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Abstract Background Blood and urine biomarkers have been associated with urologic tumors, but their causal relationship with urologic tumors is unclear. Methods We performed a bidirectional Mendelian randomization (MR) analysis of the association between 35 blood and urine biomarkers and urological tumors in our discovery cohort. A Bayesian weighting approach was used to validate the positive results identified in the discovery cohort, and steiger filtering analysis was used to distinguish causality from reverse causality. Bayesian colocalization analysis was used to analyze which single nucleotide polymorphisms (SNPs) specifically co-located between the positive blood and urine biomarkers and the disease phenotypes were driven, and MR-positive results from the discovery cohort and the validation cohort were combined using the MR-meta method. Results Several blood and urine biomarkers were found to be significantly and causally associated with urologic cancers. Notably, calcium (OR: 1.34, 95%CI 1.10–1.63, P = 0.0040) and sex hormone-binding globulin (SHBG) (OR: 0.81, 95%CI 0.70–0.95, P = 0.0092) were associated with bladder cancer; gamma glutamyl transferase (GGT) (OR: 0.91, 95%CI 0.83–0.99, P = 0.0209), lipoprotein A (Lp(a)) (OR: 1.12, 95%CI 1.01–1.24, P = 0.0399), and insulinlike growth factor 1 (IGF 1) (OR: 1.10, 95%CI 1.01–1.20, P = 0.0220) were linked to prostate cancer (PCa); non albumin protein (OR: 0.78, 95%CI 0.65–0.93, P = 0.0065) and total protein (OR: 0.80, 95%CI 0.64–0.99, P = 0.0380) were linked to renal cancer; and apolipoprotein A (Apo-A) (OR: 0.56, 95%CI 0.32–0.98, P = 0.0426) and urate (OR:1.89, 95%CI 1.03–3.47, P = 0.0399) were associated with renal pelvis cancer. These associations were validated in an independent cohort, with GGT, IGF 1, and Lp(a) being consistently linked to PCa. Conclusion This study identified significant biomarkers associated with urological cancers in blood and urine. These include GGT, IGF 1, and Lp(a), which are strong biomarkers for PCa. In addition, the findings of this study provide evidence for a handful of risk and protective factors for the development of urologic cancers.

Human activities have significantly altered flow regimes, but the effects of shifting flow regimes on riparian species interactions and the ecosystem network remain unclear. This study applied a modified community-wide population model, integrated with network theory, to simulate the responses of the riparian ecosystem to potential future flow regimes in a desert oasis in northwestern China. Results indicate that the riparian ecosystem network in the desert oasis is inherently unstable under the Heihe River’s current hydrological conditions, characterized by considerable intra-annual and inter-annual runoff variability, as even minor changes in flow regimes could alter the network. Specifically, increased frequency of drought year simplifies the network, while heightened flood year frequencies (not exceeding 85%) create a more intricate network. Additionally, longer flood duration (more extended overlap with seed dispersal periods) significantly improves network connectivity and redundancy. Notably, the keystone status of species within the flow regime influences the network structure. With reduced flooding and more homogeneous flow, the keystone roles shift from trees and shrubs to herbs. This underscores the essential role of flooding events in sustaining riparian ecological networks. Thus, flood frequency, timing, and duration, alongside water quantity, should be considered in ecological restoration efforts for desert oasis environments. The study’s methodology and findings provide a scientific foundation for developing comprehensive management guidelines to regulate riparian areas under global change.

An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

Coastlines have experienced profound alterations due to human disturbances and ocean dynamics against the backdrop of climate change and intensified anthropogenic activities. Significant modifications have occurred within the muddy coastal ecosystem, rendering it extremely susceptible to heightened levels of vulnerability. However, existing studies on coastal eco-environmental vulnerability have often overlooked crucial factors such as coastline erosion and siltation, particularly concerning coastline change. In this study, we assess coastline stability utilizing Landsat remote sensing imagery to investigate the vulnerability of coastal ecology alongside 24 economic and natural parameters, serving as indices for the Coastal Vulnerability Index (CVI) of the coastal eco-environment. Subsequently, we analyze the impact factors to vulnerability and predict trends in coastline change over the next 20 years to propose recommendations for sustainable development to safeguard the coastal eco-environment. Our findings reveal a trend towards coastline stabilization in Jiangsu, with significantly higher stability observed during 2010–2020 compared to 1985–2010. The overall CVI along the Jiangsu coast shows an downward trajectory over the past 15 years, primarily influenced by tidal action and coastal topography and geomorphology. Predictive modeling suggests Jiangsu coastline is bounded by the Sheyang estuary, with the coastline to the north dominated by stabilization and to the south dominated by siltation. Climate change, coupled with intensified human activities and future coastline alterations, may exacerbate vulnerability. Effective protection strategies for coastal ecosystems necessitate a comprehensive understanding of local natural conditions, resource availability, and socioeconomic dynamics. Governmental interventions should focus on reinforcing regulatory frameworks for coastal organism management, implementing coastal protection infrastructure, and enhancing supervision and management protocols for beachfront development, thereby fostering the sustainable management of muddy coastlines.

