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To unravel the general relationship between bus travel and land use around bus stops and along bus routes and to promote their coordinated development, this paper explores a method to estimate passenger flow volumes from and to bus stops based on land use types, intensities, and spatial distributions around bus stops and along bus routes. Firstly, following the principle of the gravity model, which considers traffic volumes analogous to gravity based on trip generation and distance impedance between traffic analysis zones (TAZs), a gravitational logic estimation method for passenger flow volumes from and to bus stops was constructed with land use elements between bus stop TAZs and the upstream and downstream collections of bus stop TAZs. Building upon this, the passenger flow volumes from and to 38 bus stops in the Xueyuan Square area of Dalian during weekday morning peak hours were taken as the experimental objects. The basic estimation models of two gravity sets corresponding to passenger flow volumes from and to bus stops were constructed using the bus travel generation based on the aggregation of area-based origin unit method and the bus travel distance impedance based on the probability density method. Finally, the reliability of the estimation method of passenger flow volumes from and to bus stops was verified by regression fitting between the surveyed values of passenger flow volume and the estimated values of the basic models. The results indicate that the fuzzy estimation and transformation of bus travel based on land use elements, which serves as a crucial lever for facilitating strategic alignment in transit-oriented development (TOD), can be effectively achieved by using the area-based origin unit method to aggregate bus travel generation and the probability density method to evaluate the bus travel distance impedance.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

Observations of the effects of long-term paddy-upland rotations on soil carbon (C) and nitrogen (N) stocks are scarce. This experiment aimed to examine soil C and N stock characteristics and their relationships with rice yield using four long-term rotation systems. The results showed that in the topsoil at 50 cm, compared to rice-fallow (RF), rice-potato with rice straw mulch and return (RP) and rice-Chinese milk vetch (RC) increased soil organic carbon (SOC) stock by 19.67% and 15.29%, total nitrogen (TN) stock by 20.40% and 18.54%, and available nitrogen (AN) density by 61.54% and 34.44%, respectively; while rice-wheat (RW) increased AN density by 30.24%. Furthermore, the increases in densities of soil C and N at 20–30 cm were likely to increase rice above biomass and yield. The highest soil C and N densities were observed in RP, which was the best choice for increasing soil nutrients and rice yield. In addition, SOC, TN, and AN had obvious stratification and surface aggregation characteristics, and more than 70% of the total stocks were concentrated at the top 30 cm. These results provide a theoretical basis for the high-yield cultivation of rice in the middle and lower reaches of the Yangtze River.

Mid−season drainage (MSD) is a widely used water management practice in rice (Oryza sativa L.) cultivation. However, the timing of the initiation and termination of MSD is highly arbitrary and subjective, and a quantitative indicator is lacking in precision agronomic practice. In this study, datasets (91 cases) from previous field experiments were obtained and used to fit adjusted exponential growth models, incorporating rice canopy light interception (CLI) and tillering development dynamics. Different criteria for initiating and terminating MSD were developed based on CLI indicators. The results showed that the CLI indicator at 80% of the projected panicle could be used to predict the initiation of MSD; however, it was highly variable, depending on the growing season and rice cultivated variety. The values for Indica rice were 0.26 to 0.31 and 0.31 to 0.42 in the late and single seasons, respectively, while the values for Japonica rice were 0.15 to 0.29 and 0.23 to 0.33, respectively. In addition, the CLI values at 7 to 10 days prior to panicle initiation (PI) ranged from 0.77 to 0.87 and from 0.56 to 0.83 for the Indica and Japonica varieties, respectively, and were calculated to determine MSD termination. The CLI indicators for MSD were greatly dependent on the cultivated variety, growing season, and planting method. The results of the correlation study and principal component analysis (PCA) indicated that the differences in CLI values attributed to rice type and growing season were driven by the tillering and canopy characteristic traits, respectively. Therefore, the current parameters could provide a reference for subsequent field applications in specific areas, but further optimization is needed to increase their robustness. To evaluate the usefulness of the CLI indicator for determining MSD initiation and termination, a set of devices for monitoring canopy light interception and water level was developed, and an on-farm trial was carried out in the middle and lower reaches of the Yangtze River in China in 2022. The field application demonstrated that MSD could be scheduled automatically based on the current system, and that the effect was consistent in practice.

