in matrix form, (2) identifying additional 180° rotation elements related to twin elements K1 or η1 by traversing all symmetrically equivalent misorientations, and (3) using a minimum and crystal structure-dependent uniform shear as the constraint to determine the other twin elements (K2andη2). By combining the experimental data and the proposed method, we systematically analyze the complex twin relationships of Cu–Zn–Al shape memory alloys (SMAs) with monoclinic martensite (M18R). This methodology has been demonstrated to be effective and possesses broad applicability.

In this paper, we propose a novel optimization approach to designing wideband infinite impulse response (IIR) digital fractional order differentiators (DFODs) with improved accuracy at low frequency bands. In the new method, the objective function is formulated as an optimization problem with two tuning parameters to control the error distribution over frequencies. The gradient based optimizer (GBO) is effectively employed on the proposed objective function. A wide range of design examples are presented to illustrate the effectiveness of the proposed approach. The proposed approximations are compared to those of recent literature in terms magnitude, phase, and group delay errors. The result reveal that our method can attain approximations have a favorable low frequency performance (with about 60% of relative magnitude error reduction) and maintain a comparable accuracy at most of the Nyquist band to those of the existing ones. Thus, our approximations can be attractive for low frequency applications.

Power system planning, load forecasting, and energy utilization analysis are significantly impacted by power load anomalies, necessitating prompt detection and identification. Firstly, the abnormal classification, causes, and characteristics of power load data are analyzed. Secondly, it enhances the traditional Transformer encoder structure by replacing the mask multi-header attention layer with the multi-header attention layer and eliminating the feedforward network. These improvements aim to enhance the model's global attention to the load sequence. To capture trend characteristics more effectively and expedite convergence, an improved generative adversarial networks (GAN)-Transformer model is proposed based on the generator and discriminator game structure of traditional GAN. Additionally, a multi-stage mapping and training approach along with an integrated focus score scoring mechanism are introduced. These techniques facilitate phased load sequence reconstruction, enabling the model to better extract anomalous features of the load data. Finally, through an analysis of an arithmetic example, the results demonstrate that the GAN-Transformer model outperforms in terms of load data anomaly detection accuracy, recall, F1 value, and training time. These results validate the effectiveness and superiority of the proposed method. The findings of this research provide valuable insights for advancements in power load data anomaly classification and data repair based on deep learning.

In this study, Cu-2.7Ti-0.2Fe (CT-0.2Fe) and Cu-2.7Ti–1Fe (CT-1Fe) alloys were fabricated and underwent multi-stage thermomechanical treatment, and the microstructure and properties evolution of the alloys during aging were investigated. After aging for 60 h at 350 °C, the CT-0.2Fe and CT-1Fe alloys exhibited the yield strength of 1068/935 MPa, tensile strength of 1127/1057 MPa, elongation of 2.54%/2.21%, and electrical conductivity of 20.3/26.4 %IACS, respectively. The relatively higher electrical conductivity of CT-1Fe was primarily attributed to the formation of numerous TiFe particles. Typical modulated structure and β′-Cu4Ti precipitates formed during direct aging in both alloys. However, in the CT-1Fe alloy, the growth of β′-Cu4Ti precipitates was suppressed. During multi-stage thermomechanical treatment, the interaction of the dislocations and precipitates was enhanced, retarding the softening effects of the alloy at the elevated temperatures. The calculation results showed that work hardening and precipitation strengthening significantly contributed to enhancing the mechanical properties of the alloys.

A new Cu-1.5Ni-0.7Co-0.7Si-0.3Fe-0.1Mg alloy with low cobalt content was designed. Multi-stage thermo-mechanical treatment (MTT) was used to produce the high-performance alloy with hardness of 286 HV, yield strength of 858 MPa, tensile strength of 910 MPa and electrical conductivity of 45.4 % IACS. The addition of 0.3 wt% Fe element dissolved in (Ni,Co)2Si resulted in the formation of the orthorhombic δ-(Ni,Co,Fe)2Si. During MTT, the δ-(Ni,Co,Fe)2Si, (Ni,Co)2Si, β-Ni3Si, and Fe2Si precipitated successively near the deformation microstructure. Transmission electron microscope observations indicated that the δ-(Ni,Co,Fe)2Si precipitates showed disc-like and rod-like shapes at different orientations, respectively. A new resource-saving, high-strength and medium-conductivity Cu-Ni-Co-Si-Fe-Mg alloy was prepared by replacing an equal amount of Co element with 0.3 wt% Fe element, reducing the amount of scarce metal Co in the alloy.

