ALBERT

Beschreibung: As an important form of clean energy, photovoltaic (PV) power generation is entering a rapid development phase. Qinghai, China is located on the Qinghai-Tibet Plateau. It has sufficient sunlight and rich heat and light resources, includes a large area of the Gobi Desert, and has become China’s largest base for PV power generation. However, large-scale PV development in deserts changes the local surface energy distribution and impacts local microclimates. This study considered the Gonghe PV Power Plant in Qinghai as an example. Three monitoring stations were set up in the PV power plant, transition, and reference areas, and the influence of large-scale PV developments on the local air temperature and humidity was studied based on long-term, multi-point field observation data. The results showed that the overall daytime air temperature in the PV power plant had changed slightly (increased and decreased), while the night-time temperature dropped significantly. Specifically, in spring and summer, the daytime temperature increased slightly, with a maximum increase of 0.34 °C; in autumn and winter, the daytime temperature decreased slightly, with a maximum decrease of 0.26 °C; in all seasons, the night-time temperature decreased, with a maximum decrease of 1.82 °C during the winter night. The relative humidity in the PV power plant generally increased; except for a slight decrease in summer, the daytime and night-time relative humidity in spring, autumn, and winter always increased. The humidification in winter was the most significant, with increases of 5.00% and 4.76% for the transition and reference areas, respectively. The diurnal air temperature and relative humidity ranges in the PV power plant were greater than those outside the PV power plant. The results obtained in this field observation study could serve as a basis for quantitative evaluation of the microclimate effects of large-scale PV development in deserts and provide technical support for guiding the future planning and development of the PV industry.

Beschreibung: Zeolites are generally defined as three-dimensional (3D) crystalline microporous aluminosilicates in which silicon (Si4+) and aluminum (Al3+) are coordinated tetrahedrally with oxygen to form large negative lattices and consequent Brønsted acidity. Two-dimensional (2D) zeolite nanosheets with single-unit-cell or near single-unit-cell thickness (~2–3 nm) represent an emerging type of zeolite material. The extremely thin slices of crystals in 2D zeolites produce high external surface areas (up to 50% of total surface area compared to ~2% in micron-sized 3D zeolite) and expose most of their active sites on external surfaces, enabling beneficial effects for the adsorption and reaction performance for processing bulky molecules. This review summarizes the structural properties of 2D layered precursors and 2D zeolite derivatives, as well as the acidity properties of 2D zeolite derivative structures, especially in connection to their 3D conventional zeolite analogues’ structural and compositional properties. The timeline of the synthesis and recognition of 2D zeolites, as well as the structure and composition properties of each 2D zeolite, are discussed initially. The qualitative and quantitative measurements on the acid site type, strength, and accessibility of 2D zeolites are then presented. Future research and development directions to advance understanding of 2D zeolite materials are also discussed.

Beschreibung: As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of conventional hydrological coding systems, one is the top-down approach, and the other is the bottom-up approach. However, the former does not accurately reflect the hierarchies of a dendritic river network, which is produced by catchment relationships, and it is not appropriate for the stream selection of river networks with uniform distributions of tributaries. The latter cannot directly indicate the subtree depth of a stream, and it is not favorable to stream selection of river systems that have topologically deep structures. Therefore, a selection method for dendritic river networks based on hybrid coding is proposed in this paper. First, the dendritic river network is coded through classical top-down Horton coding. Second, directed topology trees are constructed to organize the river network data, and stroke connections are calculated to code the river network in the bottom-up approach. Third, the river network is marked through hybrid usage of the top-down approach and bottom-up approach, and based on the spatial characteristics of the river network, the river network is classified into three kinds of subtrees: deep branch, shallow branch and modest branch. Then, appropriate coding is assigned automatically to different subtrees to achieve river selection. Finally, actual topographic map data of a river system in a region of Hubei Province are used to comparatively validate the hybrid coding system against two existing isolated coding systems. The experimental results demonstrate that the hybrid coding method is very effective for river network selection, not only in highlighting hierarchies formed by catchment relationships but also in the uniform distribution of tributaries.

Beschreibung: The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for supervising dimensionality reduction, but the band subset obtained by this method has a large number of continuous bands, resulting in a reduction in the classification accuracy. In this paper, an improved method—called Partitioned Relief-F—is presented to mitigate the influence of continuous bands on classification accuracy while retaining important information. Firstly, the importance scores of each band are obtained using the original Relief-F method. Secondly, the whole band interval is divided in an orderly manner, using a partitioning strategy according to the correlation between the bands. Finally, the band with the highest importance score is selected in each sub-interval. To verify the effectiveness of the proposed Partitioned Relief-F method, a classification experiment is performed on three publicly available data sets. The dimensionality reduction methods Principal Component Analysis (PCA) and original Relief-F are selected for comparison. Furthermore, K-Means and Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) are selected for comparison in terms of partitioning strategy. This paper mainly measures the effectiveness of each method indirectly, using the overall accuracy of the final classification. The experimental results indicate that the addition of the proposed partitioning strategy increases the overall accuracy of the three data sets by 1.55%, 3.14%, and 0.83%, respectively. In general, the proposed Partitioned Relief-F method can achieve significantly superior dimensionality reduction effects.

