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Abstract This paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.

Abstract Different vegetation restoration methods have improved soil quality to varying degrees. This study, focused on the forest–grassland–desert transition zone in the Hebei–Inner Mongolia border region, and employed a systematic grid sampling method to establish fixed monitoring plots in the Saihanba Mechanized Forest Farm and the Ulan Buh Grassland. The differences in soil quality evolution across various vegetation restoration methods under the same climatic and soil historical conditions were analyzed, elucidating the roles of these vegetation restoration methods in degraded forest ecosystems, with the aim of providing a reference for ecological restoration under similar land conditions. This study used a grid method to establish sample points in the forest–grassland–desert transitional zone and assessed five methods of vegetation restoration sites: artificial forest composed of native species of Larix principis-rupprechtii (FL), artificial forest composed of exotic Pinus sylvestris var. mongolica (FP), natural secondary broad-leaved forest (FN), open grassland (GO), and enclosed grassland (GC). The differences in soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkaline hydrolysis nitrogen (AN), rapidly available phosphorus (AP) and rapidly available potassium (AK) among the different vegetation restoration sites were compared via variance analysis, and the soil quality index (SQI) was calculated to assess the soil quality at the sample points. The SOC, TN, and AN contents of forest soil were significantly greater than those of grassland, and the TN, TP, AN, AK, and SOC contents of FL, FN, and GC were significantly greater than those of FP and GO. Among them, the TN, TP, and SOC contents were the highest in the FL, reaching 2.74, 0.39, and 47.27 g kg−1, respectively. In terms of ecological stoichiometric characteristics, the average N:P ratio in the study area was 6.68, indicating a serious lack of N in the study area. Among the different types of restoration sites, the effect was stronger in the FP than in the FL, and the TN and AN contents were only 1.48 g kg−1 and 116.69 mg kg−1, respectively. The SQI in the FL was not significantly different from that in the FN or GC, but it was significantly greater than that in the FP and GO. These findings indicate that native tree species restoration in degraded forest ecosystems significantly improved soil quality, while the introduction of exotic tree species for afforestation had a minimal effect on improving soil quality.

Abstract Background Pancreatic cancer is one of the deadliest cancers in the world. In recent years, the incidence and mortality rates of pancreatic cancer have shown an increasing trend year by year. This study investigates the independent effects of age, period, and cohort on the global incidence, mortality, and disability-adjusted life years (DALYs) of pancreatic cancer from 1990 to 2019, and evaluates the differences in the burden of pancreatic cancer across regions with different Sociodemographic Index (SDI) levels. Methods Estimating the impact of age, period, and cohort on pancreatic cancer disease burden in different SDI regions using age-period-cohort modeling with data (with 95% uncertainty intervals [UI]) from the Global Burden of Disease (GBD) Study 2019 and net drift of age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALY rates (ASDR) for pancreatic cancer in 120 countries. Results The number of new cases of pancreatic cancer worldwide increased from 197,348 (95% UI: 188,604,203,971) in 1990 to 530,297 (486,175,573,635) in 2019, the number of deaths increased from 198,051 (189,329 to 204,763) in 1990 to 531,107 (491,948 to 566,537) in 2019, and the number of DALY increased from 4,647,207 (4,465,440 to 4,812,129) in 1990 to 11,549,016 (10,777,405 to 1,238,912) in 2019. The ASIR of the average levels in global pancreatic cancer increased from 5.22 (4.97 to 5.40) per 100,000 population to 6.57 (6.00 to 7.09) per 100,000 population, the ASMR increased from 5.34 (5.07 to 5.52) per 100,000 population to 6.62 (6.11 to 7.06) per 100,000 population, and the ASDR increased from 115.47 (110.82 to 119.60) per 100,000 population to 139.61 (130.18 to 149.14) per 100,000 population. The incidence, mortality, and DALY rates of pancreatic cancer increase with age globally and across all SDI regions, peaking in the 85–89 age group. In high and high-middle SDI regions, the growth rate for males is higher than for females before the age of 85, while females have a higher growth rate after 85. The 75–79 age group exhibits the highest DALY rate in high and high-middle SDI regions, significantly higher than the global and other SDI regions. From 1990 to 2019, the period effects of pancreatic cancer incidence, mortality, and DALY rates have increased significantly worldwide, while remaining almost unchanged in high and high-middle SDI regions. In contrast, period effects have significantly increased in middle, low-middle, and low SDI regions. Cohort effects are more pronounced in middle, low-middle, and low SDI regions. Conclusions With the aggravation of population aging, the incidence and mortality rates of pancreatic cancer in the world are increasing, and effective prevention and control measures can be achieved by reducing the exposure of risk factors. The APC model used in our analysis provides a novel approach to understanding the complex trends in the incidence, mortality, and disability-adjusted life years of pancreatic cancer. It can inform the development of targeted interventions to reduce the severe disease burden caused by pancreatic cancer.

Abstract A beamwidth (BW) and beam direction (BD) reconfigurable antenna based on multi‐mode parasitic coupling is proposed. Two pairs of metal strips connected by only two positive‐intrinsic‐negative diodes are added as parasitic structures over the driven element, a circular microstrip patch, constituting a simple antenna design. By controlling the diode switching state, four pattern reconfigurable modes can be realised. Two beam‐pointing deflection modes in the E‐plane are realised based on the Yagi–Uda principle. Besides, the BW reconfigurability is achieved due to a joint effect of the Yagi principle and beam‐stacking principle. All the reconfigurable modes share a 10% impedance bandwidth from 5.8 to 6.3 GHz. Simulation and measurement results show that the BW can be switched between 67° and 120° in an omni‐directional plane, and the BD can be switched between 0° and ±30° in the E‐plane.

Electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in wind farms (WFs). However, as WFs continue to develop on a large scale, the increasing number of wind turbines and network nodes poses significant challenges for efficient EMT simulation of WFs. To address this issue, we propose a fine-grained network decoupling method for doubly-fed induction generator (DFIG) based WFs. This paper first establishes the decoupling algorithm for core electrical equipment of DFIG-based WFs. By employing device-level fine-grained decoupling, the dimensionality of the admittance matrix for WF is effectively reduced, significantly decreasing the computational load. Additionally, this paper establishes a scalable computational framework by integrating multi-threaded parallel computation into the simulation process, which enhances efficiency further. The proposed method is compared with detailed models in Matlab/Simulink to verify efficiency and accuracy. Simulation results demonstrate that this method significantly improves simulation efficiency, achieving a two-order-of-magnitude speedup with 50 wind turbines, and it maintains high simulation accuracy, with a maximum relative error of 1.68%.

