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Abstract Macrophages play a pivotal role in the healing of diabetic ulcers. The sustained elevation of glucose levels damages the insulin signaling pathway in macrophages, leading to dysfunctional macrophages that struggle to transition from pro-inflammatory (M1) to reparative (M2) states. Therefore, modulating macrophage inflammatory responses via the insulin pathway holds promise for diabetic ulcer treatment. Additionally, the presence of biofilm impedes drug penetration, and the resulting immunosuppressive microenvironment exacerbates the persistent infiltration of pro-inflammatory M1 macrophages. Therefore, we designed an array of dissolvable microneedle (denoted as NPF@MN) loaded with self-assembled nanoparticles that could deliver NPF nanoparticles, acid-sensitive NPF-releasing Protocatechualdehyde (PA) with hypoglycemic and insulin-like effects, regulating macrophage polarization to an anti-inflammatory M2 phenotype. Additionally, this study extensively examined the mechanism by which NPF@MN accelerates the healing of diabetic ulcers through the activation of the insulin signaling pathway. Through RNA-seq and GSEA analysis, we identified a reduction in the expression of pathway-related factors such as IR, IRS-1, IRS-2, and SHC. Our work presents an innovative therapeutic approach targeting the insulin pathway in diabetic ulcers and underscores its translational potential for clinical management.

Abstract Background In patients with unprotected left main coronary artery disease (ULMCAD), this study compared the long-term prognosis of drug-eluting stent insertion guided by intravascular ultrasonography (IVUS) vs. angiography. Patients and methods This retrospective consort investigation was performed in December 2021. This analysis included 199 patients who underwent IVUS-guided (IVUS group, n = 81) or angiography-guided (angiography group, n = 118) drug-eluting stent implantation at the Affiliated Hospital of Inner Mongolia Medical University between September 2013 and September 2018. Major adverse cardiac events (MACE) were defined as cardiovascular death, sudden cardiac death, myocardial infarction. Results The IVUS group had considerably lower proportions of MACE within 1 year postoperatively (P = 0.002) and cardiac mortality within 3 years postoperatively (P = 0.018) compared to the angiography group. However, after adjusting for confounding variables, the hazard ratio for 3-year cardiac mortality was similar between the two groups (P = 0.28). In the IVUS group, there was considerably greater minimum lumen diameter (MLD) (P = 0.046), and reduced frequencies of target vessel restenosis (P

Abstract Osteoarthritis (OA) is marked by cartilage deterioration, subchondral bone changes, and an inflammatory microenvironment. The study introduces the Microneedle‐Delivered Polydopamine‐Exosome (PDA@Exo MN), a therapeutic that not only preserves cartilage and promotes bone regeneration but also improves localized drug delivery through enhanced penetration capabilities. PDA@Exo MN shows strong reactive oxygen species (ROS) scavenging abilities and high biocompatibility, fostering osteogenesis and balancing anabolic and catabolic processes in cartilage. It directs macrophage polarization from M0 to the anti‐inflammatory M2 phenotype. RNA sequencing of treated chondrocytes demonstrates restored cellular function and activated antioxidant responses, with modulated inflammatory pathways. The PI3K‐AKT‐mTOR pathway's activation, essential for PDA@Exo's effects, is confirmed via bioinformatics and Western blot. In vivo assessments robustly validate that PDA@Exo MN prevents cartilage degradation and OA progression, supported by histological assessments and micro‐CT analysis, highlighting its disease‐modifying impact. The excellent biocompatibility of PDA@Exo MN, verified through histological (H&E) and blood tests showing no organ damage, underscores its safety and efficacy for OA therapy, making it a novel and multifunctional nanomedical approach in orthopedics, characterized by organ‐friendliness and biosecurity.

Predicting the risk of diminished wheat yields caused by drought under future climate change climate is essential for the long-term sustainability of agriculture. Although studies have explored the relationship between drought and crop yield loss, the precise thresholds triggering yield losses in the future remain unclear. In this study, we established a conditional probability framework for drought trigger thresholds at various yield loss levels in China’s winter wheat regions in the future based on copula functions. The primary drivers influencing the dynamics of drought thresholds were evaluated using a random forest model. The results revealed that the projected drought thresholds for the baseline period (1981–2020), near future (2021–2060), and far future (2061–2100) ranged from –2.1 to –1.2, –0.8 to –0.6, and –1.2 to –1.0, respectively, implying that the drought thresholds for winter wheat yield loss in the future firstly rises and then declines. This trend was primarily due to the increased contribution of precipitation (Pre) (from 24.0% to 31.5%) to the drought threshold in the far future, coupled with a decrease in the contribution of temperature (Tmean) (from 37.1% to 30.4%). This shift suggested that the increased Pre might alleviate the adverse effect of high temperature on yield in the future. The average drought thresholds for yield loss were higher in the Southwest (–1.0 to –0.6) and Xinjiang (–1.1 to –0.7) winter wheat regions, where mild drought occurrences led to a 30% yield loss (70th percentile). Tmean was the primary driving factor for the dynamic changes in future drought thresholds. The research findings provide scientific guidance for future agricultural water resource allocation and drought risk management.

Study region: The Dongzhi tableland on the loess plateau, China. Study focus: This study investigates how various land use types on the Loess Plateau affect groundwater recharge using the chloride mass balance method (CMB). By integrating data from Cl–, NO3/Cl–, and stable isotopes (δ18O and δ2H), the research develops a new approach to estimate the average ion concentration rates in soil profiles. This methodology provides a nuanced understanding of how precipitation infiltration recharges groundwater under different land use scenarios and the impact of land use changes on these processes. New hydrological insights for the region: The research findings highlight significant variations in groundwater recharge rates among different land uses. Grasslands show the highest recharge rate at 26 mm/yr, followed by forests at 23 mm/yr, while shrublands have the lowest at 5 mm/yr. Agricultural lands have moderate recharge rates ranging from 14 to 16 mm/yr. Transitioning from agricultural land to other land uses like shrublands and forests results in dramatic changes in recharge rates, with decreases and increases in recharge of 72% and 61%, respectively. These insights are crucial for developing strategic land-use planning to optimize groundwater recharge and enhance water security on the Loess Plateau. The study emphasizes the need for policies that balance hydrological science and land management to sustain both human livelihoods and ecological continuity.

