Search

Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple and easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes.

High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is difficult to obtain remote sensing images with both high spatial and temporal resolution. The spatiotemporal fusion model is a crucial method to solve this problem. The spatial and temporal adaptive reflectivity fusion model (STARFM) and its improved models are the most widely used spatiotemporal adaptive fusion models. However, the existing spatiotemporal adaptive reflectivity fusion model and its improved models have great uncertainty in selecting neighboring similar pixels, especially in spatially heterogeneous areas. Therefore, it is difficult to effectively search and determine neighboring spectrally similar pixels in STARFM-like models, resulting in a decrease of imagery fusion accuracy. In this research, we modify the procedure of neighboring similar pixel selection of ESTARFM method and propose an improved ESTARFM method (I-ESTARFM). Based on the land cover endmember types and its fraction values obtained by spectral mixing analysis, the neighboring similar pixels can be effectively selected. The experimental results indicate that the I-ESTARFM method selects neighboring spectrally similar pixels more accurately than STARFM and ESTARFM models. Compared with the STARFM and ESTARFM, the correlation coefficients of the image fused by the I-ESTARFM with that of the actual image are increased and the mean square error is decreased, especially in spatially heterogeneous areas. The uncertainty of spectral similar neighborhood pixel selection is reduced and the precision of spatial-temporal fusion is improved.

Abstract The serious problem of carbon monoxide (CO) poisoning on the surface of Pt-based catalysts has long constrained the commercialization of proton exchange membrane fuel cells (PEMFCs). Regeneration of Pt sites by maintaining CO scavenging ability through precise construction of the surface and interface structure of the catalyst is the key to obtaining high-performance CO-resistant catalysts. Here, we used molybdenum carbide (MoCx) as the support for Pt and introduced Ru single atoms (SA-Ru) at the Pt-MoCx interface to jointly decrease the CO adsorption strength on Pt. More importantly, the MoCx and SA-Ru are immune to CO poisoning, which continuously assists in the oxidation of adsorbed CO by generating oxygen species from water dissociation. These two effects combine to confer this anode catalyst (SA-Ru@Pt/MoCx) remarkable CO tolerance and the ability to operate stably in fuel cell with high CO concentration (power output 85.5 mW cm−2@20,000 ppm CO + H2 – O2), making it possible to directly use the cheap reformed hydrogen as the fuel for PEMFCs.

Three new rare-earth high-entropy oxides with compositions of (Y1/7La1/7Nd1/7Sm1/7Gd1/7Yb1/7Lu1/7)2O3 (7HEO), (Y1/8La1/8Nd1/8Sm1/8Gd1/8Yb1/8Lu1/8Ce1/8)2O3 (8HEO) and (Y1/10La1/10Nd1/10Sm1/10Gd1/10Yb1/10Lu1/10Ce1/10Zr1/10Hf1/10)2O3 (10HEO) were prepared using a solid-state reaction method combined with conventional sintering at 1600 °C for 10 h 7HEO has a monoclinic structure. The addition of Ce into 7HEO not only leads to the phase transformation from monoclinic to bixbyite structure, but also contributes to the grain refinement. When the three elements of Ce, Zr and Hf are added into 7HEO, a second fluorite-structured phase forms and further promotes the grain refinement. 10HEO and 8HEO have a higher thermal conductivity compared with 7HEO in 25–600 °C. When the temperature reaches 900 °C, the thermal conductivity of 8HEO is lower than that of 7HEO. The addition of Ce or Ce/Zr/Hf into 7HEO causes the weakening of the bond strength and the increase of the bond length of cation-O2- in the samples, which in turn increases the thermal expansion coefficient. Moreover, 7HEO possesses a better oxygen barrier property compared with 8HEO and 10HEO. The hardness and Young's modulus of 7HEO are lower than those of 8HEO and 10HEO.

Abstract Ruthenium dioxide is presently the most active catalyst for the oxygen evolution reaction (OER) in acidic media but suffers from severe Ru dissolution resulting from the high covalency of Ru-O bonds triggering lattice oxygen oxidation. Here, we report an interstitial silicon-doping strategy to stabilize the highly active Ru sites of RuO2 while suppressing lattice oxygen oxidation. The representative Si-RuO2−0.1 catalyst exhibits high activity and stability in acid with a negligible degradation rate of ~52 μV h−1 in an 800 h test and an overpotential of 226 mV at 10 mA cm−2. Differential electrochemical mass spectrometry (DEMS) results demonstrate that the lattice oxygen oxidation pathway of the Si-RuO2−0.1 was suppressed by ∼95% compared to that of commercial RuO2, which is highly responsible for the extraordinary stability. This work supplied a unique mentality to guide future developments on Ru-based oxide catalysts’ stability in an acidic environment.

Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory to accommodate both the model and a large data batch size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity because the batch size can only fit into the remaining memory after the model is uploaded. Moreover, the data item size is also an important factor because if each data item size is larger then the batch size that can fit into the remaining memory becomes smaller. This paper proposes a method called Micro-Batch Processing (MBP) to address this problem. This method helps deep learning models to train by providing a batch processing method that splits a batch into a size that can fit in the remaining memory and processes them sequentially. After processing the small batches individually, a loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using larger batch sizes that exceed the memory capacity of a system without increasing the memory size or using multiple devices (GPUs).

Extreme ambient temperatures cause electric vehicles’ batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control.

Advances in conducting polymers (CPs) have promoted the development of diabetic monitoring and treatment, which is of great significance in human healthcare and modern medicine. CPs are special polymers with physical and electrochemical features resembling metals, inorganic semiconductors and non-conducting polymers. To improve and extend their properties, the fabrication of CPs and CP composites has attracted intensive attention in recent decades. Some CPs are biocompatible and suitable for biomedical use. Thus, the intriguing properties of CPs make wearable, noninvasive, continuous diabetes managing devices and other potential applications in diabetes possible in the near future. To highlight the recent advances of CPs and their derived materials (especially in conducting polymer hydrogels), here we discuss their fabrication and characterization, review the current state-of-the-art research in diabetes management based on these materials and describe current challenges as well as future potential research directions.