This study investigated the impact of particle size variation in un-silanized bioactive glass 45S5 (BAG) on the viscosity, stickiness and packability of experimental resin composites, while altering the ratio of 45S5 to inert glass. A series of 25 groups experimental resin composites with a constant total filler loading (65 vol%) were formulated. Filler system contains silanized barium borosilicate (BB, D50 [µm]: 0.7) and un-silanized BAG (45S5). Different ratios of BB and 45S5 were achieved by substituting 65 vol% BB with 0, 5, 10, 15, 20, 25 or 30 vol% of 45S5 (D50 [µm]: 7.0, 10.6, 17.4 and 43.6). The morphology of filler particles and formulated experimental resin composites was examined using a scanning electron microscope (SEM). Viscosity measurements were conducted on both experimental and commercial resin composites measured using an AR-G2 rheometer. A texture analyser instrument was used to measure the stickiness and packability of resin composites. Two commercial resin composites served as control groups for comparison. Statistical analysis, including one-way ANOVA tests, Tukey post-hoc tests (p < 0.05), and Kruskal-Wallis Tests (p < 0.05) for homogeneity of variables, was performed using SPSS software. Results indicated that filler loading and particle size of 45S5 significantly influenced the viscosity, stickiness, and packability of resin composites. Resin composites containing smaller 45S5 particles at lower ratios exhibited characteristics akin to packable resin composites, whereas those with larger 45S5 particles tended to resemble commercial flowable resin composites, contingent upon the particle size and ratio of 45S5.

Colorectal cancer (CRC) is a malignant tumor with a high incidence, ranking first among gastrointestinal malignancies. We investigated the impact of polyphyllin I (PPI), a natural compound found in Paris polyphylla, on CRC. PPI has been documented to exhibit anticancer activity against various tumors. This study aimed to assess the effects of PPI on colorectal cancer and explore its potential mechanisms. Our research demonstrated that PPI inhibited proliferation, promoted apoptosis, and induced G2 cell-cycle arrest in a dose-dependent manner. Additionally, our results indicated that PPI suppressed Notch signaling by downregulating the Notch1 receptor, its ligand Jagged1, and the downstream target Hes1 expression. Furthermore, we confirmed the antitumor effect of PPI on patient-derived organoids. In conclusion, our study indicates that PPI impedes the growth of colon cancer by suppressing the Notch signaling pathway.

The recent years have witnessed the rapid advancement of clustered regularly interspaced short palindromic repeats/associated protein 9 (CRISPR/Cas9)-mediated gene editing in cancer therapy. The success of cancer gene therapy relies heavily on the development of safe and effective delivery vectors. We focus on organic nanocarriers because of their flexible customizability, which permits them to accomplish excellent delivery efficiency and performance. In this review, the pharmacokinetic behavior of CRISPR/Cas9 nanocarriers serves as a guide to outline the strategies for improving the bioavailability of nanocarriers through physical, chemical, and biological means. Furthermore, the potential of organic nanocarriers for simultaneous delivery of therapeutic agents and gene editing tools is highlighted. Finally, we discuss future directions for the development of cancer therapeutic nanosystems, paving the way for the construction of effective CRISPR/Cas9 delivery systems.

Abstract Objective The predictive biomarkers of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) still need to be further explored. This study aims to establish a new immune prognosis biomarker to predict the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors. Methods The subjects of this study were 151 HCC patients receiving ICIs at Harbin Medical University Cancer Hospital from January 2018 to December 2021. This study collected a wide range of blood parameters from patients before treatment and used Cox’s regression analysis to identify independent prognostic factors in blood parameters, as well as their β coefficient. The hepatocellular carcinoma immune prognosis score (HCIPS) was established through Lasso regression analysis and COX multivariate analysis. The cut-off value of HCIPS was calculated from the receiver operating characteristic (ROC) curve. Finally, the prognostic value of HCIPS was validated through survival analysis, stratified analyses, and nomograms. Results HCIPS was composed of albumin (ALB) and thrombin time (TT), with a cut-off value of 0.64. There were 56 patients with HCIPS

Abstract Background Thyroid dysfunction is associated with abnormal glucose-insulin homeostasis, and the triglyceride-glucose (TyG) index has been recommended as a convenient surrogate of insulin resistance (IR). This study aimed to investigate the relationship between TyG and thyroid function in the US population. Methods We analyzed data from the National Health and Nutrition Examination Survey (NHANES) conducted from 2007 to 2012 in a cross-sectional manner. Aside from conventional thyroid parameters, our study evaluated the central sensitivity to thyroid hormones (THs) using the thyroid feedback quantile-based index (TFQI), thyrotropin resistance index (TT4RI), and thyrotropin index (TSHI). To evaluate peripheral sensitivity to THs, we calculated the ratio of free triiodothyronine (FT3) to free thyroxine (FT4) and the sum activity of peripheral deiodinases (SPINA-GD). In the 1848 adults, multivariable linear regression, subgroup, and interaction analyses were employed to estimate the association between TyG and thyroid parameters. The nonlinear relationship was addressed by smooth curve fittings and generalized additive models. Results After adjusting covariates, we demonstrated a significant negative association between TyG and FT4 (β = − 0.57, p

Abstract The rapid increase of photovoltaic (PV) penetration in active distribution networks (ADN) is posing great challenges to traditional voltage control schemes. A two‐stage voltage control strategy of ADN is proposed by the authors based on the alternating direction method of multipliers (ADMM) with refined power to hydrogen (P2H) model, considering multiple types of PV forms such as residential photovoltaic cluster (RPVC) and small‐scale PV power stations. In the day‐ahead stage, the optimal power flow is performed to determine the optimal scheduling results of on‐load tap changers and capacitor banks etc. In the intra‐day stage, the real‐time voltage control strategy is implemented at the distribution network layer to regulate the power of each type of PV, energy storage systems and P2H to further reduce the voltage deviation. At the customer layer, the residential photovoltaic (RPV) within the RPVC is precisely controlled based on the ADMM algorithm to achieve the minimum voltage deviation at each RPV access point. The proposed strategy is tested on the modified IEEE 33‐bus and IEEE 69‐bus distribution systems, and the simulation results verify its effectiveness in mitigating voltage violations.