Abstract Background Non-communicable diseases (NCDs) pose a major challenge to health economic cost and residents’ health status. Community health workers (CHWs) are the gatekeeper of primary health care. Objective This study aimed to conduct a situational analysis of current human resource and requirements of NCDs-related training among CHWs in Chengdu with regard to address to understand the suggestions for improvement of challenges and barriers. Methods A descriptive online cross-sectional survey was conducted among CHWs (doctors and nurses) from 23 districts and counties in Chengdu. Sociodemographic and NCDs-related variables were collected. Univariate analysis and multiple response analysis were used to describe the characteristics of these variables. Results 711 doctors and 637 nurses completely responded. There were significant differences among gender, age, educational levels, professional title, working year, type of institution, urban circle and registration in general practice between doctors and nurses (P

In this study, the effects of differences in grain size, grain boundary type and misorientation on the initiation of discontinuous precipitates are investigated for the Cu–5Ni–1.25Si alloy. Samples with different grain sizes are obtained by thermomechanical treatment, and the smaller the grain size, the higher the percentage of discontinuous precipitate at the same aging time, while there is no significant difference in the growth rate of the reaction front of discontinuous precipitates. EBSD is used to study the effect of grain boundary misorientation on discontinuous precipitates, the characteristics of discontinuous precipitates initiation on different grain boundaries are investigated by multivariate statistical analysis. It is found that discontinuous precipitates only initiate from random grain boundaries instead of coincident site lattice boundaries, where random grain boundaries with misorientation angles of 30–50° are more prone to discontinuous precipitates. And the initiation of DPs at different misorientation axes is different. There is a clear borderline for the axes such as , , and . The grain boundaries below this borderline do not initiate DP, while all grain boundaries below this angle initiate DPs. The cases for axes such as , , and are more complicated. On these misorientation axes, grain boundaries with lower or higher misorientation angles do not initiate DPs, while grain boundaries at intermediate range misorientation angles do initiate DPs.

Cellular senescence is a state of irreversible cellular growth arrest that occurs in response to various stresses. In addition to exiting the cell cycle, senescent cells undergo many phenotypic alterations, including metabolic reprogramming, chromatin rearrangement, and senescence-associated secretory phenotype (SASP) development. Furthermore, senescent cells can affect most physiological and pathological processes, such as physiological development; tissue homeostasis; tumour regression; and age-associated disease progression, including diabetes, atherosclerosis, Alzheimer’s disease, and hypertension. Although corresponding anti-senescence therapies are actively being explored for the treatment of age-associated diseases, the specific regulatory mechanisms of senescence remain unclear. N6-methyladenosine (m6A), a chemical modification commonly distributed in eukaryotic RNA, plays an important role in biological processes such as translation, shearing, and RNA transcription. Numerous studies have shown that m6A plays an important regulatory role in cellular senescence and aging-related disease. In this review, we systematically summarize the role of m6A modifications in cellular senescence with regard to oxidative stress, DNA damage, telomere alterations, and SASP development. Additionally, diabetes, atherosclerosis, and Alzheimer’s disease regulation via m6A-mediated cellular senescence is discussed. We further discuss the challenges and prospects of m6A in cellular senescence and age-associated diseases with the aim of providing rational strategies for the treatment of these age-associated diseases.

With the continuous development of Internet technology and the continuous expansion of network scale, new network services emerge in an endless stream. In order to ensure the quality of user service, accurate and rapid classification of application traffic is the focus of current research. The traditional service identification method is based on protocol or specific service classification, which is suffered from low applicability. Combining traffic characteristics and machine learning methods, this paper proposes a traffic identification method based on the fusion of Generative Adversative Network (GAN) and Extreme Gradient Lift Boosting (XGBoost). Firstly, the traffic characteristics representing service resource requirements. Then GAN algorithm was improved to expand a few class samples to solve the problem of low model accuracy caused by the unbalanced distribution of data sets in the process of application identification. Finally, the random forest algorithm was used to select the feature, and the XGBoost algorithm was used to complete the model training. The results show that the accuracy of this method is 97.32%.

As an important part of the power Internet of things, the distribution field area network is the "last mile" infrastructure of the power Internet of things. It can solve the communication problem of the last mile of the power Internet of things. In order to meet the high real-time and high reliability requirements of distribution field area network control services and improve the coverage and reliability of the network, a highly reliable and fast routing optimization algorithm based on Dijkstra algorithm is proposed. The field high-speed power line carrier and radio frequency communication methods of the field area network have complementary advantages. The nodes select the most reliable communication link to transmit data through the hybrid routing table, and consider the reliability and real-time constraints at the same time. The simulation results show that the high-reliability routing optimization algorithm of the distribution field area network can effectively increase the coverage capacity, the number of successfully transmitted services and the average routing reliability.