Abstract The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

Coastal zones are facing intensive ecological pressures and challenges, which could vary over a wide range of spatiotemporal scales. Our limited capability to understand and especially predict this variability can lead to the misinterpretation of coastal ecological resilience. Therefore, the assessment and prediction of ecological resilience are particularly important. In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China’s coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. The results show that ER in the CCZ displayed a general spatial distribution pattern of “higher in the southern half and lower in the northern half” from 1995 to 2020. Over the 25-year period, ER exhibited a declining trend. Specifically, the eastern provinces experiencing the most significant decline. The ER levels across scenarios ranked from high to low as follows: SSP1-2.6 > SSP4-3.4 > SSP2-4.5 > SSP3-7.0 > SSP5-8.5. The assessment and prediction methods designed can be applied to ER studies in other coastal zones, making it a valuable approach for broader applications.

The aim of this study was to investigate the effects of temporal instability and possible heterogeneity on pedestrian accident severity, 48786 accident data from 2018 to 2021 in the UK STATS database were used as the study object, and accident severity was used as the dependent variable, and 49 accident characteristics were selected as independent variables from 6 characteristics of accident pedestrian, driver, vehicle, road, environment and time to construct the pedestrian accident mean heterogeneity random-parameter logit model and examined its temporal stability. The results of model estimation and likelihood ratio tests indicate that the variables affecting pedestrian injury severity are highly variable and not stable over the years. And further demonstrates the potential of models that address unobserved heterogeneity for significant relationships in pedestrian accident severity analyses.

Determining regional and global carbon cycles hinges on investigating the dynamic characteristics and influencing factors of soil respiration in various types of natural grasslands located in arid regions, and these characteristics are important indicators for assessing the structural and functional health of grassland ecosystems. Such investigations also provide theoretical support for carbon sink monitoring, energy conservation, emission reduction and low-carbon development in the western arid zone and are important for obtaining an in-depth understanding of the carbon cycle, as well as for ecosystem management, restoration and the reconstruction of arid areas. In this study, during the growing season (from May to October) of 2022, the LI-8100A automated soil CO2 flux system was used to measure the soil respiration rate (Rs), temperature from 1.5 m above the surface to depths of 5–25 cm (T, T5, T10, T15, T20 and T25) and the soil moisture content (SM) at a depth of 20 cm in four types of grasslands: lowland meadow, alpine meadow, temperate desert steppe and temperate steppe desert. Five replicates were established for each plot, and the responses of Rs to T and SM were fitted to construct the optimal regression model. The results revealed that (1) the daily average soil respiration was highest in the lowland meadow (0.07 to 5.76 μmol·m−2·s−1), followed by the alpine meadow (−0.57 to 0.95 μmol·m−2·s−1), the temperate desert steppe (−0.45 to 3.0 μmol·m−2·s−1) and the temperate steppe desert (−1.29 to 1.61 μmol·m−2·s−1); (2) the soil respiration rates of the four grassland types were significantly correlated with the temperature in the 5–15 cm soil layer, and the best model was an exponential function; the peak values generally appeared between 13:00 and 17:00 (h), with the minimum values at 2:00 or 8:00 (h); the maximum value was observed in July–August, and the minimum value was observed in October; and the soil respiration in the lowland meadow was higher than that in the other three types of grassland during the same period. The average variation intensities of the soil respiration from May to October were as follows: temperate steppe desert (91.78%) > temperate desert steppe (76%) > alpine meadow (58.77%) > lowland meadow (43.93%). (3) The partial correlation analysis revealed that when soil temperature was used as a control, the correlation between SM and soil respiration in the four types of grasslands changed, and the coefficient of determination (R2) increased to varying degrees, explaining up to 80% of the variation in the soil respiration in the lowland meadows. The correlation between soil respiration and the SM normalized to 10 °C explained up to 93.8% of the variation in soil respiration; the two-factor fitting equations revealed that the model with soil temperature and SM was superior to the single-factor model with either soil temperature or SM.