Safety and efficiency are important when Unmanned Surface Vehicles (USVs) pass through narrow waters in complex marine environments. This paper considers the issue of path planning for USVs passing through narrow waterways. We propose a distributed optimization algorithm based on a polymorphic network architecture, which maintains connectivity and avoids collisions between USVs while planning optimal paths. Firstly, the initial path through the narrow waterway is planned for each USV using the narrow water standard route method, and then the interpolating spline method is used to determine its corresponding functional form and rewrite the function as a local cost function for the USV. Secondly, a polymorphic network architecture and a distributed optimization algorithm were designed for multi-USVs to maintain connectivity and avoid collisions between USVs, and to optimize the initial paths of the multi-USV system. The effectiveness of the algorithm is demonstrated by Lyapunov stability analysis. Finally, Lingshui Harbor of Dalian Maritime University and a curved narrow waterway were selected for the simulation experiments, and the results demonstrate that the paths planned by multiple USVs were optimal and collision-free, with velocities achieving consistency within a finite time.

Abstract Objective To screen the possible potential signaling pathways related to enhancer of zeste homolog 2 (EZH2) based on ceRNA mechanism, and to analyze the correlation between E2H2 and depths of various immune cell infiltration depths. The relationship between different immune checkpoints were also analyzed. Methods First, the expression of EZH2 in pan-cancer (18 malignancies) was analyzed with the TCGA database. Hepatocellular carcinoma (HCC) tissues of 374 cases and normal tissues of 50 cases were analyzed in terms of the differential expression, overall survival (OS) and progression-free-survival (PFS). Then, we conducted GO and KEGG enrichment analysis on target gene. We also analyzed mRNA-miRNA and MicroRNA (miRNA)- long non-coding RNA (lncRNA) correlation with starbase databse, so as to determine the potential ceRNA mechanism associated with EZH2. Finally, immunoassay and drug-sensitivity analysis of EZH2 was performed. Results Seven potential EZH2-related ceRNA pathways were screened out, namely lncRNA: Small Nucleolar RNA Host Gene 1 (SNHG1), SNHG 3, and SNHG 6-miR-101-3p-EZH2; and lncRNA: Long Intergenic Non-Protein Coding RNA 1978 (LINC01978), SNHG12, Ring Finger Protein 216 Pseudogene 1 (RNF216P1), and Coiled-coil Domain Containing 18 Antisense RNA 1 (CCDC18-AS1)-let-7c-5p—EZH2. Finally, 4 potential EZH2-related ceRNA pathways were identified through qPCR.According to immune correlation analysis, EZH2 may be positively correlated with T cells follicular helper, T cells Cluster of differentiation (CD)4 memory activated, Macrophages M0, and B cells memory (P 0.2); while be negatively correlated with T cells CD4 + memory resting (P

Abstract The tumor immune microenvironment (TIME) of colon cancer (CC) has been associated with extensive immune cell infiltration (IMI). Increasing evidence demonstrated that plasma cells (PC) have an extremely important role in advance of antitumor immunity. Nonetheless, there is a lack of comprehensive analyses of PC infiltration in clinical prognosis and immunotherapy in CC. This study systematically addressed the gene expression model and clinical information of CC patients. Clinical samples were obtained from the TCGA (The Cancer Genome Atlas) databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), GSVA, and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were employed to research the potential mechanism and pathways. Immunophenoscore (IPS) was obtained to evaluate the immunotherapeutic significance of risk score. Half maximal inhibitory concentration (IC50) of chemotherapeutic medicine was predicted by employing the pRRophetic algorithm. A total of 513 CC samples (including 472 tumor samples and 41 normal samples) were collected from the TCGA-GDC database. Significant black modules and 313 candidate genes were considered PC-related genes by accessing WGCNA. Five pivotal genes were established through multiple analyses, which revealed excellent prognostic. The underlying correlation between risk score with tumor mutation burden (TMB) was further explored. In addition, the risk score was obviously correlated with various tumor immune microenvironment (TIME). Also, risk CC samples showed various signaling pathways activity and different pivotal sensitivities to administering chemotherapy. Finally, the biological roles of the CD177 gene were uncovered in CC.

Unsafe behavior among construction personnel poses significant risks in petroleum engineering construction projects. This study addresses this issue through the application of a multi-field coupled homogeneous analysis model. By conducting case analyses of petroleum engineering construction accidents and utilizing the WSR methodology, the influencing factors of unsafe behaviors among construction personnel are systematically categorized into organizational system factors, equipment management factors, and construction personnel factors. Subsequently, employing Risk coupling theory, the study delves into the analysis of these influencing factors, discussing their coupling mechanisms and classifications, and utilizing the N-K model to elucidate the coupling effect among them. Furthermore, a novel approach integrating coupling analysis and multi-agent modeling is employed to establish an evolutionary model of construction personnel's unsafe behavior. The findings reveal that a two-factor control method, concurrently reinforcing equipment and construction personnel management, significantly mitigates unsafe behavior. This study provides valuable insights into the evolution of unsafe behavior among construction personnel and offers a robust theoretical framework for targeted interventions. Significantly, it bears practical implications for guiding safety management practices within petroleum engineering construction enterprises. By effectively controlling unsafe behaviors and implementing targeted safety interventions, it contributes to fostering sustainable development within the petroleum engineering construction industry.

