Previous studies found that Central Equatorial Africa (CEA) has experienced a long-term drying trend over the past two decades. To further evaluate this finding, we investigate possible mechanisms ...for this drought by analyzing multiple sources of observations and reanalysis data. We examine the atmospheric circulation changes related to sea surface temperature (SST) variations that control the equatorial African rainfall. Our results indicate that the long-term drought during April, May and June over CEA may reflect the large-scale response of the atmosphere to tropical SST variations. Likely the drought results primarily from SST variations over Indo-Pacific associated with the enhanced and westward extended tropical Walker circulation. These are consistent with the weakened ascent over Central Africa that is associated with the reduced low-level moisture transport. The large-scale atmospheric circulation changes associated with a weaker West African monsoon also have some contribution. These results reinforce the notion that tropical SSTs have large impacts on rainfall over equatorial Africa and highlight the need to further distinguish the contribution of SSTs changes (e.g., La Niña-like pattern and Indian Ocean warming) due to natural variability and anthropogenic forcing to the drought.
Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and ...only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.
Trends in the duration or extent of snow cover are expected to feedback to temperature trends. We analyzed trends in dates of onset and termination of snow cover in relation to temperature over the ...past 27 years (1980-2006) from over 636 meteorological stations in the Northern Hemisphere. Different trends in snow duration are observed over North America and Eurasia. Over North America, the termination date of snow cover remained stable during the 27 years, whereas over Eurasia it has advanced by 2.6 ± 5.6 d decade−1. Earlier snow cover termination is systematically correlated on a year-to-year basis with a positive temperature anomaly during the snowmelt month with a sensitivity of −0.077 °C d−1. These snow feedbacks to air temperature are more important in spring, because high net radiation is coupled with thin snow cover.
This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation ...greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April-October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China.
The relationship between vegetation phenology and climate is a crucial topic in global change research because it indicates dynamic responses of terrestrial ecosystems to climate changes. In this ...study, we investigate the possible impact of recent climate changes on growing season duration in the temperate vegetation of China, using the advanced very high resolution radiometer (AVHRR)/normalized difference vegetation index (NDVI) biweekly time-series data collected from January 1982 to December 1999 and concurrent mean temperature and precipitation data. The results show that over the study period, the growing season duration has lengthened by 1.16 days yrsuperscript -1 in temperate region of China. The green-up of vegetation has advanced in spring by 0.79 days yrsuperscript -1 and the dormancy delayed in autumn by 0.37 days yrsuperscript -1. The dates of onset for phenological events are most significantly related with the mean temperature during the preceding 2-3 months. A warming in the early spring (March to early May) by 1°C could cause an earlier onset of green-up of 7.5 days, whereas the same increase of mean temperature during autumn (mid-August through early October) could lead to a delay of 3.8 days in vegetation dormancy. Variations in precipitation also influenced the duration of growing season, but such influence differed among vegetation types and phenological phases.
Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, ...monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.
Background
Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, characterized by its complex etiology and slow research progress. Diabetes, as an independent risk factor for ...CRC, has been widely certified. Consequently, this study centers on elucidating the intricacies of CRC cells initiation and progression within a high‐glucose environment.
Methods
A battery of assays was employed to assess the proliferation and metastasis of CRC cells cultured under varying glucose concentrations. Optimal glucose levels conducive to cells' proliferation and migration were identified. Western blot analyses were conducted to evaluate alterations in apoptosis, autophagy, and EMT‐related proteins in CRC cells under high‐glucose conditions. The expression of PI3K/AKT/mTOR pathway‐associated proteins was assessed using western blot. The effect of high glucose on xenograft growth was investigated in vivo by MC38 cells, and changes in inflammatory factors (IL‐4, IL‐13, TNF‐α, IL‐5, and IL‐12) were measured via serum ELISA.
Results
Our experiments demonstrated that elevated glucose concentrations promoted both the proliferation and migration of CRC cells; the most favorable glucose dose is 20 mM. Western blot analyses revealed a decrease in apoptotic proteins, such as Bim, Bax, and caspase‐3 with increasing glucose levels. Concurrently, the expression of EMT‐related proteins, including N‐cadherin, vimentin, ZEB1, and MMP9, increased. High‐glucose cultured cells exhibited elevated levels of PI3K/AKT/mTOR pathway proteins. In the xenograft model, tumor cells stimulated by high glucose exhibited accelerated growth, larger tumor volumes, and heightened KI67 expression of immunohistochemistry. ELISA experiments revealed higher expression of IL‐4 and IL‐13 and lower expression of TNF‐α and IL‐5 in the serum of high‐glucose‐stimulated mice.