Rodents, particularly mice and rats, are extensively utilized in fundamental neuroscience research. Brain atlases have played a pivotal role in this field, evolving from traditional printed histology atlases to digital atlases incorporating diverse imaging datasets. Magnetic resonance imaging (MRI)-based brain atlases, also known as brain maps, have been employed in specific studies. However, the existence of numerous versions of MRI-based brain atlases has impeded their standardized application and widespread use, despite the consensus within the academic community regarding their significance in mice and rats. Furthermore, there is a dearth of comprehensive and systematic reviews on MRI-based brain atlases for rodents. This review aims to bridge this gap by providing a comprehensive overview of the advancements in MRI-based brain atlases for rodents, with a specific focus on mice and rats. It seeks to explore the advantages and disadvantages of histologically printed brain atlases in comparison to MRI brain atlases, delineate the standardized methods for creating MRI brain atlases, and summarize their primary applications in neuroscience research. Additionally, this review aims to assist researchers in selecting appropriate versions of MRI brain atlases for their studies or refining existing MRI brain atlas resources, thereby facilitating the development and widespread adoption of standardized MRI-based brain atlases in rodents.

The limitations of current drug-eluting stent technologies in selectively inhibiting vascular smooth muscle cell proliferation, which often leads to inflammation, call for innovative approaches in coronary artery disease treatment. In the present work, we propose a revolutionary solution: a three-in-one platform for vascular stents, combining arsenic trioxide (ATO) and tacrolimus (TAC) to address anti-proliferation, pro-endothelialization, and anti-inflammation aspects. Our findings demonstrate that the synergistic action of ATO and TAC effectively suppresses aberrant vascular smooth muscle cell proliferation and mitigates endothelial cell inflammation. Remarkably, the combination treatment of TAC/ATO enhances endothelial cell migration and adhesion abilities. Moreover, our TAC/ATO-eluting stent exhibits superior re-endothelialization and anti-restenosis effects in a rabbit and porcine stent implantation model. Both in vitro and in vivo results solidify the notion that the TAC/ATO-eluting stent ensures rapid re-endothelialization and significantly reduces the incidence of in-stent restenosis. Overall, this study represents a promising and novel multifunctional platform with immense potential in the therapy of coronary artery disease.

Irrigation and nitrogen (N) input are effective methods of balancing crop yields and greenhouse gas (GHG) emissions. However, the effects of irrigation and N management on the GHG emission of winter wheat production systems have not yet been well documented. Therefore, we conducted 2-year field experiments on winter wheat and studied the effects of irrigation and N reduction/replacement on GHG emissions, grain yield, global warming potential (GWP), and GHG emission intensity (GHGI). The winter wheat was irrigated once or twice with 60 or 90 ​mm each time. Five N reduction/replacement treatments were no N application (CK), 25% traditional urea (U) reduction (75%U), traditional U (U), 25% reduction in polymer-coated U (PCU), and 25% reduction in traditional U combined with organic fertilizers (U ​+ ​OM). The results indicated that N reduction/replacement significantly reduced N2O emissions, but had no significant effect on CH4 emissions. Soil water-filled pore space was the same key factor affecting the emission fluxes of N2O and CH4. Soil temperature significantly affected the fluxes of CH4 (P ​

Differences in land use type and chronological age affect soil properties and plant community characteristics, which may influence soil structural stability and erodibility. However, knowledge on the effects of soil physicochemical properties on soil aggregate stability and erodibility at different land use years is limited. This study selected five land use types: corn field (Year 38th-y), corn intercropped with cabbage field (Year 38th-y + b), fruit and meridian forest (Year 6th-jgl), naturally restored vegetation (Year 6th-zr), and artificial forest (Year 7th-rgl) in the karst landscape of the Chishui River Basin in Yunnan Province. We aimed to identify the influencing factors of soil stability and erodibility under different land use time series. The results indicated that the mean weight diameter (MWD), the geometric mean diameter (GMD), and soil structural stability index (SSI values) were highest in Y6th-zr and lowest in Y7th-rgl. Conversely, the erodibility K value was lowest in Y6th-zr, suggesting that the soil structure in Y6th-zr exhibited greater stability, whereas soil stability in Y7th-rgl was lower. Redundancy and throughput analyses revealed that organic carbon and water-stable aggregates > 2.0 mm content had higher vector values. Soil bulk density, total nitrogen, organic carbon, and soil texture content were the main factors contributing to soil stability variation (0.338–0.646). Additionally, total nitrogen, organic carbon, total phosphorus, and soil texture content drove the variation in K values (0.15–1.311). Natural vegetation restoration measures can enhance soil structure to a certain extent. These findings highlight changes in soil aggregate stability and erodibility over different land use durations. The research results have important theoretical and practical significance for understanding the differences in soil erosion and soil restoration under different land use patterns in the karst landscapes of southwest China.

The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to landslides or demonstrate spatial biases, leading to differences in model predictions. To address the impact of non-landslide sample selection on landslide susceptibility predictions, this study uses the western region of Henan Province as a case study. Utilizing historical data, remote sensing interpretation, and field surveys, a sample dataset comprising 834 landslide points is obtained. Ten environmental factors, including elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road, are chosen to establish an evaluation index system. Negative sample sampling areas are delineated based on the susceptibility assessment outcomes derived from the information value model. Two sampling strategies, whole-region random sampling (I) and partition-based random sampling (II), are employed. Random Forest (RF) and Back Propagation Neural Network (BPNN) models are used to forecast and delineate landslide susceptibility in the western region of Henan Province, with prediction accuracy evaluated. The model prediction accuracy is ranked as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). Under the Receiver Operating Characteristic (AUC) curve and accuracy, the II-RF and II-BPNN models exhibit increases in the region by 12.17% and 15.61%, respectively, compared to the I-RF and I-BPNN models. Moreover, the II-BPNN model shows improvements over the I-BPNN model with increases in AUC and accuracy by 14.54% and 16.52%, respectively. This indicates enhancements in model performance and predictive capability. In terms of recall and specificity, the II-RF and II-BPNN models demonstrate increases in recall by 15.09% and 17.47%, respectively, and in specificity by 15.80% and 14.99%, respectively. These findings suggest that the optimized models have better predictive capabilities for identifying landslide and non-landslide areas, effectively reducing the uncertainty introduced by point data in landslide risk prediction.