Hepatocellular carcinoma (HCC) is a grievous tumor with an increasing incidence worldwide. Basic transcription factor 3 (BTF3) is discovered to regulate the expression of glucose transporter 1 (GLUT1), which benefits glycolysis, a momentous signature of tumors, through transactivation of the forkhead box M1 (FOXM1) expression. BTF3 is highly expressed in HCC. However, whether BTF3 promotes GLUT1 expression through FOXM1 to modulate glycolysis in HCC remains unclear. The expression profile of BTF3 were determined by online database, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot. The role and mechanism of BTF3 in the proliferation and glycolysis of HCC cells were examined by cell counting kit-8 (CCK-8), 5-ethynyl-2′-deoxyuridine (EdU) incorporation, XF96 Extracellular Flux analyzer, spectrophotometry and western blot analysis. In addition, the direct interaction between BTF3 and FOXM1 was verified by dual-luciferase reporter and co-immunoprecipitation assays. Moreover, the role of BTF3 was also explored in a xenografted mice model. The expression of BTF3 was increased in HCC cells and tumor tissues. Knockdown of BTF3 reduced the cell viability, Edu positive cells, extracellular acidification rate (ECAR), glucose consumption and lactate production in both Huh7 and HCCLM3 cells. The expressions of FOXM1 and GLUT1 were increased in HCC tissues, which were positively correlated with the BTF3 expression. Moreover, a direct interaction existed between BTF3 and FOXM1 in HCC cells. Downregulation of BTF3 decreased the relative protein levels of FOXM1 and GLUT1, which were rescued with overexpression of FOXM1 in both cells. More importantly, overexpression of FOXM1 restored the cell viability, ECAR, glucose consumption and lactate production in both Huh7 and HCCLM3 cells transfected with siBTF3#1. Furthermore, inhibition of BTF3 decreased tumor weight and volume, and the relative level of BTF3, FOXM1, GLUT1 and Ki-67 in tumor tissues from mice xenografted with Huh7 cells. BTF3 enhanced the cell proliferation and glycolysis through FOXM1/GLUT1 axis in HCC.

Abstract Background Synaptogyrin-2 (SYNGR2), as a member of synaptogyrin gene family, is overexpressed in several types of cancer. However, the role of SYNGR2 in pan-cancer is largely unexplored. Methods From the TCGA and GEO databases, we obtained bulk transcriptomes, and clinical information. We examined the expression patterns, prognostic values, and diagnostic value of SYNGR2 in pan-cancer, and investigated the relationship of SYNGR2 expression with tumor mutation burden (TMB), microsatellite instability (MSI), immune infiltration, and immune checkpoint (ICP) genes. The gene set enrichment analysis (GSEA) software was used to perform pathway analysis. Besides, we built a nomogram of liver hepatocellular carcinoma patients (LIHC) and validated its prediction accuracy. Results SYNGR2 was highly expressed in most cancers. The high expression of SYNGR2 significantly reduced the overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in multiple types of cancer. Also, receiver operating characteristic (ROC) curve analysis demonstrated that SYNGR2 showed high accuracy in distinguishing cancerous tissues from normal ones. Moreover, SYNGR2 expression was correlated with TMB, MSI, immune scores, and immune cell infiltrations. We also analyzed the association of SYNGR2 with immunotherapy response in LIHC. Finally, a nomogram including SYNGR2 and pathologic T, N, M stage was built and exhibited good predictive power for the OS, DSS, and PFI of LIHC patients. Conclusion Overall, SYNGR2 is a critical oncogene in various tumors. SYNGR2 participates in the carcinogenic progression, and may contribute to the immune infiltration in tumor microenvironment. Our study suggests that SYNGR2 can serve as a predictor related to prognosis in pan-cancer, especially LIHC.

Abstract Edible rose is one of the main cash crops in Yunnan, China. Due to the high degree of lignification of rose stalks which is difficult to cut, and roses can only be pruned by hand after picking. Most of Yunnan Province is hilly landscape. Therefore, it is necessary to design an efficient small rose pruner for hills. Based on the experimental results of the physical and mechanical properties of rose stems, a simulation experiment of three different cutting methods was conducted to determine the optimal scheme. The results showed that single disc cutter is the best cutting method. An improved disc cutter with variable sliding angle is designed. In order to study the effects of the different structure parameters on the performance of the pruning machine and get the optimal combination of parameters, the orthogonal test was conducted. Forward speed, rotational speed of cutter, number of blades, cutting angle and knife-edge angle as factors, and the quality of pruning was selected as the evaluation index. The experiment results indicated that the working performance of machine was the highest when the number of blades was 6, rotational speed of cutter was 2000 rpm, cutting angle was 20°, knife-edge angle was 10°, forward speed was 0.5 m s−1. The average quality of pruning index, miss cutting index and tearing index were 90.6%, 5.68% and 3.72%, respectively. The designed machine can meet the agronomic requirements of Yunnan edible rose pruning operation. The research provides a reference for the design and extending of the pruning machine for edible rose.

Abstract This paper proposes an efficient and extensible simulation framework for mid‐fidelity wind field model based on heterogeneous architecture. The wind field model consists of 3‐D wind inflow, wake dynamics, and wind turbine models. The CPU‐GPU heterogeneous platform accelerates the 3‐D wind inflow model simulation by performing large‐scale parallel computing based on GPU. The CPU is utilized to dispatch the simulation and perform serial computing. Compared with the conventional simulation tool, the simulation speed of the 3‐D wind inflow model is highly improved, which fulfils the requirement of the real‐time wind field simulation. The hardware architecture of the framework is designed to be highly extensible by multiple computing nodes for 3‐D wind inflow, wake dynamic, and wind turbine model. The extendability enables the ability for large‐scale wind field simulation with high efficiency. A 5 km$ m km$ × 2 km$ m km$ × 0.35 km$ m km$ wind field consisting of three wind turbines is used as the case. The case study was conducted and analyzed, proving that the framework can capture the detailed wake dynamic, wind turbine dynamic, and wind inflow dynamic. Meanwhile, the calculation speed of disturbed wind speed increased by 264 times.

Current mesenchymal stem cell (MSC) transplantation practices are limited by the loss or reduced performance of MSCs. Here the authors develop a bead-jet printer for intraoperative formulation and printing of MSCs-laden Matrigel beads to improve skeletal muscle and hair follicle regeneration.

The power flow calculation is a significant basis for the optimized operation of integrated energy systems. For two-way coupled electricity–gas integrated energy systems, the dynamic characteristics of the slow time scale of the gas subsystem make the steady-state energy flow calculation unable to accurately obtain its real-time operating status. In this paper, for the electricity–gas integrated energy system, propose a time-domain continuous power flow calculation method considering the dynamic process of the gas subsystem, which can obtain continuous change of the state variables. The Taylor series expansion method is applied to the power subsystem, the unified energy path model and matching calculation method is used in the natural gas subsystem. The solution is realized by alternating the input and output variables of the coupling element. At the same time, the input of the power subsystem derivative information and the natural gas subsystems frequency domain information makes this method obtain continuous solutions in the time domain, which can meet the time scale requirements in different scenarios. Finally, a calculation example is carried out, and the results show that compared with traditional discrete method, this method can greatly reduce the calculation time under the condition of ensuring a certain degree of accuracy, which verifies the effectiveness of the proposed method.