Introduction ST-segment elevation myocardial infarction (STEMI) with high thrombus burden is associated with a poor prognosis. Manual aspiration thrombectomy reduces coronary vessel distal embolisation, improves microvascular perfusion and reduces cardiovascular deaths, but it promotes more strokes and transient ischaemic attacks in the subgroup with high thrombus burden. Intrathrombus thrombolysis (ie, the local delivery of thrombolytics into the coronary thrombus) is a recently proposed treatment approach that theoretically reduces thrombus volume and the risk of microvascular dysfunction. However, the safety and efficacy of intrathrombus thrombolysis lack sufficient clinical evidence.Methods and analysis The intrAThrombus Thrombolysis versus aspiRAtion thrombeCTomy during prImary percutaneous coronary interVEntion trial is a multicentre, prospective, open-label, randomised controlled trial with the blinded assessment of outcomes. A total of 2500 STEMI patients with high thrombus burden who undergo primary percutaneous coronary intervention will be randomised 1:1 to intrathrombus thrombolysis with a pierced balloon or upfront routine manual aspiration thrombectomy. The primary outcome will be the composite of cardiovascular death, recurrent myocardial infarction, cardiogenic shock, heart failure readmission, stent thrombosis and target-vessel revascularisation up to 180 days.Ethics and dissemination The trial was approved by Ethics Committees of China-Japan Friendship Hospital (2022-KY-013) and all other participating study centres. The results of this trial will be published in peer-reviewed journals.Trial registration number NCT05554588.

Objective: The preventive and therapeutic effects of the aqueous extract of Ficus pumila on COPD model rats were observed and its mechanism was also discussed. Methods: COPD model was established by intratracheal injection of lipopolysaccharide and smoking. Using the pulmonary function test, the level of related inflammatory factors detection in the BALF by ELISA, the blood gas analysis and HE staining for lung pathological observation. Results: The results showed that the indexes of the model rats were consistent with the pathological characteristic of COPD. The model group animals had obvious airflow obstruction, while the high dose of F. pumila aqueous extract group could significantly increase the levels of FVC, Fev0.3, PEF, Fev0.3/FVC （P2 and CO2 in the lungs of the model rats; At the same time, the high dosage of Ficus pumila aqueous extract can significantly improve the pulmonary ventilation function of COPD, and the effect was better than positive drug. Medium dose-, high dose aqueous extract group and positive drug group could significantly reduce the number of leukocytes （P

Improving high-temperature strength and resistance to high-temperature softening is an important method to promote the application of high-performance Cu-Cr-Zr alloy in fields such as resistance welding electrodes and high-speed railway contact wires. A Cu-1.0Cr-0.4Zn-0.1Zr-0.05Si alloy was designed and the combining effects of Zn and Si elements on the microstructure and high-temperature mechanical properties of the alloy were studied. The tensile strength of the alloy at room temperature was 556 MPa, and it was 349 MPa at 500℃ with a softening temperature of 620℃. The main strengthening phases of the alloy were submicron Cr3Si and nano-scaled Cr-rich precipitates. The Zn elements were uniformly solid-solved in the Cu matrix, and the addition of Zn and Si elements significantly retarded the phase transformation of the Cr-rich precipitates. Thermodynamics and kinetics analysis showed that Zn and Si elements promoted the dispersive precipitation of the nano-scaled FCC coherent Cr-rich precipitates by reducing the nucleation energy barrier, while the Si and Zr elements inhibited the coarsening of the Cr-rich precipitates by enriching at the phase boundaries, effectively impeding dislocation motion and grain boundary migration, which mainly contributed to good high-temperature strength and resistance to softening of the Cu-Cr-Zn-Zr-Si alloy.

With the advantages of flexible parking locations and convenient cycling, Dockless Bicycle-sharing (DBS) has become increasingly popular worldwide. However, with the massive increase of DBSs and electric fences, DBS systems face several challenges: (1) the hardness of identifying the DBS tidal zones; (2) the difficulty of accurately evaluating and identifying overload fences; (3) the issues of rebalancing DBS in time. To deal with these challenges, we propose a Dockless Bicycle-sharing Dynamic Rebalance (DBSDR) system to dynamically provide the optimal bicycle guidance for the DBS network. The DBSDR system contains three modules: DBS tidal zone identification, evaluation framework of overload fences, and DBS dynamic guidance. For DBS tidal zone identification, tidal zone identification and location from each fence with bicycle flows are provided with the HDBSCAN clustering method. The evaluation framework, covering DBS flows and the parking demand density, is proposed to assess the characteristics of overload fences. Finally, a DBS dynamic guidance method is provided to balance DBS for the tidal phenomenon with guiding users to the optimal target fence. Extensive experiments conducted on real-world DBS datasets show the effectiveness and accuracy of rebalancing the tidal phenomenon in the DBS system.

Cupronickel alloys have a variety of applications in marine engineering due to their good mechanical and corrosion properties. In this work, the effect of Cr on the corrosion behavior of Cu–10Ni–1.6Fe–0.5Mn alloy in 3.5 wt% NaCl solution was investigated through electrochemical and surface characterization. The results indicate that Cu–10Ni–1.6Fe–0.5Mn–0.3Cr alloy has better corrosion resistance performance compared to Cu–10Ni–1.6Fe–0.5Mn alloy in NaCl solution. The addition of Cr reduces the corrosion current density of cupronickel alloys from 7.82 μA/cm2 to 7.10 μA/cm2. The better corrosion resistance performance of the Cu–10Ni–1.6Fe–0.5Mn–0.3Cr alloy could be mainly attributed to the incorporation of Cr3+ ions in Cu2O lattice during corrosion. The corrosion product film of Cu–10Ni–1.6Fe–0.5Mn–0.3Cr alloy mainly contains the compounds including Cu2O, NiO, γ-FeOOH, Cr2O3, and Cu2(OH)3Cl. The formation of Cr2O3 can further enhance the stability of corrosion film and improve the corrosion properties of the alloy.