Crop growth models have been used to develop irrigation and nitrogen schedules (INSs). However, differences in crop cultivar coefficients are often ignored in the development of regional INSs. This study aimed to formulate suitable INSs under spatial heterogeneities in crop cultivar coefficients. Therefore, we propose two strategies for retrieving maize cultivar coefficients by using a data assimilation algorithm and spatial clustering algorithm. The first strategy involves determining the cultivar coefficients for each simulation unit in the examined region and then assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm, with the cultivar coefficients employed as clustering characteristics (CAs). The second strategy involves assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm on the basis of cultivar coefficients and geographical characteristics (CAGCs). By using observational data, the accuracy of cultivar coefficients CAs and CAGCs was compared with that of commonly used regional representative coefficients (RRs) and sub-region representative coefficients (SRRs). Furthermore, we formulated INSs with these four coefficients by using yield maximization as the objective and water use efficiency (WUE) and nitrogen use efficiency (NUE) as constraints. We examined the differences between the INSs, yields, WUE values, and NUE values obtained using each of the aforementioned four coefficients and those obtained using a point optimization approach. The results revealed that the highest accuracy in the simulation of the regional leaf area index, yield, and phenological stage was exhibited by the CAGCs-based strategy, followed by the CAs-based strategy. The RRs-based and SRRs-based strategies produced considerable errors. Crucially, the INSs obtained using the CAGCs-based strategy were more similar to those obtained through point optimization and more reasonable than were the INSs obtained using the other three strategies. In addition, more accurate yield, WUE, and NUE values were obtained with the CAGCs-based strategy than with the other three coefficients-based strategies. The results of this study indicate that the combination of a data assimilation algorithm and spatial clustering algorithm can improve the application potential of crop models in agricultural systems.

To meet the evolving demands of land use and transport interaction (LUTI) assessment within China’s national territory spatial planning (NTSP) system, this paper introduces the level of development (LoD) and the level of matching (LoM) evaluation models, based on the traffic facility and land use factors. The LoD model, founded on the modified node-place model, provides a comprehensive assessment of the traffic facility and land use development scales. Conversely, the LoM model, grounded in data envelopment analysis methods, evaluates the relative relationship between transport services and the travel demand generated by land use. The integrated use of LoD and LoM can both reflect the development scale and matching status between traffic services and travel activities, which are crucial contents in the planning works, especially within the NTSP framework. The proposed models are tested in the city of Guangzhou, and the LoD values exhibit peaks in central urban zones, suburban towns, and areas adjacent to railway transit, with a decline observed in rural farmland and ecological regions. In contrast, the LoM distribution performs a distinct pattern, highlighting numerous underperforming areas with congestion or idle problems in urban centers, alongside well-coordinated regions showcasing a balance between traffic facilities and land uses in rural regions. Furthermore, the LoM scores revealed frequent instances of facility crowding in urban regions and intensive occurrences of facility idleness in rural areas. By marking regions with low LoD scores, the LoD model finds suitable application in determining the urban development border, essential for restricting land development and preserving farmland and ecological areas. Meanwhile, LoM aids in improving urban renewal efforts by assessing and optimizing the balance between intensive land uses and limited traffic facilities. Validated against the existing metrics, the combined use of LoD and LoM efficiently captures the most details of the LUTI process at the lowest computational cost.

Full-film double-ridge furrow sowing technology (FDRFST) and nitrogen (N) management are crucial for sustainable agricultural development. To investigate the N fertilizer application scheme for spring maize with high yield, precipitation water productivity (PWP), and N use efficiency (NUE) under the FDRFST, a two-year field experiment was carried out in a rainfed area of Longzhong (LZ). Treatments included two types of N fertilization (common urea [U] and controlled release urea [CU]), three application rates (180, 225, and 270 kg hm−2), and the conditions of no N fertilizer application and no plastic film mulching. The results showed that the temperature-increase effect of PM on the soil between 2021 and 2022 was concentrated for 0–90 days, with average values of the 5 cm soil temperature increasing by 4.15 and 3.58 ºC, respectively. However, the N fertilizer application rate had negligible effects on soil temperature. Plastic film mulching (PM) required temperature-increase compensation, particularly during the sowing–emergence stage, with a 1.68 compensation coefficient. Based on the simulation results of the modified DNDC model, the recommended application rate of CU (180 kg hm−2) for spring maize in LZ increased the average yield, PWP, and NUE by 15.8%, 16.0%, and 36.4% from 1981 to 2020, respectively, compared to U treatments. Our results provide a theoretical basis for N management in spring maize production in LZ and offer critical insights for improving the DNDC model under PM conditions.

Hydroponic experiments were conducted to investigate the effects of varying particle sizes (5 μm, 0.1 μm, and 0.08 μm) and concentrations (0, 50, 100, 200, 500, 1000, and 2000 mg/L) of polystyrene plastic microspheres (PS-MPs) on the germination and growth of P. sativum seeds in order to investigate the effects of the microplastic environment on crop seed germination and seedling growth. The findings demonstrated that PS-MPs significantly harmed P. sativum seed germination. Specifically, low concentrations or high particle sizes weakened or promoted P. sativum seed germination, indicating varying susceptibilities to PS-MP treatment at different particle sizes. The strongest inhibitory effect on growth was observed under small particle size (0.08 μm) and high concentration (2000 mg/L). Stress had less of an impact on P. sativum in environments with low concentrations of PS-MPs, but in environments with medium and high concentrations, physiological and biochemical indicators like germination rate, stem length, and root length were significantly impacted. Furthermore, the particle size and concentration of PS-MPs had an impact on the growth effect of P. sativum seedlings. The indices of P. sativum seed germination were significantly impacted by the particle size of PS-MPs, despite the fact that PS-MP concentration was low. When the concentration of PS-MPs is low, the effects of varying PS-MP particle sizes on seed germination and root length are as follows: 0.1 μm > 5 μm > 0.08 μm. The concentration of PS-MPs had a significant impact on how it affected the buildup of dry matter and photosynthetic pigment.

Univariate drought indices do not reflect the integrated process through which droughts develop and occur. Therefore, developing a multivariate drought index that accounts for precipitation, evapotranspiration, and soil moisture is crucial for comprehensive drought prediction and evaluation. In this study, a comprehensive agricultural drought index (CADI) was developed using a combination of five commonly used binary copulas by considering evapotranspiration factors based on the Multivariate Standardized Drought Index (MSDI), which is based on both precipitation and soil moisture, and the drought monitoring capability of CADI was evaluated. The relationship between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the North China Plain (NCP) was analyzed, and the drought evolution characteristics and trends in rainfed and irrigated croplands were examined. The results indicated that compared with the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSI), and MSDI, the proposed CADI accurately reflected the integrated process of meteorological and agricultural drought occurrence and evolution. In addition, the strength of the correlation between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the NCP surpassed that of the SPEI, SSI, and MSDI, thereby confirming the validity and reliability of the proposed CADI for drought monitoring. Over the past four decades, droughts in the rainfed and irrigated croplands of the NCP have demonstrated a decreasing trend, and severe droughts have been concentrated in winter. Overall, the results of this study can be used in agrometeorological drought early warning and agricultural impact assessment and can provide theoretical guidance for drought monitoring in other regions.

