Search

Herein, we explore the effect of dynamic strain aging (DSA) on dislocation evolution using nanoindentation and transmission electron microscopy (TEM). DSA is primarily manifested as serrations on the load-displacement curves at lower strain rates (10−2 and 10−3 s−1), whereas these serrations are substantially less at higher strain rate (10−1 and 100 s−1). Interestingly, despite variations in strain rates and DSA occurrence, detailed TEM observations beneath the indentations reveal no discernible differences in dislocation density and substructure. The present study indicates that although DSA can increase the gliding resistance of dislocations, it does not affect the evolution of dislocation.

Abstract Background Type 2 diabetes mellitus (T2DM) is a public health crisis that requires adequate knowledge, attitudes, and practices (KAP) by health care providers to prevent or delay the progression of the disease. This study aimed to assess the KAP regarding T2DM among primary care providers (PCPs) in Central China. Methods This multicenter cross-sectional study was conducted among 971 PCPs using self-employed KAP questionnaires. Questionnaires were designed to evaluate KAP regarding T2DM among PCPs, and was measured with SPSS software. Descriptive statistics, the Pearson correlation coefficients and multiple regression models used to analyze the data.70%, 80% and 70% of total values were considered as the cut-off point for defining good knowledge, positive attitude and correct practice. Results A total of 971 PCPs with a mean age of 44.0 ± 10.2 years were evaluated. 620 (63.9%) PCPs worked at village clinic and 605 (62.3%) PCPs have been working more than 20 years. Only 26.3% of the respondents participated in Continued Medical Education (CME) programs regarding diabetes in the past year due to Covid-19 pandemic. Overall, despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. The PCPs scored 7.25 out of 14 points on the knowledge subscales, 7.13 out of 8 on the attitude subscales, and 4.85 out of 11 on the practice subscales. Gender, age, practice setting, professional titles, duration of practice and CME attendance were significant predictors of knowledge; Age, practice setting and duration of practice were significant predictors of attitudes; and family history of diabetes affected PCP practices. Conclusions Despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. These findings call for action from relevant health authorities and policy makers to improve PCPs’ KAP regarding diabetes in Central China.

ABSTRACTAccurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Abstract Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Accurate estimation of crown fire potential (CFP) can improve guidance on crown fire control and management. However, robust simulations of crown fire behavior are still challenging, limiting the accuracy of regional-scale CFP mapping. This study aims to incorporate fire spread simulation and machine learning algorithms to improve CFP mapping at a regional scale. First, we built a crown fire dataset using the fire simulations from the FARSITE model, as well as multi-source data, including fuel, weather, and topography variables. Fuel model parameters were optimized with four metaheuristic algorithms for robust fire simulations. Then, the hybrid models of CFP estimation (TBA-ML) were established by coupling with the transfer AdaBoost (TrAdaBoost) algorithm and three machine learning (ML) algorithms, i.e., Bayesian Network (BN), Random Forest (RF), and Support Vector Machine (SVM), to estimate CFP for crown fire danger assessment spatially. Results showed that the TBA-BN model performed best in estimating CFP with higher accuracy (AUC>0.9 and F1 score > 0.8) than the RF- and SVM-based CFP models. The variable importance and causal analysis showed that fuel and topography variables have major contributions to crown fire occurrence. Finally, we mapped monthly average passive and active CFP at regional scales and qualitatively demonstrated that our CFP time-series products successfully captured the dynamic change of crown fire danger. The above results suggest the potential of integrating fire spread simulation and machine learning algorithms to accurately estimate CFP to improve crown fire management.

To clarify the influence range and saturation distribution after the biogas desaturation method is applied, a three-dimensional model is established with TOUGH2 software to analyze the effect of construction parameters such as grouting volume, grouting rate, grouting depth, nitrogen source concentration, and soil porosity. After that, the sensitivity of the parameters on the influence range is determined. The grouting volume and soil porosity are the most sensitive to the lateral and vertical influence range, respectively. This study provides a basis for the engineering practice of treating liquefiable subgrade by the biogas desaturation method.

Abstract Background Regulatory T cells (Tregs) are involved in the maintenance of immune homeostasis and immune regulation. Clinical trials on the adoptive transfer of Tregs have been ongoing for > 10 years. However, many unresolved issues remain in the production of readymade Treg products and selection of patients. Hence, this study aimed to develop a method to expand off-the-shelf Tregs derived from umbilical cord blood (UCB-Tregs) in vitro without changing their phenotype and inhibitory function. In addition, the study intended to design an approach to precisely select patients who are more likely to benefit from the adoptive Treg transfer therapy. Methods UCB-Tregs were isolated and cultured in a medium containing human recombinant IL-2 and rapamycin and then multiply restimulated with human T-activator CD3/CD28 dynabeads. The phenotype and suppressive capacity of Tregs were assessed on days 18 and 42. The relationship between the suppressive function of UCB-Tregs in vitro and clinical indicators was analyzed, and the ability of the in vitro suppressive capacity to predict the in vivo therapeutic effects was evaluated. Results UCB-Tregs expanded 123-fold and 5,981-fold at 18 and 42 days, respectively. The suppressive function of UCB-Tregs on the proliferation of immune cells at 42 days was not significantly different compared with that of UCB-Tregs obtained at 18 days. The suppression rate of UCB-Tregs to PBMCs was negatively correlated with the course of diabetes. Moreover, the high-suppression group exhibited a better treatment response than the low-suppression group during the 12-month follow-up period. Conclusions Multiply restimulated UCB-Tregs expanded at a large scale without any alterations in their classical phenotypic features and inhibitory functions. The suppressive function of Tregs in vitro was negatively correlated with the disease duration. The present study revealed the possibility of predicting the in vivo therapeutic effects via the in vitro inhibition assay. Thus, these findings provided a method to obtain off-the-shelf Treg products and facilitated the selection of patients who are likely to respond to the treatment, thereby moving toward the goal of precision treatment.