The magnetic properties of 50 nm Ni80Fe20 deposited on Cu underlayers with different thicknesses by obliquely sputtering were studied. It was found that the in-plane uniaxial magnetic anisotropy (IPUMA) of the Ni80Fe20 film can be induced by the obliquely sputtered Cu underlayer deposited under the NiFe layer. The IPUMA field of NiFe film varies between 20 Oe and 40 Oe when the thickness of Cu underlayer varies from 5 nm to 50 nm. The permeability spectrum results show that the damping factor increases with increasing Cu underlayer thickness. This indicates that changing the thickness of the Cu underlayer of obliquely sputtering is an effective method to adjust the damping factor in the dynamic magnetization process of Ni80Fe20 thin films.

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

Pseudomonas aeruginosa is an opportunistic pathogen using virulence factors and biofilm regulated by quorum sensing (QS) systems to infect patients and protect itself from environmental stress and antibiotics. Interfering with QS systems is a novel approach to combat P. aeruginosa infections without killing the bacteria, meaning that it is much harder for bacteria to develop drug resistance. A marine fungus Cladosporium sp. Z148 with anti-QS activity was obtained from Jiaozhou Bay, China. Cladodionen, a novel QS inhibitor, was isolated from the extracts of this fungus. Cladodionen had a better inhibitory effect than pyocyanin on the production of elastase and rhamnolipid. It also inhibited biofilm formation and motilities. The mRNA expressions of QS-related genes, including receptor proteins (lasR, rhlR and pqsR), autoinducer synthases (lasI, rhlI and pqsA) and virulence factors (lasB and rhlA) were down-regulated by cladodionen. Molecular docking analysis showed that cladodionen had better binding affinity to LasR and PqsR than natural ligands. Moreover, the binding affinity of cladodionen to LasR was higher than to PqsR. Cladodionen exhibits potential as a QS inhibitor against P. aeruginosa, and its structure–activity relationships should be further studied to illustrate the mode of action, optimize its structure and improve anti-QS activity.

Light use efficiency (LUE), which characterizes the efficiency with which vegetation converts captured/absorbed radiation into organic dry matter through photosynthesis, is a key parameter for estimating vegetation gross primary productivity (GPP). Studies suggest that diffuse radiation induces a higher LUE than direct radiation in short-term and site-scale experiments. The clearness index (CI), described as the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere, is added to the parameterization approach to explain the conditions of diffuse and direct radiation in this study. Machine learning methods—such as the Cubist regression tree approach—are also popular approaches for studying vegetation carbon uptake. This paper aims to compare and analyze the performances of three different approaches for estimating global LUE and GPP. The methods for collecting LUE were based on the following: (1) parameterization approach without CI; (2) parameterization approach with CI; and (3) Cubist regression tree approach. We collected GPP and meteorological data from 180 FLUXNET sites as calibration and validation data and the Global Land Surface Satellite (GLASS) products and ERA-interim data as input data to estimate the global LUE and GPP in 2014. Site-scale validation with FLUXNET measurements indicated that the Cubist regression approach performed better than the parameterization approaches. However, when applying the approaches to global LUE and GPP, the parameterization approach with the CI became the most reliable approach, then closely followed by the parameterization approach without the CI. Spatial analysis showed that the addition of the CI improved the LUE and GPP, especially in high-value zones. The results of the Cubist regression tree approach illustrate more fluctuations than the parameterization approaches. Although the distributions of LUE presented variations over different seasons, vegetation had the highest LUE, at approximately 1.5 gC/MJ, during the whole year in equatorial regions (e.g., South America, middle Africa and Southeast Asia). The three approaches produced roughly consistent global annual GPPs ranging from 109.23 to 120.65 Pg/yr. Our results suggest the parameterization approaches are robust when extrapolating to the global scale, of which the parameterization approach with CI performs slightly better than that without CI. By contrast, the Cubist regression tree produced LUE and GPP with lower accuracy even though it performed the best for model validation at the site scale.

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.