Abstract Hypoxia and high accumulation of lactic acid in the tumor microenvironment provide fertile soil for tumor development, maintenance and metastasis. Herein, we developed a calcium peroxide (CaO2)-loaded nanostructure that can play a role of “one stone kill two birds”, i.e., acidic and hypoxic tumor microenvironment can be simultaneously regulated by CaO2 loaded nanostructure. Specifically, CaO2-loaded mesoporous polydopamine nanoparticles modified with sodium hyaluronate (denoted as CaO2@mPDA-SH) can gradually accumulate in a tumor site. CaO2 exposed in acidic microenvironment can succeed in consuming the lactic acid with oxygen generation simultaneously, which could remodel the acid and hypoxia tumor microenvironment. More importantly, the relief of hypoxia could further reduce lactate production from the source by down-regulating the hypoxia inducible factor-1α (HIF-1α), which further down-regulated the glycolysis associated enzymes including glycolysis-related glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA). As a result, CaO2@mPDA-SH alone without the employment of other therapeutics can dually regulate the tumor hypoxia and lactic acid metabolism, which efficiently represses tumor progression in promoting immune activation, antitumor metastasis, and anti-angiogenesis.

Using crop models to develop reasonable irrigation and nitrogen schedules (INSs) is critical for achieving high yields and maximizing water use efficiency (WUE) and nitrogen use efficiency. However, the INSs developed based on crop parameters determined through field experiments have low robustness at the regional scale. Remote sensing data indicate the growth conditions of regional crops, and representative regional crop parameters can be determined using remote sensing data. The objective of the present study was to develop a regional INS by adopting a crop model with crop parameters calibrated using the remotely sensed leaf area index (LAI). First, the crop parameters of a field-scale crop model were calibrated on the basis of field-scale trial data for four summer maize growing seasons. Sensitive crop parameters at the regional scale were then identified using an assimilation algorithm based on the remotely sensed LAI. The rationality of using the remotely sensed LAI for determining regional crop parameters was demonstrated by simulating regional maize growth. Finally, a regional-scale crop model was developed and coupled with a multiobjective genetic algorithm to optimize the regional INS for different typical-rainfall years. The results indicated that the regional-scale crop model was more accurate than was the field-scale crop model in simulating the regional LAI, plant height, soil water content, and yield and the corresponding RMSE decreased by 0.50 m2/m2, 0.15 m, 0.02 cm3/cm3, 344.11 kg/ha. Compared with the INSs currently used by farmers, the optimized INS was associated with a marginally higher irrigation quantity (average increase of 2.66%) but a significantly lower nitrogen application rate (average decrease of 31.6%). The crop yield, WUE, and partial factor productivity for nitrogen were 6.35–12.51%, 2.64–10.41%, and 61.8–66.2% higher, respectively, in different typical-rainfall years when using the optimized INS than when using an unoptimized INS. The results of this study indicate that a regional INS established on the basis of regional crop model parameters estimated using remote sensing data can aid irrigation and nitrogen application scheduling.

Non-structural carbohydrates (NSC) as resource reserves of plants play important roles in energy supply for normal growth and reproduction under environmental stress. The yield of perennial crops is mainly determined by the carbohydrate production and allocation in the current growth season, as well as the re-allocation of NSC reserves. However, the contribution of NSC to crop yield has not been fully determined. Fengdan (Paeonia ostii) is a variety of oil Peony that is newly developed in China. The effects of tree age and NSC on yield were examined by investigated the variations of biomass, soluble sugars, starch, and NSC in the organ and whole tree levels in the dormant and ripening stages of Fengdan populations with 4-, 6-, and 8-year-old in 2020 and 5-, 7-, and 9- year old in 2021. Results showed that the biomass, yield (seed biomass), soluble sugars, starch, and NSC reserve of Fengdan at the whole tree level increased with the increase in age. Although consistent correlations were observed between soluble sugars, starch and NSC storage, and yield among the plants with different ages, Fengdan showed allometric growth relationships between the accumulation of soluble sugars, starch, and NSC and yield and biomass (standardized major axis analyses slope b ≠ 1). Tree age significantly affected biomass and its allocation and NSC levels, especially the yield of Fengdan plants. The results of the investigation of the variations in the relationships between the yield and seasonal fluctuations of NSC and biomass indicate that roots is the key storage structure, whereas stems both serve as sink and/or source functions for the adult (7–9a) plants. NSC level, particularly the concentration of soluble sugars, in stems mainly influences Fengdan yield. These findings together provide new insights into the mechanisms underlying the yield formation of Fengdan and have implications for manipulating sink-source relationship to achieve high yield.

Manganese dioxide (MnO2) nanosheets are regarded as a new class of two-dimensional nanomaterials with several attractive properties with enormous progress in biomedical fields. Gold nanoparticles (AuNPs) are also important biocompatible nanomaterials with unusual optical properties. Hetero-nanostructure of MnO2 and AuNPs with the medium of DNA is an interesting topic. In this work, the protection of the hetero-nanostructure from salt-induced aggregation is systematically investigated including the effects of sequence length, reagents concentrations, incubation time and temperature. The MnO2@Au nanostructures are thus applied for the analysis of miRNA. Duplex-specific nuclease (DSN) catalyzed digestion, hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA) are utilized for signal amplification. By finally analyzing the optical responses of the nanocomponents, highly sensitive analysis of target miRNA can be achieved. Excellent analytical performances are attributed to the unique features of MnO2@Au nanostructures and signal amplification designs. They are promising basis for the construction of novel biosensors for clinical applications.

The prediction of the daily crop leaf area index (LAI) plays a crucial role in forecasting crop growth trends and guiding field management decisions in the realm of scientific research. However, research on the daily prediction of LAI is scarce, and the challenges associated with acquiring sufficient training data pose limitations to the application of machine learning in this context. This study aimed to synergize the strengths of data assimilation and machine learning algorithms to forecast the daily LAI of maize. Initially, a data assimilation algorithm was employed to minimize the disparity between moderate-resolution imaging spectroradiometer-derived LAI and LAI generated through the CERES-Maize model. This effort resulted in a dataset comprising 289 LAI curves. Building upon this dataset, long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF) algorithms were formulated, incorporating N-day LAI input history (N = 5, 10, 15, 20, and 25) to predict LAI for days N + 1 to N + 15. The outcomes revealed that, in contrast to the LAI simulated by the crop model before assimilation, the assimilated LAI closely approximated the observed LAI, with an R2 value of 0.90 and an RMSE of 0.44 m2/m2. Furthermore, when compared to SVR and RF, the LSTM-based LAI prediction model exhibited superior accuracy at N = 15, achieving R2 values of 0.99 and 0.99 for the training and testing datasets, respectively, along with RMSE values of 0.12 and 0.14 m2/m2. It was evident that data assimilation supplied an ample number of samples for the training of machine learning algorithms. The integration of data assimilation technology with machine learning algorithms proved to be an effective methodology for forecasting daily crop LAI.