Beschreibung: Based on the perspective of conservation of resources (COR) theory, this study adopts a multilevel approach to examine the influences of employees’ personal resources (i.e., work engagement and intrinsic motivation) and external resources (i.e., transformational leadership) on knowledge sharing. This study conducts a survey to explore the interrelationships among transformational leadership, work engagement, intrinsic motivation, and knowledge sharing. The sample includes 33 healthcare work groups consisting of 214 group members. The results show that an individual’s personal and external resources are positive and benefit the promotion of knowledge sharing. As for personal resources, work engagement has a positive impact on knowledge sharing by increasing intrinsic motivation. Regarding external resources, transformational leadership acts as a facilitator for knowledge sharing. Specifically, the conditional indirect effects of work engagement on knowledge sharing through intrinsic motivation are more positive under high levels of transformational leadership, rather than low levels of transformational leadership. Based on the COR theory, this is the first study to argue that knowledge sharing could be considered as an active activity and that individuals could be eager to perform knowledge sharing when they possess significant personal and external resources. The results of this study provide new insights into knowledge sharing.

Beschreibung: Out-of-plane wrinkling has a significant influence on the mechanical performance of composite laminates. Numerical simulations were conducted to investigate the progressive failure behavior of fiber-reinforced composite laminates with out-of-plane wrinkle defects subjected to axial compression. To describe the material degradation, a three-dimensional elastoplastic damage model with four damage modes (i.e., fiber tensile failure, matrix failure, fiber kinking/splitting, and delamination) was developed based on the LaRC05 criterion. To improve the computational efficiency in searching for the fracture angle in the matrix failure analysis, a high-efficiency and robust modified algorithm that combines the golden section search method with an inverse interpolation based on an existing study is proposed. The elastoplastic damage model was implemented in the finite-element code Abaqus using a user-defined material subroutine in Abaqus/Explicit. The model was applied to the progressive failure analysis of IM7/8552 composite laminates with out-of-plane wrinkles subjected to axial compressive loading. The numerical results showed that the compressive strength prediction obtained by the elastoplastic damage model is more accurate than that derived with an elastic damage model. The present model can describe the nonlinearity of the laminate during the damage evolution and determine the correct damage locations, which are in good agreement with experimental observations. Furthermore, it was discovered that the plasticity effects should not be neglected in laminates with low wrinkle levels.

Beschreibung: Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

Beschreibung: As humans and climate change continue to alter the landscape, novel disease risk scenarios have emerged. Sever fever with thrombocytopenia syndrome (SFTS), an emerging tick-borne infectious disease first discovered in rural areas of central China in 2009, is caused by a novel bunyavirus (SFTSV). The potential for SFTS to spread to other countries in combination with its high fatality rate, possible human-to-human transmission, and extensive prevalence among residents and domesticated animals in endemic regions make the disease a severe threat to public health. Because of the lack of preventive vaccines or useful antiviral drugs, diagnosis of SFTS is the key to prevention and control of the SFTSV infection. The development of serological detection methods will greatly improve our understanding of SFTSV ecology and host tropism. We describe a highly sensitive protein detection method based on gold nanoparticles (AuNPs) and enzyme-linked immunosorbent assay (ELISA)—AuNP-based ELISA. The optical sensitivity enhancement of this method is due to the high loading efficiency of AuNPs to McAb. This enhances the concentration of the HRP enzyme in each immune sandwich structure. The detection limit of this method to the nucleocapsid protein (NP) of SFTSV was 0.9 pg mL−1 with good specificity and reproducibility. The sensitivity of AuNP-based ELISA was higher than that of traditional ELISA and was comparable to real-time quantitative polymerase chain reaction (qRT-PCR). The probes are stable for 120 days at 4 °C. This can be applied to diagnosis and hopefully can be developed into a commercial ELISA kit. The ultrasensitive detection of SFTSV will increase our understanding of the distribution and spread of SFTSV, thus helping to monitor the changes in tick-borne pathogen SFTSV risk in the environment.

Beschreibung: Recent studies indicated that contamination by adatoms on the surface ion trap can generate contact potential, leading to fluctuations in patch potential. By investigating contamination induced by surface adatoms during a loading process, a direct physical image of the contamination process and the relationship between the capacitance change and the contamination from surface adatoms is examined theoretically and experimentally. From the relationship, the contamination by surface adatoms and the effect of in situ treatment process can be monitored by the capacitance between electrodes in real time. This study is foundational to further research on anomalous heating with practical applications in quantum information processing from surface ion traps.

Beschreibung: Printing technology offers a simple and cost-effective opportunity to develop all-printed stretchable circuits and electronic devices, possibly providing ubiquitous, low-cost, and flexible devices. To successfully prepare high-aspect-ratio Ag nanowires (NWs), we used water and anhydrous ethanol as the solvent and polyvinylpyrrolidone (PVP) as the viscosity regulator to obtain a water-soluble Ag NWs conductive ink with good printability. Flexible and stretchable fabric electrodes were directly fabricated through screen printing. After curing at room temperature, the sheet resistance of the Ag NW fabric electrode was 1.5 Ω/sq. Under a tensile strain of 0–80% and with 20% strains applied for 200 cycles, good conductivity was maintained, which was attributed to the inherent flexibility of the Ag NWs and the intrinsic structure of the interlocked texture.