Abstract Tumor spread is responsible for most deaths related to cancer. Increasing the accuracy of cancer prognosis is critical to reducing the high mortality rates in cancer patients. Here, we report that the electrostatic potential difference (EPD) between tumor and its paratumor tissue is a prognostic marker for tumor spread. This finding is concluded from the patient‐specific EPD values and clinical observation. The electrostatic potential values were measured on tissue cryosections from 51 patients using Kelvin probe force microscopy (KPFM). A total of ~44% (15/34) patients of Vtumor–paratumor > 0 were featured with tumor spread, whereas only ~18% (2/11) patients of Vtumor–paratumor

With the deepening of a new round of technological revolution and industrial reform, digital technology has been continuously innovated and widely penetrated into various economic fields. The digital economy (DE) is gradually becoming the focus of China's economic development planning and a new engine to enhance national strength. Evaluating the development level of DE in various regions is conducive to timely discover the shortcomings in China's DE development, as well as provide an important basis for putting forward corresponding policy suggestions. This investigation established a hybrid multi-criteria decision making (MCDM) model for evaluating DE development of 31 provincial level regions in China ranging from 2015 to 2020. Firstly, the evaluation indicator system is established from digital infrastructure, integrated development, social benefits, innovation ability, and electronic-commerce dimensions containing 17 quantitative sub-criteria based on Fuzzy-Delphi method. Secondly, integrated weights of 17 sub-criteria from 2015 to 2020 are computed in terms of objective weights calculated by the anti-entropy weight (AEW) approach from 2015 to 2020 and subjective weights obtained via the best-worst method (BWM). Thirdly, MARCOS model is applied to evaluate the DE development degree of various regions in China ranging from 2015 to 2020. Case analysis illustrates that the DE development of Guangdong, Jiangsu, Zhejiang, and Beijing always maintain in the top four from 2015 to 2020, while the southwest and northwest regions in China are obviously fall behind others. And the DE development degree of various regions is primarily affected under the integrated development performance, innovation ability performance, and social benefits performance. Therefore, the backward regions should emphasize the development of software industry and information technology industry. The robustness of the proposed MCDM model combining Fuzzy-Delphi, AEW, BWM and MARCOS is discussed employing three similarity coefficients of rankings. And it is verified that the proposed MCDM model has superior robustness and validity in evaluating DE development.

The synthesis of biomass carbon dots (BCDs) using natural products has attracted increasing attention in the past few years owing to its advantages in terms of abundant sources and green environmental protection. However, the low luminescence quantum yield (QY) of BCDs is still a bottleneck for practical applications. Herein, nitrogen and sulfur co-doped biomass carbon dots (N,S-BCDs) with high QY (84.93%) were successfully prepared by one-pot green hydrothermal method using natural grapefruit juice and urea as carbon and nitrogen sources, respectively. The as-obtained N,S-BCDs showed an average size of 3.8 nm with strong blue luminescence and excitation-dependence fluorescence behavior. Furthermore, good selectivity and sensitivity were noticed toward hexavalent chromium (Cr(VI)) due to the dynamic quenching of fluorescence. The limit of detection (LOD) was determined as 0.155 μM, and the linearity was extended from 0 to 400 μM. Furthermore, the paper sensors for the visual semi-quantitative rapid detection of Cr(VI) were constructed by using the as-obtained N,S-BCDs, which reveals the great potential applications as fluorescent probes for the detection of heavy metal ions in the environment.

ObjectiveAbdominoperineal resection (APR) is currently established as a standard treatment regimen for fistula-associated anal adenocarcinoma (FAAA), however, the efficacy of chemoradiotherapy (CRT) remains unclear. The aim of this study is to evaluate the role of CRT in patients with FAAA treated with APR through single-center experience and literature review.MethodsA retrospective review was performed on patients with FAAA consecutive treated in our institution from 2005 to 2022. In addition, a systematic literature search was performed using PubMed and MEDLINE. All patients with FAAA who received APR in our institution and reported in the literature were included and divided into three categories for statistical analysis: APR alone (APR group), neoadjuvant therapy combined APR (CRT+APR group), and APR combined postoperative therapy (APR+CRT group).ResultsFifteen patients with FAAA were identified from our retrospective charts review. At a median follow-up time of 18 months, the recurrence-free survival rate was 53.3% and the survival rate was 73.3%. Eight patients underwent APR and 6 received postoperative chemotherapy. Among them, one died, one developed recurrence and the remaining six patients were alive with disease free. We found 37 publications describing 62 patients with FAAA treated with APR. Clinical data from these articles were analyzed together with the 8 cases in our institution. The overall survival rates were 94.1%, 70.8%, and 38.5% at 1-, 3-, 5-years respectively. Combining (neo)adjuvant therapy did not appear to improve outcomes in FAAA treated with APR (CRT+APR vs. APR, p=0.977; APR+CRT vs. APR, p=0.351). Lymph node involvement was shown to be significantly associated with poor outcomes by multivariate analysis (p=0.020).ConclusionsFor patients with FAAA without lymph node involvement, APR is adequate to control disease and the addition of CRT does not appear to prolong survival.

Ultrafast electron diffraction (UED) from aligned molecules in the gas phase has successfully retrieved structures of both linear and symmetric top molecules. Alignment of asymmetric tops has been recorded with UED but no structural information was retrieved. We present here the extraction of two-dimensional structural information from simple transformations of experimental diffraction patterns of aligned molecules as a proof-of-principle for the recovery of the full structure. We align 4-fluorobenzotrifluoride with a linearly polarized laser and show that we can distinguish between atomic pairs with equal distances that are parallel and perpendicular to the aligned axis. We additionally show with numerical simulations that by cooling the molecules to a rotational temperature of 1 K, more distances and angles can be resolved through direct transformations.