This study investigates the characteristics and intensity of El Niño–Southern Oscillation (ENSO) events from January 1875 to December 2023, employing an advanced method for intensity determination based on various ENSO indices defined as a continuous five-month period with temperatures exceeding 0.5 °C for warm events or falling below −0.5 °C for cold events. A total of 40 warm and 41 cold events were identified, with further classification revealing seven extreme warm events and five extreme cold events. The analysis shows a positive skewness in frequency distribution, indicating a predominance of strong warm events. The primary mode of variability is found to be interannual oscillation in the 3–8 year range, with significant decadal oscillations in the 10–16 year range. This study highlights the importance of methodological rigor in evaluating ENSO dynamics, contributing to a more comprehensive understanding of climate variability and offering a reliable framework for future research.

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km2/10a. Among the total area, the natural wetland experienced a decline of 446.9 km2/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km2/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km2/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km2/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change.

The recurring occurrence of green tides as an ecological disaster has been reported annually in the Yellow Sea. While remote sensing technology effectively tracks the scale, extent, and duration of green tide outbreaks, there is limited research on the underlying driving mechanisms of green tide drift transport and the determination of the leeway coefficient. This study investigates the green tide transport mechanism and evaluates the feasibility of estimating the leeway coefficient by analyzing green tide drift velocities obtained from Geostationary Ocean Color Imager-II (GOCI-II) images using the maximum cross-correlation (MCC) technique and leeway method across various time intervals alongside ocean current and wind speed data. The results reveal the following: (1) Significant spatial variations in green tide movement, with a distinct boundary at 34°40′N. (2) Short-term green tide transport is primarily influenced by tidal forces, while wind and ocean currents, especially the combined Ekman and geostrophic current component, predominantly govern net transport. (3) Compared to 1, 3, and 7 h intervals, estimating the leeway coefficient with a 25 h interval is feasible for moderate-resolution geostationary images, yielding values consistent with previous studies. This study offers new insights into exploring the transport mechanisms of green tides through remote sensing-driven velocity.

The Jiangsu Coast (JC), China, is an area susceptible to the impact of tropical cyclones (TCs). However, due to the lack of available on-site observation data, nearshore sedimentary dynamic processes under the impact of TCs have not been fully explored. This study developed a 3D wave–current–sediment numerical model for the JC based on the Finite Volume Community Ocean Model (FVCOM) to investigate sediment dynamic responses to TCs under various scenarios, including different tracks, intensities of TCs and tidal conditions. The validation results demonstrated the model’s satisfactory performance. According to the simulation results, typhoons can significantly impact the hydrodynamics and sediment dynamics. During Typhoon Lekima in 2019, strong southeasterly winds substantially increased the current velocity, bottom stress, wave height, and suspended sediment concentration (SSC). Three typical landfall-type typhoons, with prevailing southeasterly winds, brought significant sediment flux from southeast to northwest along the coast, while the typhoon that moved northward in the Yellow Sea induced a relatively small sediment flux from north to south. Typhoons could also induce stripe-like erosion and deposition, which is closely related to seafloor topography, resulting in seabed thickness variations of up to ±0.3 m. Additionally, strengthening typhoon wind fields can lead to increased sediment flux and seabed morphological changes. Typhoon Winnie, particularly at spring tide, had a greater impact on sediment dynamics compared to other landfall typhoons. Numerical simulations showed that the typhoon-induced net sediment flux within the spring tidal cycle could increase by 80% to 100% compared to the neap tidal cycle, indicating the significant influence of tidal conditions on sediment transport during TC events.

This article aimed to investigate the feasibility of using imaging spectroscopy (IS) to predict soil organic carbon density (SOCD) either directly or indirectly through soil organic carbon (SOC). Three methods, partial least square regression, support vector machine (SVM), and random forest, were utilized to calibrate the models and map the SOCD. The results showed that direct prediction was better than indirect prediction and the best model SVM had high R2 of 0.94 and 0.93 for calibration and validation, respectively. The measured SOCD was mainly concentrated in the surface soil layer from 0 to 40 cm, and the deeper layer tended to be gentle. The continuous depth variation trend of SOCD in the topsoil (0–40 cm) was relatively close to the measured values, while in the deeper layers (below 40 cm), it was much higher than the measured values. This article also found that the best fitting function of measured SOC stocks over time varied from linear to power and then to logarithmic with increasing depth, indicating less efficient accumulation of SOC in deep soil compared to topsoil, resulting in an overall decrease in SOC accumulation rate across soil depth. The best temporal functions for the predicted values differed from the measured values at each depth, but the changing trends of the three functions were basically consistent. It suggests that IS technology has the potential to quantitatively reveal the process of coastal soil evolution and offers a new insight for the rapid monitoring of soil property changes.