Abstract Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.

[Objective] During the process of EPB SC (earth pressure balanced shield construction), inevitable soil disturbances occur, causing ground deformation and subsequently impacting the surrounding built environment negatively. Therefore, conducting research on the ground deformation caused by EPB SC holds significant importance. [Method] By integrating various relevant literatures, the research is centered around the connections between each stage of the construction. The working mechanism of EPC SC is clarified, the causes of ground deformation are studied from three aspects: tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC are discussed, the applicability and pros and cons of each method are analyzed. Based on the causes of deformation and deformation prediction, control measures for ground deformation caused by EPB SC are listed. The shortcomings in the research of EPB SC are pointed out, and the future development direction of EPB SC is discussed. [Result & Conclusion] The causes of ground deformation induced by EPB SC are tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC include empirical formula method, theoretical analysis method, numerical simulation method of finite elements and finite difference, model test method, and artificial intelligence method. Based on the monitoring of ground deformation, continuously adjusting the shield parameters during construction is an effective method to control ground deformation by EPB shield.

In the current seismic analysis of high-speed railway track–bridge structures, the constitutive parameters of track lateral blocks (TLBs) are not uniform. A finite element model of the TLB’s main beam is established, which considers concrete plastic damage, interface bond between old and new concrete, and bond slip between anchorage steel bars (ASBs) and concrete. Quasi-static tests of nine TLBs with different numbers and ASB diameters at a 1:2 scale was carried out to verify the accuracy of the TLB finite element model in terms of the failure pattern, interface damage, hysteretic properties, and skeleton model. Based on the verified TLB finite element model, the influence of different TLB design parameters on its seismic performance parameters was studied. The results show that the established TLB finite element model is in good agreement with the test data in terms of failure pattern, interface damage, hysteretic properties, and skeleton model, which verifies the accuracy of the TLB finite element model. ASB number has the greatest influence on the TLB yield load, followed by ASB diameter, concrete strength, ASB strength, and ASB spacing. ASB diameter has the greatest influence on the TLB peak load, followed by ASB number, ASB strength, concrete strength, and ASB spacing. ASB diameter has the greatest influence on the TLB energy dissipation capacity, followed by ASB number, concrete strength, ASB strength, and ASB spacing. With the increase in concrete strength and ASB strength, TLB ductility decreases. When the ASB number is higher than eight or the diameter is higher than 12 mm and the corresponding interfacial steel bar ratio reaches 0.82%, the TLB ductility will decrease.

Retrospective analysis of forest dynamics is indispensable for formulating forest management policies. However, such work is rarely performed at a regional scale in China, especially the southwest region that serves as an important carbon sink. Here, we employed annual Landsat images and LandTrendr to map stand-replacing forest disturbances and attribute them to anthropogenic, geological disaster, fire, and logging in the Sichuan province from 2001 to 2020. The results show that the Overall Accuracy (OA) of disturbance map reaches 90.5 % (±0.6 %) (95 % confidence interval) after unbiased estimation. An estimated 1.3 % (2910.0 ± 1274.1 km2) of the forest area has been disturbed as of 2020. Fire is the dominant factor that comprises 36 % of the total disturbance area, followed by geological disaster (30 %), anthropogenic (17 %), and logging (17 %). In contrast to geological disaster dropping rapidly over the last 20 years, fire increased by 58 %, while anthropogenic disturbance and logging tripled and doubled respectively. The spatial distribution of forest disturbances in Sichuan province is as expected, characterized by fire predominantly disturbing natural forests of western and southern after the implementation of the natural forest protection policy, most geological disaster occurring in northern areas adjacent to the plateau and basin, most logging occurring in southeastern planted forest areas, and most anthropogenic disturbance occurring in eastern flat basin area. Our results present a regional-scale pattern of Sichuan’s forest disturbance agents and their changes over time, which enhances our ability to accurately assess forest management, carbon dynamics, and potential disturbance risk mitigation in the region.

The purpose of this study is to expand and deepen research in the field of safety production, explore the role of enterprises as decision-making entities in safety production, analyze the role of different types of decision-making behaviors in the process of enterprise safety production, and provide certain theoretical guidance for multi-agent decision-making in safety production. By summarizing and comparing the existing dual system of supply chain safety management, this study proposes dividing supply chain safety management into the two categories of safety organization and safety research and development (safety R&D) in the context of digitalization. Differential equations are established to describe a two-level supply chain safety management problem including manufacturers and suppliers. The research results show that, compared with the Nash equilibrium, when there are strong manufacturers involved in safety management cooperation, the manufacturer's safety R&D subsidy to suppliers improves the total revenue of the supply chain. When the manufacturer's safety organization supervision efficiency is high, the Stackelberg equilibrium is lower. It is a Pareto improvement for the manufacturer to supervise the supplier's safety organization to the Nash equilibrium. In the case of dual safety cooperation in the supply chain, when there are strong manufacturers, R&D subsidies have a crowding out effect on the safety organization cooperation. Finally, based on the example analysis, relevant suggestions are put forward for supply chain safety management.

Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed that it obtained a better network performance, proving its suitability for multi-node UV networking.