Objective: Organoids have recently been used as in vitro models to screen chemotherapy drugs in combination with hyperthermia treatment in colorectal cancer. Our research aimed to establish a library of patient-derived colorectal cancer organoids to evaluate synergism between chemotherapy drugs and hyperthermia; validate an index of the hyperthermia chemotherapy sensitization enhancement ratio (HCSER) to identify the chemotherapeutics most enhanced by hyperthermia; and recommend chemotherapy drugs for hyperthermic intraperitoneal treatment. Methods: Organoids were grown from cells extracted from colorectal cancer patient samples or colorectal cancer cell lines. Cells from both sources were encapsulated in 3D Matrigel droplets, which were formulated in microfluidics and phase-transferred into identical cell-laden Matrigel microspheres. The microspheres were seeded in 96-well plates, with each well containing a single microsphere that developed into an organoid after 7 days. The organoids were used to evaluate the efficacy of chemotherapy drugs at both 37 °C as a control and 43 °C for 90 min to examine hyperthermia synergism. Cell viability was counted with 10% CCK8. Results: We successfully established a library of colorectal cancer organoids from 22 patient parental tumors. We examined the hyperthermia synergism of 7 commonly used hyperthermic intraperitoneal chemotherapy drugs. In 11 of the 22 patient organoids, raltitrexed had significant hyperthermia synergism, which was indexed as the highest HCSER score within each patient group. Conclusions: Our results primarily demonstrated the use of patient-derived colorectal cancer organoids as in vitro models to evaluate hyperthermia synergistic chemotherapeutics. We found that hyperthermia enhanced the effect of raltitrexed the most among the common anti-colorectal cancer drugs.

The present study aimed to investigate the effects of palygorskite (PAL) as an alternative to antibiotic on the growth performance, oxidative status, immune function, intestinal barrier and cecal microbial community of broilers. A total of 360 1-day-old male Ross-308 broilers were randomly allotted to three treatments with eight replicates. Broilers in the three groups were designated as follows: basal diet (CON group), basal diet+50 mg/kg chlorotetracycline (ANT group), and basal diet+ 10 g/kg PAL (PAL group). Supplementing PAL reduced feed to gain ratio in broilers during 22 to 42 days of age (P < 0.05), with its value being similar to that of the ANT group (P > 0.05). Broilers fed a PAL-supplemented diet exerted decreased contents of interferon-γ (IFN-γ) and interleukin-1β in serum, and the same reduction was found in jejunal IFN-γ level, when compared to the CON group (P < 0.05). Moreover, compared with the CON group, broilers after PAL treatment had a lower malondialdehyde content in jejunal mucosa (P < 0.05). Supplementing PAL elevated jejunal villus height (VH) and ratio of VH to crypt depth compared with the ANT group (P < 0.05). Cecal microbiota communities among the three groups were significant different, as demonstrated by distinct clusters from partial least squares discriminant analysis, although dietary treatments had no significant effects on the bacterial richness and diversity indices (P > 0.05). At genus level, the addition of PAL increased the relative abundance of norank_f__Barnesiellaceae and decreased that of unclassified_f__Oscillospiraceae in cecal digesta compared with those in the CON group (P < 0.05); the proportion of genus norank_f__Barnesiellaceae was increased by PAL treatment when compared with the ANT group (P < 0.05). Moreover, spearman's correlations showed that the modulation of cecal microflora composition by PAL supplementation was closely correlated with the promotion of growth performance (feed to gain ratio) and intestinal health-related (contents of malondialdehyde and IFN-γ, and VH value in jejunum) variables of broilers (P < 0.05). Taken together, dietary PAL could improve the growth performance, antioxidant capacity, and immune status, as well as intestinal barrier function in broilers, which might be partially associated with the alteration of cecal microbiota. Moreover, dietary PAL may be a promising alternative to antibiotic growth promoter for broilers.

Phytoplankton play a critical role in marine food webs and biogeochemical cycles, and their abundance must be monitored to prevent disasters and improve the marine environment. Although existing algorithms for automatic phytoplankton identification at the image level are available, there are currently no video-level algorithms. This lack of datasets is a significant obstacle to the development of video-level automatic identification algorithms for phytoplankton observations. Deep learning-based algorithms, in particular, require high-quality datasets to achieve optimal results. To address this issue, we propose the PMOT2023 (Phytoplankton Multi-Object Tracking), a multi-video tracking dataset based on 48,000 micrographs captured by in situ observation devices. The dataset comprises 21 classes of phytoplankton and can aid in the development of advanced video-level identification methods. Multi-object tracking algorithms can detect, classify, count, and estimate phytoplankton density. As a video-level automatic identification algorithm, multi-object tracking addresses trajectory tracking, concentration estimation, and other requirements in original phytoplankton observation, helping to prevent marine ecological disasters. Additionally, the PMOT2023 dataset will serve as a benchmark to evaluate the performance of future phytoplankton identification models and provide a foundation for further research on automatic phytoplankton identification algorithms.

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future.

The passive optical network (PON) is widely used in optical fiber communication thanks to its low cost and low resource consumption. However, the passiveness brings about a critical problem that it requires manual work to identify the topology structure, which is costly and prone to bringing noise to the topology logs. In this paper, we provide a base solution firstly introducing neural networks for such problems, and based on that solution we propose a complete methodology (PT-Predictor) for predicting PON topology through representation learning on its optical power data. Specifically, we design useful model ensembles (GCE-Scorer) to extract the features of optical power with noise-tolerant training techniques integrated. We further implement a data-based aggregation algorithm (MaxMeanVoter) and a novel Transformer-based voter (TransVoter) to predict the topology. Compared with previous model-free methods, PT-Predictor is able to improve prediction accuracy by 23.1% in scenarios where data provided by telecom operators is sufficient, and by 14.8% in scenarios where data is temporarily insufficient. Besides, we identify a class of scenarios where PON topology does not follow a strict tree structure, and thus topology prediction cannot be effectively performed by relying on optical power data alone, which will be studied in our future work.