Abstract Soil gas diffusivity (Dp/Do, gas diffusion coefficients in soil and in free air, respectively) and its relation to soil moisture is of great importance for describing and quantifying essential provisional and regulatory functions associated with terrestrial ecosystems such as soil aeration and greenhouse gas (GHG) emissions. Because gas migration in terrestrial soil systems is predominantly diffusion controlled, soil gas diffusivity becomes a fundamental prerequisite to quantify diffusive gas fluxes. Descriptive–predictive models are often used to estimate Dp/Do from easily measurable soil physical properties. Most of the available models take the form of power‐law functions and often tend to mischaracterize soil moisture effects at high moisture regimes. Based on a wide range Dp/Do data available in literature representing both intact and repacked soils, this study developed a novel air‐saturation‐dependent exponential (ASEX) gas diffusivity model to model Dp/Do in relation to soil air saturation. The model variable α, which represents the diffusivity at half air saturation normalized by the same in complete soil air saturation, could potentially differentiate moisture effects on different soil structural states. For specific applications in intact soils, we propose corresponding α values for upper‐limit (α = .6) and lower‐limit (α = .05) estimates of diffusivity, while an average value (α = .3) for general applications in both intact and repacked soils. As expected, our model based on a few a priori measured supportive data showed a better performance over the classical predictive models that do not use such measurements. The new model was further used to derive useful implications to showcase soil density effects on Dp/Do.

China implemented a one-month lockdown after the 2020 Spring Festival to prevent the spread of COVID-19. The closure measures provide a rare opportunity to understand the resulting changes in air pollution levels and to test the effectiveness of previous environmental protection measures. We used the time series decomposition method to quantify the air pollution in Lanzhou during the closure period. The results showed that during the epidemic lockdown period, although the concentration of SO2 in Lanzhou decreased substantially, there was a significant increase in the concentration of O3 (by 19.14%), followed by a gradual return to the normal level. Most of the changes during the COVID-19 lockdown were within the range of fluctuations over the past five years. The trend of decreasing SO2 and CO in 2020 was less than that during 2015–2019, and the continuous decline of the PM10 concentration exceeded expectations. NO2, PM2.5 and O3 maintained the trend of the previous five years. Our results show that temporary social closure measures have a limited effect on improving air quality in Lanzhou, and they emphasize the importance of reducing the O3 concentration in the future.

With the development of technologies based on gold nanoparticles (AuNPs), bare AuNPs cannot meet the increasing requirements of biomedical applications. Modifications with different functional ligands are usually needed. DNA is not only the main genetic material, but also a good biological material, which has excellent biocompatibility, facile design, and accurate identification. DNA is a perfect ligand candidate for AuNPs, which can make up for the shortcoming of bare AuNPs. DNA-modified AuNPs (DNA-AuNPs) have exciting features and bright prospects in many fields, which have been intensively investigated in the past decade. In this review, we summarize the various approaches for the immobilization of DNA strands on the surface of AuNPs. Representative studies for biomedical applications based on DNA-AuNPs are also discussed. Finally, we present the challenges and future directions.

In the context of global warming and intensified human activities, the quantitative assessment of the combined effects of land use/cover change (LUCC) and climate change on the hydrological cycle is crucial. This study was based on the simulation results of future climate and LUCC in the Jinghe River Basin (JRB) using the GFDL–ESM2M and CA–Markov combined with the SWAT models to simulate the runoff changes under different scenarios. The results revealed that the future annual precipitation and average temperature in the JRB are on the increase, and the future LUCC changes are mainly reflected in the increase in forest and urban lands and decrease in farmlands. Changes in runoff in the JRB are dominated by precipitation, and the frequency of extreme events increases with the increase in the concentration of CO2 emissions. Under four climate scenarios, the contribution of future climate change to runoff changes in the JRB is −8.06%, −27.30%, −8.12%, and +1.10%, respectively, whereas the influence of future LUCC changes is smaller, ranging from 1.14–1.64%. In response to the future risk of increasing water-resources stress in the JRB, the results of this study can provide a scientific basis for ecological protection and water-resources management and development.

Owing to serious environmental and climatic impacts of increasing carbon dioxide (CO2) concentrations, there is an urgent need for the development of efficient CO2 capture methods. Carbonic anhydrases (CAs) can mediate CO2 capture via a rapid reaction between CO2 and bicarbonate ions. However, because of their stability, most of the CAs are not suitable for use in hostile environments (high temperature, high alkalinity, high pressure, and solvent). Therefore, this review explores thermophilic microorganisms in submarine hydrothermal environments as a valuable source of thermostable tolerant CAs, and highlights the questions and future directions that must be addressed for the application of CAs in CO2 capture.

Objective: Arsenic trioxide (ATO or As2O3) has beneficial effects on suppressing neointimal hyperplasia and restenosis, but the mechanism is still unclear. The goal of this study is to further understand the mechanism of ATO's inhibitory effect on vascular smooth muscle cells (VSMCs). Methods and results: Through in vitro cell culture and in vivo stent implanting into the carotid arteries of rabbit, a synthetic-to-contractile phenotypic transition was induced and the proliferation of VSMCs was inhibited by ATO. F-actin filaments were clustered and the elasticity modulus was increased within the phenotypic modulation of VSMCs induced by ATO in vitro. Meanwhile, Yes-associated protein (YAP) nuclear translocation was inhibited by ATO both in vivo and in vitro. It was found that ROCK inhibitor or YAP inactivator could partially mask the phenotype modulation of ATO on VSMCs. Conclusions: The interaction of YAP with the ROCK pathway through ATO seems to mediate the contractile phenotype of VSMCs. This provides an indication of the clinical therapeutic mechanism for the beneficial bioactive effect of ATO-drug eluting stent (AES) on in-stent restenosis (ISR).