Considering the growing pressure of the edible oil imports bill on Pakistan’s national accounts, the significance of sunflower cultivation is gaining more attention to meet the domestic edible oil demand. Given the declining area for sunflower production, this study explored the factors of economic and environmental inefficiencies affecting sunflower growers in Pakistan. The study adopted the epsilon-based measure (EBM) model at the first stage and the Tobit truncated regression model at the second stage to precisely estimate the economic and environmental inefficiencies in the data collected from 240 sunflower growers. Results found that out of 240, 69.9% of the sunflower growers are economically inefficient, while the average environmental inefficiency of sunflower growers is 56.3%. The findings further revealed that farmers’ age, cultivated land, and market distance are the driving factors of farmers’ economic and environmental inefficiencies. However, formal education, farming experience, and access to agricultural extension services decreased the farmer’s inefficiencies. Based on the present study’s findings, diverse policy options are presented to address the problem of contraction of the area under sunflower production.

Abstract Spinal cord injury (SCI) is a devastating condition for patients, affecting nearly 2.5 million people globally. Multiple side effects of SCI have resulted in a terrible life experience for SCI patients, of which neuropathic pain has attracted the most scientific interest. Even though many efforts have been made to attenuate or eliminate neuropathic pain induced by SCI, the outcomes for patients are still poor. Therefore, identifying novel diagnosis or therapeutic targets of SCI-induced neuropathic pain is urgently needed. Recently, multiple functions of long non-coding RNA (lncRNA) have been elucidated, including those in SCI-induced neuropathic pain. In this study, lncRNA small nucleolar RNA host gene 12 (SNHG12) was found to be upregulated in the dorsal root ganglion (DRGs) of rats with spare nerve injury (SNI). By constructing SCI rat models, we found that lncRNA SNHG12 expression was increased in the DRGs, and mainly distributed in the cytoplasm of PC12 cells. Paw withdrawal threshold (PWT), paw withdrawal latency (PWL), and enzyme linked immunosorbent assay (ELISA) results indicated that lncRNA SNHG12 knockdown attenuated SNI-induced neuropathic pain, and decreased the expression levels of interleukin (IL)−1β, IL-6, and tumour necrosis factor α (TNF-α) in the DRGs. Bioinformatics analysis, RNA pull-down, chromatin immunoprecipitation (ChIP), and luciferase reporter gene assays showed that lncRNA SNHG12 regulates the RAD23 homologue B, nucleotide excision repair protein (RAD23B) expression, through targeting micro RNA (miR)−494-3p. Furthermore, the study indicated that Kruppel-Like Factor 2 (KLF2) could regulate lncRNA SNHG12 expression in PC12 cells. This study identified a novel KLF2/lncRNA SNHG12/miR-494-3p/RAD23B axis in SNI-induced neuropathic pain, which might provide a new insight for developing novel diagnosis, or therapeutic targets of SCI-induced neuropathic pain in the future.

It is a fundamental task to monitor the topography and understand the changes of intertidal zone for rational utilization and sustainable development. A new method is proposed for identifying the terrain of the intertidal zone, using ICESat-2 data to replace a large amount of on-site observation data, thereby reducing costs and improving efficiency. Based on pre-experiments and correlation analysis, time phase index, water index, water transparency index and suspended sediment concentration index are added as features for the random forest (RF). Compared with using only the original band as the model input, the RMSE is reduced by 0.08 m. The results show that the inverted terrain has an RMSE of 0.45 m compared with handheld RTK data, and the RMSE at the mudflat from UAV data is 0.20 m. Based on the analysis of terrain changes over the four-year period, the trend towards sedimentation closer to land becomes more pronounced.