Conclusion
The most favorable dose and time for tumor cells proliferation and migration is 20 mM, 48 h. High glucose fosters CRC cell proliferation and migration while suppressing autophagy through the activation of the PI3K/AKT/mTOR pathway.
In this work, a kind of side chain liquid crystalline poly(urethane-acrylate)s was synthesized by free polymerization based on self-made liquid crystalline monomers, and a series of liquid ...crystalline polyurethane/shape memory polyurethane composite membranes were prepared by electrospinning. The synthesized liquid crystalline poly(urethane-acrylate)s have excellent thermal stability. Due to the regular arrangement of azobenzene on the side chains, polymers can rapidly undergo a photoinduced transition from trans-isomerism to cis-isomerism in THF solution and restore reversible configurational changes under visible light. The composite membranes prepared by electrospinning can also undergo photoinduced deformation within 6 s, and the deformation slowly returns under visible light. Meanwhile, the composites have shape memory, and after deformation caused by stretching, the membranes can quickly recover their original shape under thermal stimulation. These results indicate that the composites have triple response performances of photoinduced deformation, photo-, and thermal recovery.
With the advancements in deep learning and synthetic aperture radar (SAR) technology, an increasing number of individuals are utilizing deep-learning techniques to detect ships in SAR images. ...However, the efficiency of SAR ship detection is affected by complex background interference and various ship sizes. Addressing these challenges, this article proposes a balanced feature enhanced attention model. First, we introduce a novel attention feature fusion network (WEF-Net) tailored for SAR multiscale ship detection. WEF-Net effectively balances the information across different backbone layers and harmonizes semantic information from various levels of the feature pyramid through aggregation and averaging. Next, we embed the receiving field extension module in WEF-Net to learn the context information and generate the global characteristics of the receiving field balance. In addition, it can extract features from multiple scales to enhance the detection capability of the model for ships of different scales. At the same time, acknowledging the impact of surrounding complex background interference on the detector, we redesigned the ELAN module by combining convolution and attention. This enhancement enables the model to better attend to target position information during feature fusion, suppress the surrounding complex background interference, and highlight the ship's feature information. Finally, owing to the prevalence of small targets in SAR images, we employ an optimized loss function to bolster the model's performance in detecting small targets. This approach accelerates training convergence, reduces instances of missed detection on small targets, and enhances overall detection performance across multiple scales. Experimental results demonstrate that our model achieves detection accuracies of 98%, 93.1%, and 76.9% on the SAR ship detection dataset, high-resolution SAR image dataset, and large-scale SAR ship detection dataset, respectively, effectively discerning ship targets amid complex backgrounds in SAR images.
At present, there are excessive fertilizer use and poor uniformity of fertilizer discharge in corn fertilizer planter. The key difficulty is that accurate perception and control of fertilizer amount ...has not been solved. Aiming at the above problems, a set of accurate perception and control system applied to corn fertilization planter was studied. According to the difference in dielectric properties between fertilizer and air, a sensor for online detection of fertilizer amount based on capacitance method was designed. And the relationship model of mass flow rate for N, P, K fertilizer and capacitance output was established. In order to reduce the influence of pulsation on fertilization flow, a high-precision fertilizer flow control system for fertilization planter based on the fertilizer flow feedback and PID control method was designed. The validated results showed that the maximum measurement error between the relationship model and capacitance output was 3.75%. As the temperature rises from room temperature to 55°C, the differential capacitance change rate of the sensor was less than 3%. The steady-state error of fertilizer discharge was less than 2%. The field experiment of the accurate perception and control system for corn fertilization amount show that the electric drive fertilization system has good consistency, the maximum and average variation coefficient of fertilization were 3.74%, 1.6%, respectively, and the variable control accuracy was greater than 97%. The control accuracy of the grain spacing control by electric drive seed metering was 98%. Therefore, the precision fertilization control system in this study can realize high-precision and on-demand fertilization. It is of great significance to realize the intelligence and precision for corn fertilization planter.
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