Highly automated vehicles are regarded as the next revolution of the transport system. Automated vehicles include a spectrum from vehicles with driver assistance systems through to highly automated vehicles. These vehicles will only gradually appear in the overall vehicle fleet. Their impact as part of future traffic is of reference value for transport decision-makers. The present paper starts from assumptions for the shares of vehicles with different levels of automation in 2030 and 2050 (representing the near and far-distant future) and compares the effects of these automated vehicles on traffic flow using microscopic traffic simulations. The simulated vehicles include non-assisted vehicles, semi-automated vehicles with driver assistance systems, and fully autonomous vehicles. To obtain a more realistic result, a traffic scenario of the city of Duisburg is used in this thesis. With the support of the city administration, existing data of the origin/target matrix, detector data including induction loops, and cameras were available. Thus, the data of the origin/target matrix are used to generate the real traffic scenario and the detector data to investigate the accuracy of the generated traffic. The result shows that automated vehicles would have a positive impact on traffic, a proportion of automated vehicles can reduce the average travel time. For areas with different traffic conditions, the degree of impact of automated vehicles can be very different.

Abstract Clay content in soil potentially has strong influence on soil structure and thereby controls soil physical, hydraulic, and gas transport properties. Only few studies have investigated the effect of clay content on saturated hydraulic conductivity (Ks) and gas transport parameters such relative gas diffusivity (Dp/D0) and air permeability (Ka) under varying conditions of clay contents. In this study, two clay gradients were investigated: a natural clay gradient and an induced (clay mineral attapulgite‐controlled) clay gradient. We determined the effect of the two clay gradients on soil air and water parameters and identified the potential relationships between these parameters and other physical properties. The results indicated that all Dp/D0, Ka, and Ks decreased with increasing clay content. The differences among the various treatments were dependent on the soil pore characteristics (air‐filled porosity, ε; pore continuity, POA; pore tortuosity, τ; and pore size distribution). Under the natural clay gradient, the increased clay content resulted in 52.8% more micropores and 35.2% fewer macropores. The slope of τ vs. clay content under the induced clay gradient was found to be higher than that under the natural gradient. Statistically meaningful correlations could be found between Ks and Ka (|r| = .904) and also between Ks and Dp/D0 (|r| = .810), whereas Dp/D0 was evidenced to be better correlated with Ks regardless of the type of clay gradient (P

Accurate inversion of soil hydraulic parameters based on the van Genuchten–Mualem model has received much attention in soil science research. Herein, a hybrid algorithm method using particle swarm optimization and vector-evaluated genetic algorithm was used to invert the parameters θs, α, n, and Ks, with the objective functions of infiltration rate, cumulative infiltration, and soil water content. Then, numerical experiments were conducted on four typical soils at three initial water content levels (20, 40, and 60% effective saturation) to verify the accuracy of the inverse method. The results showed that the inversed soil water retention and conductivity curves were approximately the same as the real curves, with the root mean square errors of 0.00101–0.00192 cm3·cm−3, 0.00800–0.02519 cm3·cm−3, respectively, and both the Nash-Sutcliffe coefficients were approximately 1.0. Additionally, laboratory experiments were also performed to compare with the inversed parameters for verification, within small root mean squared errors and approximately 1.0 Nash–Sutcliffe coefficients. Furthermore, the method can also achieve acceptably accurate parameter inversion even with substantial measurement errors included in the cumulative infiltration, initial water content, and final water content. Thus, the method is effective and robust and found to be practical in field experiments.

Given the increasing prevalence of obesity, the white-to-beige adipocyte conversion has attracted interest as a target for obesity treatment. Gamma-aminobutyric acid (GABA) treatment can reduce obesity, but the underlying mechanism remains unclear. Here, we aimed to investigate the mechanism by which GABA triggers weight loss by improving the beiging of inguinal white adipose tissue (iWAT) and the role of gut microbiota in this process. The results showed that GABA reduced body weight and adipose inflammation and promoted the expression of thermogenic genes in the iWAT. The 16S rRNA sequence analysis of gut microbiota showed that GABA treatment increased the relative abundance of Bacteroidetes, Akkermansia, and Romboutsia and reduced that of Firmicutes and Erysipelatoclostridium in obese mice. Additionally, serum metabolomic analysis revealed that GABA treatment increased 3-hydroxybutyrate and reduced oxidized lipid levels in obese mice. Spearman’s correlation analysis showed that Akkermansia and Romboutsia were negatively associated with the levels of oxidized lipids. Fecal microbiota transplantation analysis confirmed that the gut microbiota was involved in the white-to-beige adipocyte reconstruction by GABA. Overall, our findings suggest that GABA treatment may promote iWAT beiging through the gut microbiota in obese mice. GABA may be utilized to protect obese people against metabolic abnormalities brought on by obesity and gut dysbiosis.

Under conditions of continuous global warming, research into the future change trends of regional dry-wet climates is key for coping with and adapting to climate change, and is also an important topic in the field of climate change prediction. In this study, daily precipitation and mean temperature datasets under four representative concentrative pathway (RCP) scenarios in the geophysical fluid dynamics laboratory Earth system model with modular ocean model (GFDL-ESM2M) version 4 were used to calculate the standardized precipitation-evapotranspiration index of the Tibetan Plateau (TP) at different time scales. Using a multi-analytical approach including the Mann–Kendall trend test and run theory, the spatiotemporal variation characteristics of drought in the TP from 2016 to 2099 were studied. The results show that the overall future climate of the TP will develop towards warm and humid, and that the monthly-scale wet–dry changes will develop non-uniformly. As the concentration of carbon dioxide emissions increases in the future, the proportion of extremely significant aridification and humidification areas in the TP will significantly increase, and the possibility of extreme disasters will also increase. Moreover, influenced by the increase of annual TP precipitation, the annual scale of future drought in the region will tend to decrease slightly, and the spatial distributions of the frequency and intensity of droughts at all levels will develop uniformly. Under all four RCP scenarios, the drought duration of the TP was mainly less than 3 months, and the drought cycle in the southern region was longer than that in the northern region. The results of this study provide a new basis for the development of adaptive measures for the TP to cope with climate change.

Soil-gas diffusivity and its variation with soil moisture plays a fundamental role in diffusion-controlled migration of climate-impact gases from different terrestrial agroecosystems including cultivated soils and managed pasture systems. The wide contrast in soil texture and structure (e.g., density, soil aggregation) in agriculture topsoils (0–10 cm) makes it challenging for soil-gas diffusivity predictive models to make accurate predictions across different moisture conditions. This study characterized gas diffusivity and gas-phase tortuosity in soils sampled from managed pasture and cultivated sites in Sri Lanka at 0–10 cm depth, together with selected soil-gas diffusivity data from the literature. Soil-gas diffusivity was measured using a one-chamber diffusion apparatus using N2 and O2 as experimental gases. The measured diffusivity, together with literature data representing both intact and repacked soils, were tested against five existing widely known gas diffusivity predictive models. The tested models tended to mischaracterize the two-region behavior in some of the aggregated soils, suggesting the need of soil-specific diffusivity models to better describe gas diffusivity in agricultural soils. We suggested a new parametric two-region model, developed in line with literature-based models, to represent both unimodal and bimodal/two-region behavior of selected soils. The new model statistically outperformed the existing predictive models for both intact and repacked soils and, hence, demonstrated its applicability to better characterize site-specific greenhouse gas emissions under different soil water regimes.