BackgroundThe contracted family doctor services are the embodiment of the implementation of the new medical reform policy, and the transformation of the grass-roots health service mode. Studies have proved that the occupational stress in medical staff was at a high level. The enhancement of professional identity will contribute to strengthen team building，alleviate job burnout, and reduce turnover intention of family doctors. ObjectiveTo investigate the current situation of occupational identity among family doctor teams in Chengdu, to examine potential influencing factors of occupational identity, and to provide a reference for promoting career development and team building of family doctor teams. MethodsMulti-stage random cluster sampling was adopted to enroll study participants form 46 primary healthcare centers where family doctor contract services were implemented among 23 districts and counties in Chengdu between March 4 and 26, 2021. A total of 2 681 family doctors participated in this survey. A self-reported survey was conducted to collect participants' demographic and occupational data. The Effort-Reward Imbalance (ERI）questionnaire was implemented to assess occupational stress. The Professional Identity Scale was used to appraise occupational identity. ResultsA total of 2 327 valid questionnaires were collected, with a valid recovery rate of 86.80%, involving 1 715 females (73.7%) and 612 males (26.3%), with dominant age groups of 26−35 years (43.3%) and 36−45 years (30.4%), a high proportion of being married (82.8%), having college (36.0%) and undergraduate (47.3%) education, a high proportion of primary titles (66.0%) and informal work contract (66.1%). About 88.7% of family doctor team workers reported occupational stress. The average score of occupational identity was (3.68±0.62) points. There were significant differences in occupational identity scores among different professional title, work contract, working years in medical institutions, income, and effort/reward ratio (EER) groups (P < 0.05). ERR was negatively correlated with occupational identity (rs=−0.495, P

60NiTi alloy is an important biomedical, structural and functional material. In this paper, the effects of heat treatment (temperature, time and cooling rate) on the microstructure and properties of 60NiTi alloy were studied, and the softening and hardening mechanisms were revealed. The results showed that the number of Ni4Ti3 phase enhanced with an increase of the cooling rate and the precipitation of Ni3Ti phase was inhibited after annealing at 950∼1075 °C followed by water-cooling. The orientation relationship between NiTi matrix and Ni4Ti3 phase obey the rule: [111]NiTi//[0001]Ni4Ti3 and (123¯)NiTi//(112¯0)Ni4Ti3. The faster the cooling rate was, the higher the hardness of the alloy was. The hardness of alloy under different cooling rate ranked from high to low was water-cooling, oil-cooling, liquid nitrogen-cooling, air-cooling and furnace-cooling. The hardness of the 60NiTi alloy was 316 HV after annealing at 1075 °C for 4 h followed by furnace-cooling, and the hardness sharply increased to 669 HV after annealing at 1075 °C for 4 h followed by water-cooling. The phase transformation of Ni4Ti3→Ni3Ti during cooling mainly contributed to the hardness decrease of the 60NiTi alloy.

A Cu-9Ni-6Sn alloy bar with axial columnar grains was prepared by directional solidification. The effects of aging on the microstructure and mechanical properties of the alloy were studied. The results showed that the alloy had columnar grains oriented along the axis and underwent continuous cold-drawing with a total deformation amount of 85%, a fibrous structure along the axis was formed. For the homogenized alloy, the tensile strength is 385 MPa, the hardness is 80 HV, and the elongation after fracture can reach 38.5%. After aging at 360 °C for 120 min, (Cu, Ni)3Sn phase precipitated intermittently along the grain boundary, and the tensile strength and elongation of the alloy were 1057 MPa and 7.1%, respectively. With increasing aging time to 240 min, the number of (Cu, Ni)3Sn phase increased and coarsened obviously, forming a precipitation-free zones, with occurrence of recrystallization of the Cu matrix. The preparation of Cu-9Ni-6Sn alloy billet by directional solidification, combined with large-deformation drawing and aging, is a new method to prepare high performance Cu-9Ni-6Sn alloy wire.

With the development of 5th Generation Mobile Communication Technology (5G) and the evolution of network architecture, the analysis and optimization of network coverage need to consider comprehensive factors, including not only the link budget of the base station antenna system, but also the geographical conditions and time characteristics of the area covered by the base station. Therefore, a more accurate network coverage optimization plan should be designed. This paper proposes an adaptive network coverage optimization algorithm based on Q-learning. The method first adopts a cellular network coverage prediction model based on data mining, which can predict the coverage situation of the access terminal through the configuration of the antenna of the cell, and verify the accuracy of the prediction based on real data. Then, a network coverage optimization algorithm based on Q-learning is proposed which modifies the action selection strategy of the agent in reinforcement learning process. According to the coverage of each cell, different optimization priorities are set. Combined with the greedy strategy, a cell and its antenna parameters are decided by the agent in each iteration. This method effectively reduces the probability of falling into a local optimum during the iteration process, and the method also has a good performance in reducing the convergence time of the optimization process. The simulation result shows that the algorithm can increase the network coverage by up to 20%.

Abstract Background Family doctor contract services was launched in Sichuan province in 2016. The focus was mainly on developing primary health care services but paying less attention to the work stress and job satisfaction of in-service family doctors. Objective This study aims to explore the influencing factors of job satisfaction, and the relation between work stress indicators and job satisfaction among family physicians. Methods An analytical online cross-sectional survey was performed among 1,105 family doctors from 23 districts and counties in Chengdu. Self-administered questionnaire was completed. Sociodemographic factors, work stress measured by Effort-Reward Imbalance (ERI)scale, and job satisfaction assessed by the short Chinese version of the Minnesota Satisfaction Questionnaire (MSQ) were collected in this study. A statistical analysis and hierarchical linear regression analysis were performed to explore the influencing factors and the correlations among related variables. Results The overall mean MSQ score was 52.01 ± 13.23. Analysis of doctor satisfaction indicated that age, education, job rank, type of institution, years of working and monthly income were statistically significant (P

Historically, central banks in the United States, the United Kingdom, Australia and Japan have implemented Yield Curve Control (hereinafter referred to as YCC), and Japan is currently the only country in the world that is still pushing ahead with YCC. However, given that the implementation and implementation of the YCC showed more defects than profits, on June 15, 2022, there was an unusual fluctuation in the Japanese government bond futures market, Japan’s 10-year government bond future contracts fell by 2.01 yen, touching the melting point in the plate, the largest single-day drop in 2013. This article introduces the basic framework of the yield rate curve and its way of buying bonds, then elaborates on the operation of the Central Bank of Japan to buy bonds in 2022, analyses the abnormal fluctuations in the Japanese government bond futures market in 2022 and identifies the contradictions and limitations of the government bond market as the root cause of the fluctuation.

In the original publication [...]

Abstract Background Sucrose non-fermenting 1 (SNF1)-associated protein kinase 2 (SnRK2) proteins belong to a relatively small family of plant-specific serine/threonine (Ser/Thr) protein kinases. SnRK2s participate in the abscisic acid (ABA) signaling pathway and play important roles in many biotic and abiotic stresses. At present, no SnRK2 gene has been reported in quinoa, and the recently published genome for this species provides an opportunity to identify and characterize the SnRK2 gene family. Results We identified 13 SnRK2 genes in the C. quinoa genome by bioinformatics analysis. Based on their phylogenetic relationships, these genes were divided into three subfamilies, similar to the situation in other plant species. Gene duplication analysis showed that there were seven pairs of homologous genes in the CqSnRK2 family, and that purifying selection played an important role in the evolution of SnRK2 genes. Gene structure analysis showed that the first exon in the SnRK2 family genes has the same length as the last exon, and that CqSnRK2 genes in the same subfamily have similar gene structures. Sequence analysis showed that the N-terminal region contains three highly conserved motifs. In addition, many kinds of cis-elements were identified in the promoter region of CqSnRK2, including those for hormone responses, stress responses, and tissue-specific expression. Transcription data analysis and qRT-PCR results showed that CqSnRK2 has different expression patterns in roots, stems, and leaves, and responded to biotic and abiotic stresses such as low temperature, salt, drought, and abscisic acid (ABA). In addition, we found that the protein encoded by CqSnRK2.12 was localized to the cytoplasm and nucleus, and there was no self-activation. The results of CqSnRK2.12 overexpression showed that transgenic Arabidopsis thaliana lines had increased drought tolerance compared to the controls. Conclusion The results of our study provide references for further studies on the evolution, function, and expression of the SnRK2 gene family in quinoa.

Exosomes were enriched from plasma by ultracentrifugation, precipitation, and membrane-based approaches for yield and purity. Using the four isolation approaches, particles with mode sizes within the expected range (50–200 nm) can be isolated. By protein estimation, polymer precipitation resulted in a maximum yield (5610.59 ± 51.189 µg/mL), followed by membrane affinity (471.57 ± 12.16 µg/mL), ultracentrifugation (440.22 ± 11.71 µg/mL) and filter + ultracentrifugation (235.47 ± 13.27 µg/mL). By total RNA estimation, the yield of polymer precipitation (3.26 ± 0.42 ng/mL) was higher than that of ultracentrifugation (1.52 ± 0.06 ng/mL), filter + ultracentrifugation (1.21 ± 0.25 ng/mL) and membrane affinity (1.44 ± 0.14 ng/mL). The purity of exosomal preparations was determined as the ratio of the particle number to protein and of protein to RNA. According to the ratio of the particle number to protein concentration, the “purity” of the polymer precipitation method was similar to that of the membrane affinity method and higher than that of ultracentrifugation and filter + ultracentrifugation. When the ratio of RNA to protein was used, the “purity” of the polymer precipitation method was lower than that of the membrane affinity method. Differential methods can be employed to enrich specific exosome subpopulations. The steps of the methods affect the particle number, protein content, and even exosomal purity. The best extraction and evaluation methods for exosomes need to be selected in the laboratory according to their experimental needs.

Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector.

In order to improve the viscoelasticity of waste rubber powder （WCR） modified asphalt, WCR/C9 petroleum resin （C9PR） composite modified high viscosity asphalt binder and its mixture were prepared, the physical properties and storage stability of the modified high viscosity asphalt binder were investigated. At the same time, the properties of its OGFC-13 mixture were evaluated by general road performance test, dynamic creep test, Overlay test and indirect tensile fatigue test, and it was compared with common WCR modified asphalt and commercial high viscosity asphalt binder and their mixture. The results showed that WCR/C9PR high viscosity asphalt had the advantages of high softening point and viscosity, good storage stability and elastic recovery, and its performance was much better than that of common WCR modified asphalt. WCR/C9PR high viscosity asphalt mixture had good high and low temperature performance, water stability, shear deformation resistance, reflection crack resistance and fatigue crack resistance, which was comparable to that of commercial high viscosity asphalt mixtures and could meet the technical index requirements of porous pavements.

BACKGROUND Environmental geochemical conditions affect the quality of famous and special agricultural products. Xinken lotus root is the national product of geographical indication. Exploring the relationship between the geological background of the production area and the quality of lotus root is of great significance to the large-scale planting of Xinken lotus root. OBJECTIVES To reveal the correlation between the quality of lotus root and the environmental geochemical characteristics in the producing area. METHODS Sediment, surface water and fresh lotus root in the Xinken area were systematically sampled and analyzed. RESULTS The concentrations of nutrients, i.e., Mn, Zn, Mo, Co, V and Fe, in the sediment of the lotus root pond were high, in the first grade (rich) level. Selenium was mainly in proper amounts and high selenium grade. The concentrations of Cr, Cu, Hg, Ni, Pb and Zn were lower than those of the soil pollution risk threshold of agricultural land. Cu, Zn, Se, B, Hg, Cd, As, Cr(Ⅵ), Pb and Ni in surface water of the lotus pond met the requirement for irrigation water quality. The lotus root was rich in starch, soluble sugar, K, P, Ca, Mg, Fe, Zn and Se, and the contents of heavy metals and crude fiber were low. The average bioaccumulation coefficients of lotus root for different elements ranged from 0.0484 to 65.67. The enrichment ability of P was the strongest and that of Ge was the weakest. There was a significant positive correlation between B and starch in lotus root pond sediment (p ≤ 0.05), between Ca and protein, while a significant negative correlation between As and soluble sugar. The contents of B, Co, Fe, Mg, Mn, V, Ca and Ge in the sediment of the lotus pond were high, which was beneficial to the accumulation of nutrients in lotus root, and thus production of safe and high-quality lotus roots. CONCLUSIONS Importance should be attached to the supplement of organic matter, Ca, N and Ge in the lotus pond during the planting process and more attention to the potential ecological security risks caused by Cd and As.

The emergence of big data is breaking the spatial and time limitations of urban waterlogging scenario description. The scenario data of different dimensions (e.g., administrative levels, sectors, granularities, and time) have become highly integrated. Accordingly, a structural and systematic model is needed to represent waterlogging scenarios for more efficient waterlogging response decision-making. In this article, a full-view urban waterlogging scenario is first defined and described from four dimensions. Next a structured representation of scenario element is given based on knowledge unit method. The full-view scenario model is then constructed by extracting the scenario correlation structures between different dimensions (called scenario nesting), i.e., inheritance nesting, feedback nesting, aggregation nesting, and selection nesting. Finally, a real-world case study in Wuhan East Lake High-tech Development Zone, China is evaluated to verify the reasonability of the full-view model. The results show that the proposed model effectively integrates scenario data from different dimensions, which helps generate the complete key scenario information for urban waterlogging decision-making. The full-view scenario model is expected to be applicable for other disasters under big data environment.

Objective: This study aimed to eslablish a method for the determination of 15 benzodiazepine sedatives in aquatic products by liquid chromatography tandem mass spectrometry （HPLC-MS/MS）. Methods: The sample was extracted with acetonitrile solvent, and the extract was purified by solid phase extraction （Oasis PRIME HLB） column. After concentration, the mobile phase was used to determine the volume. The mobile phase consisted of 1% formic acid aqueous solution and acetonitrile solvent. The method was performed in multiple reaction monitoring （MRM） mode and quantified by internal standard method. Results: The results showed that the linear range of 15 sedatives was 1~100 ng/mL （R＞0.99）. The limit of detection （LOD） was 0.1~0.3 μg/kg and the limit of quantification （LOQ） was 0.3~1.0 μg/kg. The average recoveries were 78%~125% and the relative standard deviations （RSD） were 1.52%~12.02%. Conclusion: The method has the advantages of simple pretreatment, good purification effect and accurate results. It is suitable for the rapid detection of 15 kinds of sedatives in aquatic products.