This study was to evaluate the effects of dietary natural vitamin E supplementation on laying performance, egg quality, serum biochemical indices, tocopherol deposition and antioxidant capacity of laying hens. A total of 360 46-week-old Hyline Brown hens were randomly assigned into five treatments consisting of six replicates with 12 hens each for a 9-week feeding trial. Birds were fed a basal diet supplemented with 0, 25, 50, 75, and 100 mg/kg natural vitamin E, respectively. Natural vitamin E linearly increased egg production and egg mass (p

Shift-invariant and sampling spaces play a vital role in the fields of signal processing and image processing. In this paper, we extend the generalized shift-invariant and sampling subspaces from the traditional sampling spaces to the compressed sampling, and develop a compressed sampling method for analog sparse signals based on the shift-invariant spaces associated with fractional Fourier transform (FrFT). First, we show the generalized shift-invariant and sampling subspaces can be used to explain the traditional sampling spaces with single generator or multiple generators in the fractional Fourier domain (FrFD). The non-ideal sampling structures of single channel and multiple channels are special cases of the generalized shift-invariant subspaces. Second, a compressed sampling method for the sparse signals in the FrFD is proposed by reusing the multiple generators of the shift invariant spaces as sparse representation. We combine the sensing matrix of compressed sensing and the framework of sampling scheme in the shift-invariant spaces to construct a compressed sampling method, which perfectly recovered the original signal with a sufficient low sampling rate. By choosing different filters, the proposed framework allows to derive many specific sampling schemes. Finally, a compressed sampling method for multiband signals in the FrFD is proposed based on the forgoing theorems. The numeral simulation validates the theoretical derivations.

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

It is challenging to deal with the optimal scheduling problem of the multi-regional integrated energy system (MIES) precisely and efficiently due to its multi-dimensional nonlinear characteristics. This article proposes an iterative linearization approach to solve the complicated and nonlinear MIES optimization problem with a well-balanced trade-off between accuracy and computation efficiency. In particular, the proposed approach is a combination of the modified piecewise linearization (PWL) tactic and the sequential linear programming (SLP) algorithm. The modified PWL method is developed to improve the speed-accuracy trade-off of the linearization, while the SLP algorithm is used to linearize the multi-dimensional nonlinear functions and narrow the approximation error iteratively. In this way, accurate but highly nonlinear formulations such as heat network models in the variable flow and variable temperature (VF-VT) mode can be considered in the optimization and solved efficiently. Finally, the effectiveness of the given approach is validated in a day-ahead optimal scheduling case of a four-region MIES.

This study was conducted to evaluate the effects of dietary squalene supplementation on the growth performance, plasma biochemical indices, antioxidant status, and meat quality in broilers. Two hundred and forty 0-day-old male chicks were allocated into 5 groups of 6 replicates and were fed a basal diet supplemented with 0 (Control group), 250, 500, 1,000, or 2,000 mg/kg squalene for 42 d. Dietary squalene supplementation linearly increased weight gain and feed efficiency of broilers during the grower and overall periods (P 0.05). It was concluded that squalene administration especially at a level of 1,000 mg/kg can improve growth performance, antioxidant status, and meat quality in broilers, providing insights into its application as a potential feed additive in broiler production.

This paper presents an all-digital real-time co-simulation platform of refined Wind Energy Conversion System (WECS) model. The proposed platform is based on the Real-time Digital Simulator (RTDS) and FAST developed by National Renewable Energy Laboratory (NREL). The detailed generator and converter models are implemented in RTDS to simulate the electromagnetic transient. The models of wind, blade, tower, nacelle, and drivetrain are implemented in FAST to simulate the aerodynamic, structure, and turbulence dynamics. In such way, the designed co-simulation platform can capture all the dynamics, which can significantly enhance the simulation accuracy. Conventionally, RTDS and FAST communicate by the analogue signals, whose number of the exchanged data is quite limited. To overcome this limitation, the all-digital interface technique between RTDS and FAST is proposed. The correctness of the platform is verified by the simulation result. Keywords: FAST, Hardware-in-loop test, Multi-time scale, Real-time co-simulation, RTDS, Refined model

The choice of therapeutic agents remains an unsolved issue in the repair of spinal cord injury. In this work, various agents and configurations were investigated and compared for their performance in promoting nerve regeneration, including bead assembly and bulk gel of collagen and Matrigel, under acellular and cell-laden conditions, and cerebral organoid (CO) as the in vitro preorganized agent. First, in Matrigel-based agents and the CO transplantations, the recipient animal gained more axon regeneration and the higher Basso, Beattie, and Bresnahan (BBB) scoring than the grafted collagen gels. Second, new nerves more uniformly infiltrated into the transplants in bead form assembly than the molded chunks. Third, the materials loaded the neural progenitor cells (NPCs) or the CO implantation groups received more regenerated nerve fibers than their acellular counterparts, suggesting the necessity to transplant exogenous cells for large trauma (e.g., a 5 mm long spinal cord transect). In addition, the activated microglial cells might benefit from neural regeneration after receiving CO transplantation in the recipient animals. The organoid augmentation may suggest that in vitro maturation of a microtissue complex is necessary before transplantation and proposes organoids as the premium therapeutic agents for nerve regeneration.

Different scales of the objects pose a great challenge for the segmentation of remote sensing images of special scenes. This paper focuses on the problem of large-scale variations of the target objects via a dynamical receptive field of the deep network. We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding. Moreover, we propose a new Gaussian pyramid pooling (GPP) for multi-scale object segmentation. The proposed network can expand the size of the receptive field and improve its efficiency in aggregating contextual information. Experiments verify that our method outperforms the popular semantic segmentation methods on large remote sensing image datasets, including iSAID and LoveDA. Moreover, we conduct experiments to demonstrate that the Gaussian dynamic convolution works more effectively on remote sensing images than other convolutional layers.

In view of the problem of a large amount of stubble and straw in Southwest China, it is difficult to carry out no-tillage sowing operations. Based on the principle of supported cutting, a bi-directional rotating stubble-cutting no-tillage planter was designed. According to the extracted left and right mouth contours of Batocera horsfieldi (Hope), the blade curve of a bi-directional rotating cutterhead was designed. The discrete element models were established regarding ‘bi-directional rotating disc cutter, straw and soil’, ‘fertilizer apparatus and fertilizer’, and ‘opener and soil’ in the Extended-Domain-Eigenfunction Method (EDEM) software. The optimal working parameters were analyzed using a quadratic regression orthogonal rotation test and response surface methodology. Accordingly, the prototype was manufactured and the field performance test was carried out. The best working parameters of the machine were as follows: the forward speed of the machine was 0.9 m·s−1, the cutter spacing was 60 mm, the forward speed was 150 r·min−1, and the reverse speed was 313 r·min−1. The field experiment results showed that the average cutting rate of corn straw was 95.72% using the anti-blocking device when the straw mulching amount was 1.63 kg·m−2. The average sowing depth was 5.4 cm, the average fertilization depth was 10.1 cm, and the average seed–fertilizer spacing was 4.7 cm. The qualified rates of sowing depth, fertilization depth, and spacing were 88.89%, 100%, and 100%, respectively. The designed bi-directional rotating stubble-cutting no-tillage planter can meet the requirements of no-tillage sowing in Southwest China. This study can provide reference for the design and improvement of no-tillage planters under the conditions of a large amount of stubble and straw.