Vibrio vulnificus is an important pathogenic bacterium that is often associated with seafood-borne illnesses. Therefore, to detect this pathogen in aquatic products, a DNAzyme-based fluorescent sensor was developed for the in vitro detection of V. vulnificus. After screening and mutation, a DNAzyme that we denominated “RFD-VV-M2” exhibited the highest activity, specificity, and sensitivity. The limit of detection was 2.2 × 103 CFU/ml, and results could be obtained within 5–10 min. Our findings suggested that the target of DNAzyme RFD-VV-M2 was a protein with a molecular weight between 50 and 100 kDa. The proposed biosensor exhibited an excellent capacity to detect marine products contaminated with V. vulnificus. Therefore, our study established a rapid, simple, sensitive, and highly specific detection method for V. vulnificus in aquatic products.

The North China Plain is a major grain-producing area, but faces water scarcity, which directly threatens food security. The problem is more severe under climate change and the seasonal impact of climate change on winter wheat is different. Thus, it is of great importance to explore the spatiotemporal characteristics of irrigation requirements (IR) and the factors influencing IR in different growth periods of winter wheat, but it has not received much attention. Therefore, we used relative contribution, partial correlation and path analyses to assess the spatiotemporal characteristics of the IR and primary factors influencing the IR of winter wheat in various growing stages in the North China Plain. The results indicated that wind speed and net solar radiation showed a significant downward trend; no prominent trend was noted in IR (multiyear average, 302.3 mm). Throughout the growing season of winter wheat, IR increased gradually from the southern to northern extent of the North China Plain. The irrigation demand of winter wheat in stage P2 (green-up to heading) was the largest. Furthermore, the dominant drivers of IR in terms of spatial distribution and inter-annual variation were phenological period (Phe), effective precipitation (Pe) and relative humidity (RH); however, the degree of their effects varied across the growth stages and growing regions of winter wheat. Each factor exerted both direct and indirect effects on IR and Phe exhibited the strongest indirect effect on IR. The major factors contributing most to IR were Pe and RH in the P1 stage (sowing to green-up) and Phe, Pe and RH in the P2 and P3 (heading to maturity) stages. Pe and RH limited IR, whereas Phe promoted it. Our findings will help improve agricultural water management in the future.

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI.

In vivo beta-cell neogenesis may be one way to treat diabetes. We aimed to investigate the effect of glucagon-like peptide-1 (GLP-1) on beta-cell neogenesis in type 2 diabetes mellitus (T2DM). Male C57BL/6J mice, 6 wk old, were randomly divided into three groups: Control, T2DM, and T2DM + Lira. T2DM was induced using high-fat diet and intraperitoneal injection of streptozotocin (40 mg/kg/d for 3 d). At 8 wk after streptozotocin injection, T2DM + Lira group was injected intraperitoneally with GLP-1 analog liraglutide (0.8 mg/kg/d) for 4 wk. Apparently for the first time, we report the appearance of a primitive bud connected to pancreas in all adult mice from each group. The primitive bud was characterized by scattered single monohormonal cells expressing insulin, GLP-1, somatostatin, or pancreatic polypeptide, and four-hormonal cells, but no acinar cells and ductal epithelial cells. Monohormonal cells in it were small, newborn, immature cells that rapidly proliferated and expressed cell markers indicative of immaturity. In parallel, Ngn3 + endocrine progenitors and Nestin + cells existed in the primitive bud. Liraglutide facilitated neogenesis and rapid growth of acinar cells, pancreatic ducts, and blood vessels in the primitive bud. Meanwhile, scattered hormonal cells aggregated into cell clusters and grew into larger islets; polyhormonal cells differentiated into monohormonal cells. Extensive growth of exocrine and endocrine glands resulted in the neogenesis of immature pancreatic lobes in adult mice of T2DM + Lira group. Contrary to predominant acinar cells in mature pancreatic lobes, there were still a substantial number of mesenchymal cells around acinar cells in immature pancreatic lobes, which resulted in the loose appearance. Our results suggest that adult mice preserve the capacity of pancreatic neogenesis from the primitive bud, which liraglutide facilitates in adult T2DM mice. To our knowledge, this is the first time such a phenomenon has been reported.

The renin-angiotensin system (RAS) is involved in the pathogenesis of non-alcoholic fatty liver disease (NAFLD) and represents a potential therapeutic target for NAFLD. Glucagon-like peptide-1 (GLP-1) signaling has been shown to regulate the RAS within various local tissues. In this study, we aimed to investigate the functional relationship between GLP-1 and the local RAS in the liver during NAFLD. Wild-type and ACE2 knockout mice were used to establish a high-fat-induced NAFLD model. After the mice were treated with liraglutide (a GLP-1 analogue) for 4 weeks, the key RAS component genes were up-regulated in the liver of NAFLD mice. Liraglutide treatment regulated the RAS balance, preventing a reduction in fatty acid oxidation gene expression and increasing gluconeogenesis and the expression of inflammation-related genes caused by NAFLD, which were impaired in ACE2 knockout mice. Liraglutide-treated HepG2 cells exhibited activation of the ACE2/Ang1-7/Mas axis, increased fatty acid oxidation gene expression, and decreased inflammation, which could be reversed by A779 and AngII. These results indicate that the local RAS in the liver becomes overactivated in response to NAFLD. Moreover, ACE2 knockout increases the severity of liver steatosis. Liraglutide has a negative and antagonistic effect on the ACE/AngII/AT1R axis, a positive impact on the ACE2/Ang1-7/Mas axis, and is mediated through the PI3K/AKT pathway. This may represent a potential new mechanism by which liraglutide improves NAFLD.