Objective: This study aimed to clarify the content and distribution of polyphenols in fruit juice samples and explore the intrinsic connection of the content of polyphenols with the taste of fruit juice. Methods: 15 kinds of polyphenols in fruit juice samples were determined by HPLC-MS/MS following ethanol ultrasonic-assisted extraction. Various statistical methods such as correlation and cluster analysis, were used to establish the sensory evaluation system of fruit juice with the values of different tastes, including sweetness, sourness, bitterness, salty, umami, astringency and piquancy. Lastly, the relationship between the content of polyphenols and sensory evaluation was explored by correlation analysis. Results: 14 types of fruit juices were divided into four categories owing to their taste, namely sweet, sour-sweet, bitter and bitter-astringent. There was significant positive relationship between the content of total polyphenols, phenolic acids, flavonoids, chlorogenic acids, free phenolic acids and value of astringency. Conclusion: The contents of polyphenol varied greatly in different fruit juices, and those with bitter and astringent taste often had high content of polyphenol. However, those with sweet taste had low content of polyohenol. Polyphenolic compounds, especially the free phenolic acids were the key substances affecting taste of astringency of fruit juice.

Objective: The Polygonatum cyrtonema was processed to investigated its effects on material and energy metabolism in CUMS depressed model mice. Methods: 80 KM/SPF mice were randomly divided into model group, positive group, processed P. cyrtonema group and compatible group, with male and female were equally divided. CUMS depressed mice model were established by unpredictable mild stress for 28 days. The levels of ICD, α-KGDHC, ATPs and Na+-K+ ATPase in brain, cerebellum, liver, kidney and spleen were detected by ELISA. Results: Processed P. cyrtonema and its compatibility had different effects on the enzyme levels of material and energy metabolism in both male and female mice. Compared to the positive group, each treatment group was affected significantly (P＜0.05). P. cyrtonema could significantly increase the α-KGDHC content in brain, cerebellum, liver, kidney and spleen of female mice (P＜0.01), and complex differences in levels of ICD α-KGDHC, ATPs and Na+-K+ ATPase in different organs of male and female mice were observed. Conclusion: Processed P. cyrtonema and its compatibility had different effects on the enzyme levels of marerial and energy metabolism in both male and female mice. P cyrtonema group had the strongest effect on depressed mice.

Copper and iron are the basic metal elements that have attracted much attention in industry. Prussian blue (PB) is a significant class of metal–organic frameworks (MOFs); however, the lack of such linkages between the structure and properties, as well as properties differences, limits their potential applications. In this paper, the Cu-based Prussian blue nanocubes with and without Fe doping were synthesized. With the increasing reaction time, the morphology of the Cu-based Prussian blue nanocubes without Fe doping (PB:Cu NCs) changes from cuboidal to circular, and finally grows back to cuboidal. However, Cu-based Prussian blue nanocubes with Fe doping (PB: CuFe NCs) grow directly from the cube and eventually collapse. The nanocubes show a notable red shift with the tunable spectra from 400 nm to 700 nm. Compared with PB: Cu NCs, the PB: CuFe NCs have higher temperature rise under 808 nm irradiation and better photothermal efficacy. The catalytic efficiency of PB: CuFe NCs changes with the pH and reaches its maximum value of 1.021 mM with a pH of 5.5. The enhanced catalytic reaction by the near-infrared radiation plasmonic photothermal effect is also confirmed. This work highlights the potential of the developed PB: Cu and PB: CuFe NCs for photothermal-enhanced co-catalysis nanomaterials.

The magnetic functionalized graphene based 17β-estradiol molecularly imprinted composite membrane (GO@Fe3O4-MIP) was successfully prepared by reversible addition fragmentation chain transfer (RAFT) polymerization technique using GO@Fe3O4 as carrier and 17β-estradiol as template molecule. High resolution transmission electron microscopy (HRTEM) and atomic force microscope(AFM), vibrating sample magnetometer(VSM) were employed to characterize the as-prepared samples. The results show that the calculated magnetic property of GO@Fe3O4-MIP is 12.89 A·m2·kg-1 and the thickness is about 6.45 nm. The adsorption equilibrium time of GO@Fe3O4-MIP on 17β-estradiol is 30 min, the maximum adsorption capacity is 28.62 mg/g. These findings confirm that GO@Fe3O4-MIP has high adsorption efficiency and good adsorption selectivity for 17β-estradiol.

Compared with traditional lithium-ion batteries, all-solid-state lithium-metal batteries (ASS LMBs) have attracted much attention due to their high safety and high energy density. However, the solid-solid contact of electrodes with solid electrolyte brings large interfacial impedance. Besides, the active lithium metal reacts with most solid electrolytes,leading to unstable interface. So interfacial problem has been one of the key issues to limit the development of ASS LMBs. Solid composite electrolytes with the advantages of inorganic solid electrolytes and organic solid electrolytes, have higher ionic conductivity and certain mechanical strength, showing excellent promising in the light of practical application. The research progress on interface amelioration of solid composite electrolyte and lithium anode was summalized in this review. The main strategy includes constructing a "soft contact" at the interface, adjusting the mechanical performance of the solid electrolyte and tuning the deposition dynamics of lithium ions. Furthermore, the perspectives on interfacial modification were presented.