With the development of the internet of things (IoT),5G and industrial technology,industrial IoT has become a new research field.Due to the specific requirements of delay,security and stability in industrial production,the traditional DNS has limitations to meet the needs of industrial IoT because of its single identity object,rigid resolution results,and weak security.In this context,how to provide efficient,flexible and secure resolution services for universal objects has become a hot area worldwide.Firstly,we discussed the design principles and key supporting technologies of the identity resolution system in industrial IoT.Then summarized and compared the existing identity resolution systems in detail.Next,the new identity resolution schemes in academic research were introduced.Finally,the core problems and the future research trend in this field were discussed.

Effective cooling of a high heat flux IGBT electronic system is highly related to system efficiency and safety. A flat plate heat sink was designed to experimentally investigate the transient heat transfer characteristics of IGBT cooling. It is made of aluminum with 20 mini-channels of 249 mm × 3 mm × 4 mm dimensions, which were manufactured by milling machines and melt inert gas (MIG) welding technology to ensure no deformation. Experiments were conducted using deionized water at atmospheric pressure with flow rates of 3.2–9.5 L/min and heat fluxes of 104–347 W/cm2. It was found that instantaneous start-stop and transient heating power variation might cause IGBT failure, especially under low Reynolds and Nusselt number conditions. The temperature rise rate and cooling rate vary with different system parameters. Heating rate can be reduced by high flow rate due to local subcooled boiling. The concept of respond time (RT) based on the piecewise function is suggested to evaluate the influence of transient condition on heating rate. Analysis of flow fluctuation indicated that it would not be a threat to the system except for in extreme cases. These findings provide a reference for the considerations of the design and manufacture of IGBT cooling flat plate heat sinks with mini-channels.

In order to explore the mechanical characteristics of stubble breaking and provide a theoretical basis for the design of a stubble breaking and crushing blockage prevention device, an orthogonal test with three factors (water content, bulk weight, and root content) and a quick shearing test of remolded soil were carried out in a laboratory. The shear resistance of the rice root–soil complex was studied and the soil mechanical equation of the rice root–soil composite was established. It is found that the shear strength of the root–soil composite is related to water content and root content. When the water content was around 30% and the root content was 1.1%, the cohesion of the root–soil composite was the smallest. With the decrease or increase of water content and the decrease or increase of root content, the cohesion of the root–soil composite showed the trend of increasing layer by layer. When the water content was 40% and the root content was 1.1%, the internal friction angle of the root–soil composite showed the minimum value. With the decrease of water content and the increase of root content, the internal friction angle of the root–soil composite gradually showed an increasing trend; while the root content had a great influence on the internal friction angle, the influence of water content on it was relatively small. The direct shear and fast shear tests of root–soil composite samples showed that the shear strength of the root–soil composite and the normal pressure loaded on it conform to the Coulomb equation. The presence of roots increased the shear strength and cohesion value of the soil and improved the resistance to deformation of the soil, but had little influence on the internal friction angle.

Mycoplasma bovis is a significant bacterial pathogen which is able to persist in cattle and cause chronic diseases. This phenomenon may relate to M. bovis evading the immune system of the host. Immunoglobulin-binding proteins are widely distributed in a variety of pathogenic bacteria, including some Mycoplasma species. These proteins are considered to help the bacteria evade the immune response of the host. Here we found M. bovis strain PG45 can bind to IgG from several animals. MBOVPG45_0375 encodes a putative membrane protein, has strong amino acid sequence similarity with Immunoglobulin G-binding protein in Mycoplasma mycoides subsp. capri. Hence, we constructed recombinant MBOVPG45_0375 (r0375) in the Escherichia coli expression system and demonstrated that r0375 can bind to IgG non-immunologically rather than specific binding similar to interaction of antigen and antibody. Moreover, r0375 can bind to the Fab fragment of IgG. Also, the binding of r0375 and IgG inhibits the formation of antigen-antibody union. Furthermore, MBOVPG45_0376 encodes an IgG-cleaving protein of M. bovis strain PG45. Nevertheless, r0375 binding to IgG is required for the cleavage activity of recombinant 0376 (r0376). The activity of r0376 is also affected by incubation time and temperature. In addition, we found both MBOVPG45_0375 and MBOVPG45_0376 are membrane proteins of M. bovis strain PG45. These results about MBOVPG45_0375 as an IgG-binding protein and MBOVPG45_0376 as an IgG-cleaving protein offer a new insight into the interaction between M. bovis and its host.

Remote sensing image (RSI) scene classification aims to identify the semantic categories of remote sensing images based on their contents. Owing to the strong learning capability of deep convolutional neural networks (CNNs), RSI scene classification methods based on CNNs have drawn much attention and achieved remarkable performance. However, such outstanding deep neural networks are usually computationally expensive and time-consuming, making them impossible to apply on resource-constrained edge devices, such as the embedded systems used on drones. To tackle this problem, we introduce a novel pair-wise similarity knowledge distillation method, which could reduce the model complexity while maintaining satisfactory accuracy, to obtain a compact and efficient deep neural network for RSI scene classification. Different from the existing knowledge distillation methods, we design a novel distillation loss to transfer the valuable discriminative information, which could reduce the within-class variations and restrain the between-class similarity, from the cumbersome model to the compact model. This method could obtain the compact student model with higher performance compared with existing knowledge distillation methods in RSI scene classification. To be specific, we distill the probability outputs between sample pairs with the same label and match the probability outputs between the teacher and student models. Experiments on three public benchmark datasets for RSI scene classification, i.e., AID, UCMerced, and NWPU-RESISC datasets, verify that the proposed method could effectively distill the knowledge and result in a higher performance.