Global climate change has led to an increasing frequency and intensity of extreme climates (e.g., extreme temperature and precipitation), and its impact on land surface phenology (LSP) has attracted increasing attention. This study evaluated the effects of extreme climates on land surface phenology in Central Asia (CA) based on 13 extreme climate indices (ECIs). The results showed that the start of the season (SOS) of the LSP was gradually delayed, the end of the season (EOS) advanced, and the length of the growing season (LOS) shortened. Most ECIs showed varying degrees of an increasing trend, except the cold indices of the cool days (TX10p) and the cool nights (TN10p) and the precipitation indices of the max one-day precipitation amount (RX1day) and the max five-day precipitation amount (RX5day). The LSP was influenced by extreme climates, and 13 ECIs were mainly negatively correlated with the SOS and positively correlated with the EOS and LOS. Increased extreme temperature and decreased extreme precipitation contributed to the change in the LSP. The most important impact on the SOS was from the max Tmin (TNx), the mean Tmax (TXmean), while the mean Tmin (TNmean) and warm days (TX90p) also had noticeable effects. The most important impact on the EOS and LOS was from TX90p, followed by TX10p and TXmean. Based on climate model projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), future extreme climates are projected to intensify under Shared Socioeconomic Pathways (SSPs) and will likely continue to affect the LSP. This study helps provide a basis for formulating scientific and reasonable vegetation restoration strategies and disaster warnings in Central Asia.

Study region: Hotan River Basin, an arid inland river basin, Northwest China. Study focus: A method for assessing the spatial distribution of groundwater resilience is constructed by combining the Dempster-Shafer theory model and spatial analysis techniques. The groundwater performance indicator CRS and resilience indicator pi are calculated to present groundwater resilience. Using the Dempster-Shafer theory model to mine the data, nine groundwater conditioning factors are selected as evidential layers of the model, and the mathematical relationships between the resilience indicators and groundwater conditioning factors are established through evidence integration; Ultimately, a groundwater resilience spatial distribution map is generated, and the performance of the map is explored based on the CRS and pi values. New hydrological insights for the region: The constructed assessment method based on this study reliably and effectively predicts the regional groundwater resilience. Validation of the groundwater resilience prediction map with pi indicates that this indicator outperforms the CRS. The areas with high groundwater resilience are mainly concentrated in the upstream oasis irrigation area and the downstream river channel. The groundwater resilience (pi) zonation map is divided into five categories: very low (Bel of 0–0.072), low (0.072–0.171), moderate (0.171–0.458), moderate high (0.458–0.786), and high (>0.786). The results can provide a scientific basis for groundwater safety and management in the Hotan River Basin.

Spartina alterniflora (S. alterniflora) was introduced into China in the 1980s to control coastal erosion. However, it has significant negative impacts on coastal wetlands by encroaching on the habitat of native plant communities, which has seriously threatened coastal wetland function and biodiversity maintenance. The core area of the Migratory Bird Sanctuaries along the Coast of Yellow Sea-Bohai Gulf of China was taken as the study area and GF-2 remote sensing images from 2017, 2019 and 2020 were selected in this study. By using U-Net, this study extracted habitat information, identified the variation in habitat pattern, investigated interspecific competition and the characteristics of habitat type shifts. The results indicated that the U-Net model showed excellent classification performance, with the highest F1-score and MIoU. The habitat of S. alterniflora expanded and increased from 3,920 to 4,350 hm2 between 2017 and 2020. The habitat of Suaeda salsa (S. salsa) continuously fragmented and had been reduced to 1,414 hm2 by 2020. The degree of habitat fragmentation in the core area was strengthened and heterogeneity was enhanced. Moreover, while the competition between S. alterniflora and Phragmites australis (P. australis) was intensifying, both encroached on the habitat of S. salsa, which aggravated the reduction and fragmentation of the habitat of S. salsa, causing the loss of food and habitat of red-crowned cranes and further threatening biodiversity maintenance and ecosystem stability. These results can offer new insights on the habitat competitive mechanism among native plant communities and alien invasive species in the coastal wetland under global warming and anthropogenic influences.

Coastal wetland ecosystems around the world are facing serious challenges due to rapid economic development, climate change, and sea level rise. These factors have a great influence on the tidal creek network and vegetation ecosystem. Accordingly, based on long-term time-series remote sensing images, the evolution characteristics of tidal creeks and vegetation in silting muddy flats on the Yellow Sea were analyzed, and different quantitative methods were used to describe the relationship between tidal creeks and vegetation. The results showed that the total number of tidal creeks exhibited a downward trend, while the number of small creeks increased over time. The total length of the tidal creeks decreased at a rate of 16.1 km per year during the study period. The length of three-grade tidal creeks, the patch area ratio of Suaeda salsa, and a low vegetation coverage had a great influence on the average return flow length (LOP). LOP was negatively correlated with the patch area ratio of S. salsa, but positively correlated with the reclamation area, both significantly. With the increase in the patch area ratio of S. salsa, the total length of the tidal creeks increased by 12.95 km, and LOP decreased by 35.35 m.