Abstract Several earlier studies reported that microplastics (MP) accumulated on soil surfaces could be transported into the subsoil and ingested by soil biota, such as earthworms. The present study explores how networks of earthworm burrows and MP (low‐density polyethylene, LDPE) in subsoil affect the soil hydraulic properties and saturated water flow. A repacked and saturated sandy soil column experiment was conducted in an environment‐controlled laboratory with earthworms (anecic, Lumbricus terrestris) inoculated into the soil columns to form networks of macropore. The macropore network parameters (i.e., number, length, volume, diameter, soil saturated conductivity, and tracer breakthrough curves of soil columns) have been determined. The relative arrival times of the tracer mass (i.e. T5%, T25%, and T50%) were determined in order to describe the shapes of the breakthrough curves. The results show that in some breakthrough curves for the treatments with earthworms, there are two peaks. This is an indication that water was flowing faster in the macropores than in the soil matrix. There is a significant correlation between 5% arrival time and the median burrow volume, and the correlation coefficient was .571 (at the level of p

Applying soil amendments plays a critical role in relieving water stress in arid and semiarid areas. The natural clay mineral attapulgite (ATP) can be utilized to adjust the balance of water and soil environment. In this study, we investigated four different particle size distribution typical soils in the Loess Plateau: (1) lou soil (LS), (2) dark loessial soil (DS), (3) cultivated loess soil (CS), (4) sandy soil (SS). Five ATP application rates (0, 1%, 2%, 3%, and 4%) were selected to test the effect of ATP on the soil water retention curve, soil saturated hydraulic conductivity, and soil structure. The results showed that applied ATP significantly increased the soil clay content, and the relative change of SS with 3% ATP applied increased by 53.7%. The field water holding capacity of LS, DS, CS, and SS with 3% ATP applied increased by 8.9%, 9.6%, 18.2%, and 45.0%, respectively. Although applied ATP reduced the saturated hydraulic conductivity, the values of CS and SS were opposite when the amount of ATP applied was >3%. The relative change in the amount of 0.25–1 mm soil water-stable aggregates of SS was 155.9% when 3% ATP was applied. Applied ATP can enhance soil water retention and soil stability, which may improve limited water use efficiency and relieve soil desiccation in arid and semiarid areas or similar hydrogeological areas.

Optimization of water distribution networks can effectively reduce their annual cost, which includes the average investment for each year the operational costs and depreciation costs. However, the existing optimization models rarely directly consider the basic flow of each node in case of accidents, such as pipe bursts. Therefore, it is necessary to check the flow requirements under accident conditions. In order to deal with these drawbacks, two optimization models are established considering accident conditions: a single-objective optimization model considering annual cost as an economic objective, and a multi-objective optimization model with a reliability objective defined by the surplus water head. These models are solved based on the genetic algorithm, non-dominated sorting genetic algorithm-II algorithm and Levenberg-Marquardt iterative method. Applying two cases of a single pump station and a multi pump water station water supply, it is shown that the annual cost when considering the accident conditions is higher than that without considering the accident conditions. Moreover, the annual cost obtained with the multi-objective optimization model is slightly higher than that obtained with the single-objective optimization model. The cost is higher because the former model reduces the average surplus water head, which can improve the water distribution network reliability. Therefore, the model and optimization algorithm proposed in this paper can provide a general and fast optimization tool for water distribution network optimization.

A traffic simulation of the Jianghan Zone in Wuhan, China was carried out. In order to simulate genuine traffic flow without traditional hard-to-implement data collection methods, geographic population distribution data were gathered from the public information and traffic flow was generated by ActivityGen in SUMO (Simulation of Urban Mobility). For the sake of discovering the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data on same time of each road in this area was compared. The results show that traffic flow generated from geographic population distribution data has referential meanings and with more detailed model classification, simulated traffic data can be closer to real conditions. This may offer a new way to generate traffic flow for researchers working in traffic simulation area. Further improvement of the accuracy in traffic flow generation by geographic population needs to pay more attention on special places like hospital and train stations.

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: the first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately.

This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km2 and Origin/Destination (OD) matrix area over 800 km2 was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%.

Intelligent vehicles gradually enter the vehicular fleet with advanced driver-assistance technologies. Their impact on traffic should, therefore, be considered by transportation decision-makers. This paper examines the effect of vehicles with different levels of automation on traffic flow, such as non-assisted vehicles, vehicles with driver assistance systems, and fully autonomous vehicles. The accuracy of the examined traffic scenario is also an important factor in microscopic traffic simulation. In this paper, the central part of the city of Duisburg, Duisburg’s inner ring, is chosen for the traffic scenario. Through the cooperation with local government, official data of Origin/Destination matrices, induction loops, and traffic light plans are provided for this work. Thus, traffic demand from Origin/Destination matrices and induction loops are generated and compared, respectively. Finally, vehicles with different levels of automation are simulated in the Duisburg inner ring scenario.

Drought and uneven distribution of precipitation during stages of crop growth exert a severe reduction on crop yield. It is therefore necessary to evaluate the impact of drought on crop yields. In this study, data from a two-year (2016 and 2017) field experiment were used to calibrate and evaluate the parameters of the Decision Support System for the Agrotechnology Transfer (DSSAT) model. The evaluation model was then employed to analyze the impact of potential drought on the yield of summer maize (Zea mays L.) over different growth stages for 46 years (1970–2015). The simulated summer maize flowering and harvest date differed by three and one days of the observed in 2017. The d-index value and the normalized root-mean-square error (nRMSE) of the simulated and measured values were 0.90 and 3.72%, 0.95 and 10.21%, and 0.92 and 13.12%, for summer maize yield, soil water content, and leaf area index, respectively. This indicates that the parameters of the DSSAT model were extremely reliable and that the simulation results were better. The yield reduction of summer maize was concentrated within the range of 0–40% from 1970 to 2015, and the two-stage yield reduction was higher than the one-stage yield reduction. The highest probability of yield reduction occurs if drought occurs during jointing and heading stages. Irrigation is therefore recommended during jointing stage or heading stage. If local irrigation conditions permit, irrigation can be carried out both at the jointing and heading stages. This study provides a theoretical basis for drought resistance management and scientific irrigation of summer maize in the western Guanzhong plain.

A six-dimensional nonlinear hydropower system controlled by a nonlinear predictive control method is presented in this paper. In terms of the nonlinear predictive control method; the performance index with terminal penalty function is selected. A simple method to find an appropriate terminal penalty function is introduced and its effectiveness is proved. The input-to-state-stability of the controlled system is proved by using the Lyapunov function. Subsequently a six-dimensional model of the hydropower system is presented in the paper. Different with other hydropower system models; the above model includes the hydro-turbine system; the penstock system; the generator system; and the hydraulic servo system accurately describing the operational process of a hydropower plant. Furthermore, the numerical experiments show that the six-dimensional nonlinear hydropower system controlled by the method is stable. In addition, the numerical experiment also illustrates that the nonlinear predictive control method enjoys great advantages over a traditional control method in nonlinear systems. Finally, a strategy to combine the nonlinear predictive control method with other methods is proposed to further facilitate the application of the nonlinear predictive control method into practice.