Cu-Cr-Nb alloys have attracted many researchers’ interest for decades while their structure and second phases inside were unclear. We have characterized the second phase particles formed during solidification, hot rolling, and aging treatments in a Cu-4.06Cr-1.25Nb alloy. Face-centered-cubic (FCC) Cr, L12 Cr3Cu, C14 Cr2Nb, C15 Cr2Nb, and Nb-rich particles were detected in the alloy. The crystal structure, precipitation, phase transformation behavior and orientation relationship (OR) were uncovered. C15 Cr2Nb particles formed in the solidification process while C14 Cr2Nb particles precipitated as aged at 600 °C. The competition of nucleation between nano-scale Cr3Cu and Cr2Nb particles aged at 600 °C was discussed. The nucleation of L12 Cr3Cu was prior to that of C14 Cr2Nb, and the precipitation of Cr2Nb particles would limit the coarsening of Cr particles. A phase transformation map during age treatment was established on the basis of transmission electron microscope (TEM) images and X-ray diffraction (XRD) patterns. These findings are helpful in developing high-performance Cu-Cr-Nb alloys.

Object detection in images taken by unmanned aerial vehicles (UAVs) is drawing ever-increasing research interests. Due to the flexibility of UAVs, their shooting altitude often changes rapidly, which results in drastic changes in the scale size of the identified objects. Meanwhile, there are often many small objects obscured from each other in high-altitude photography, and the background of their captured images is also complex and variable. These problems lead to a colossal challenge with object detection in UAV aerial photography images. Inspired by the characteristics of eagles, we propose an Eagle-YOLO detection model to address the above issues. First, according to the structural characteristics of eagle eyes, we integrate the Large Kernel Attention Module (LKAM) to enable the model to find object areas that need to be focused on. Then, in response to the eagle’s characteristic of experiencing dramatic changes in its field of view when swooping down to hunt at high altitudes, we introduce a large-sized feature map with rich information on small objects into the feature fusion network. The feature fusion network adopts a more reasonable weighted Bi-directional Feature Pyramid Network (Bi-FPN). Finally, inspired by the sharp features of eagle eyes, we propose an IoU loss named Eagle-IoU loss. Extensive experiments are performed on the VisDrone2021-DET dataset to compare it with the baseline model YOLOv5x. The experiments showed that Eagle-YOLO outperformed YOLOv5x by 2.86% and 4.23% in terms of the mAP and AP50, respectively, which demonstrates the effectiveness of Eagle-YOLO for object detection in UAV aerial image scenes.

Cu–9Ni–6Sn alloys have received widespread attention due to their good mechanical properties and resistance to stress relaxation in the electronic and electrical industries. The hot compression deformation behaviors of the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy were investigated using the Gleeble-3500 thermal simulator at a temperature range of 700–900 °C and a strain rate range of 0.001–1 s−1. The microstructural evolution of the Cu–9Ni–6Sn alloy during hot compression was studied by means of an optical microscope and a scanning electron microscope. The constitutive equation of hot compression of the alloy was constructed by peak flow stress, and the corresponding 3D hot processing maps were plotted. The results showed that the peak flow stress decreased with the increase in the compression temperature and the decrease in the strain rate. The hot deformation activation energy was calculated as 243.67 kJ/mol by the Arrhenius equation, and the optimum deformation parameters for the alloy were 740–760 °C and 840–900 °C with a strain rate of 0.001~0.01 s−1. According to Deform-3D finite element simulation results, the distribution of the equivalent strain field in the hot deformation samples was inhomogeneous. The alloy was more sensitive to the deformation rate than to the temperature. The simulation results can provide a guideline for the optimization of the microstructure and hot deformation parameters of the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy.

To address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information, and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore, the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation, real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm.

Bi2S3 has attracted wide attention due to its application in photoelectric converter, but its preparation conditions are relatively complicated. The Bi2S3 microflowers, which are self-assembled from nanowires and have high photoelectric responsiveness, were synthesized by using polyvinylpyrrolidone (PVP) as structure guide under ethylene glycol solvent heat condition. The effects of different types of surfactants and different reaction time on the morphology of the products were also studied. In addition, a prototype device with photoconductive structure of visible light detection was assembled by using Bi2S3 microflowers and gold fork finger electrode as substrate. The photoresponse properties show that the photocurrent under simulated sunlight is about two orders of magnitude higher than the dark current, and the response time and decay time are 142 ms and 151 ms, respectively, indicating the photodetector has excellent photoelectric responsiveness; the light current is not attenuated by the periodic opening and closing of the photodetector, indicating that the photodetector has good stability.