Inputting text is a prevalent requirement among various virtual reality (VR) applications, including VR-based remote collaboration. In order to eliminate the need for complex rules and handheld devices for typing within virtual environments, researchers have proposed two mid-air input methods—the trace and tap methods. However, the specific impact of these input methods on performance in VR remains unknown. In this study, typing tasks were used to compare the performance, subjective report, and cognitive load of two mid-air input methods in VR. While the trace input method was more efficient and novel, it also entailed greater frustration and cognitive workload. Fortunately, the levels of frustration and cognitive load associated with the trace input method could be reduced to the same level as those of the tap input method via familiarity with VR. These findings could aid the design of virtual input methods, particularly for VR applications with varying text input demands.

The fully mechanized caving roadway’s floor heave has a significant impact on the stability of the narrow coal pillars, the filling body next to the roadway, as well as the entire roadway. Significant floor heave necessitates extensive maintenance and rebuilding work, which has a negative impact on the mine’s regular operations. The costs of sustaining and maintaining the roads are significantly increased by production. In this study, a mechanical model of the floor heave of the road along the goaf is established using the Winker elastic foundation theory model. The mechanical model of the floor heave of the roadway is confirmed when combined with engineering cases. The findings of the study indicate that there is almost no deformation of the side floor of the roadside support and the solid coal. The floor deformation of the roadway area exhibits non-positive symmetry and a “parabolic” characteristic. Roadway width, burial depth, and roadway floor heave all have linearly positive correlations, but elastic modulus of the floor, burial depth, and highway floor heave all have negatively exponential correlations. The maximum deformation of the floor heave, which has a maximum value of 628 mm, is close to the side of the roadway support body; the theoretical model’s maximum value for the floor heave after 100 days of actual deformation monitoring is 645 mm. Between the maximum value and the maximum value as measured, there is a 2.6% error. The paper has important guiding significance for explaining the mechanism of floor heave in goaf roadway and controlling the deformation of the roadway floor.

The concept of agricultural and environmental sustainability refers to minimizing the degradation of natural resources while increasing crop productions; assessment of inflow and outflow energy resources is helpful in highlighting the resilience of the system and maintaining its productivity. In this regard, the current study evaluated the amount of energy input–output of cotton productions and their environmental interventions. Data are randomly collected from 400 cotton farmers through face-to-face interview. Results suggested that the major energy is consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water (11,532.60, 11,121.54, and 4,531.97 MJ ha−1, respectively). Total greenhouse gas (GHG) emission is 1,106.12 kg CO2eq ha−1 with the main share coming from diesel fuel, machinery, and irrigation water. Stimulating data of energies, e.g., energy use efficiency (1.53), specific energy (7.69 MJ kg−1), energy productivity (0.13 kg MJ−1), and net energy gained (16,409.77 MJ ha−1). Further analysis using data envelopment analysis (DEA) showed that low technical efficiency, i.e., 69.02%, is the most probable cause of poor energy use efficiency. The impermanent trend in growth of energy efficiency has been witnessed with plausible potential of energy savings from 4,048.012 to 16,194.77 MJ ha−1 and a reduction of 148.96–595.96 kg CO2eq ha−1 in GHG emission. Cobb–Douglas production function is further applied to discover the associations of energy input to output, which inferred that chemical fertilizer, diesel fuel, machinery, and biocides have significant effect on cotton yield. The marginal physical productivity (MPP) values obliged that the additional use in energy (1 MJ) from fuel (diesel), biocides, and machinery can enhance cotton yield at the rate of 0.35, 1.52, and 0.45 kg ha−1, respectively. Energy saving best links with energy sharing data, i.e., 55.66% (direct), 44.34% (indirect), 21.05% (renewable), and 78.95% (nonrenewable), further unveiled the high usage of nonrenewable energy resources (fossil fuels) that ultimately contributes to high emissions of GHGs. We hope that these findings could help in the management of energy budget that we believe will reduce the high emissions of GHGs.

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 λ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 λ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems.