Unveiling the additive-assisted oriented growth of perovskite crystallite for high performance light-emitting diodes.

Solution-processed metal halide perovskites have been recognized as one of the most promising semiconductors, with applications in light-emitting diodes (LEDs), solar cells and lasers. Various additives have been widely used in perovskite precursor solutions, aiming to improve the formed perovskite film quality through passivating defects and controlling the crystallinity. The additive's role of defect passivation has been intensively investigated, while a deep understanding of how additives influence the crystallization process of perovskites is lacking. Here, we reveal a general additive-assisted crystal formation pathway for FAPbI3 perovskite with vertical orientation, by tracking the chemical interaction in the precursor solution and crystallographic evolution during the film formation process. The resulting understanding motivates us to use a new additive with multi-functional groups, 2-(2-(2-Aminoethoxy)ethoxy)acetic acid, which can facilitate the orientated growth of perovskite and passivate defects, leading to perovskite layer with high crystallinity and low defect density and thereby record-high performance NIR perovskite LEDs (~800 nm emission peak, a peak external quantum efficiency of 22.2% with enhanced stability)

Social Relation Cognitive Model on Virtual Prototyping Technology in Construction Project

It is valuable to study social relation cognition of participants about virtual prototyping technology in construction projects to promote its acceptance and application. Building information modeling (BIM) is a transformative virtual prototyping technology for construction industry. The analysis of social relation is closely related to BIM application to stakeholders, which involves many cognitive indicators, i.e. organizations, spaces and behaviors. Based on social network analysis, a cognitive model for actorsâ?? social relation to BIM knowledge is proposed?Using survey data, this paper explores the cognitive ability between participants. Through statistical analysis of indictors in the model, it is found that significant difference of virtual prototyping technology in social relation cognition exists

A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

Large-scale projects, such as the construction of railways and highways, usually cause an extensive Land Use Land Cover Change (LULCC). The China-Central Asia-West Asia Economic Corridor (CCAWAEC), one key large-scale project of the Belt and Road Initiative (BRI), covers a region that is home to more than 1.6 billion people. Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor, reviewing LULCC mapping studies in this area has not been studied. This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area. The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor. To this end, 350 peer-reviewed journal and conference papers, as well as book chapters were analyzed based on 17 attributes, such as main driving factors of LULCC, data collection methods, classification algorithms, and accuracy assessment methods. It was observed that: (1) rapid urbanization, industrialization, population growth, and climate change have been recognized as major causes of LULCC in the study area; (2) LULCC has, directly and indirectly, caused several environmental issues, such as biodiversity loss, air pollution, water pollution, desertification, and land degradation; (3) there is a lack of well-annotated national land use data in the region; (4) there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area; and (5) several technical issues still require more attention from the scientific community. Finally, several recommendations were proposed to address the identified issues

Comparative Analysis on Two Schemes for Synthesizing the High Temporal Landsat-like NDVI Dataset Based on the STARFM Algorithm

The NDVI dataset with high temporal and spatial resolution (HTSN) is significant for extracting information about the phenological change of vegetation in regions with a complex earth surface. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been successfully applied to synthesize the HTSN by fusing the data with different characteristics. Based on the model, there are two different schemes for synthesizing the HTSN. One scheme is that red reflectance and near-infrared (NIR) reflectance are synthesized, respectively, and the HTSN is then obtained through algebraic operation (Scheme 1); the other scheme is that the red and NIR reflectance are used to calculate NDVI, which is directly taken as input data to synthesize the HTSN (Scheme 2). In this paper, taking the hill areas in eastern Sichuan China as a case, the two schemes were compared with each other. Seven Landsat images and time-series MOD13Q1 datasets spanning from October 2001 to February 2003 were used as the test data. The results showed the prediction accuracies of both derived HTSNs by the two different schemes were generally in good agreement, and Scheme 2 was slightly superior to Scheme 1 (R2: 0.14 < Scheme 1 < 0.53; 0.15 < Scheme 2 < 0.53). Although the two HTSNs showed high temporal and spatial consistence, the small spatiotemporal difference between them had a different influence on different applications. The coincidence rate of cropping intensity extracted from two derived HTSNs was fairly high, reaching up to 93.86%, while the coincidence rate of crop peak dates (i.e., the emerging dates of peaks in an annual time-series NDVI curve) was only 70.95%. Therefore, it is deemed that Scheme 2 can replace Scheme 1 in the application of extracting cropping intensity, so that more calculation time and memory space can be saved. For extracting more quantitative crop phenological information like crop peak dates, more tests are still needed in order to compare the absolute accuracy for both schemes

Romosozumab in osteoporosis: yesterday, today and tomorrow

Abstract Osteoporosis is a systemic bone disease characterized by low bone mass, microarchitectural deterioration, increased bone fragility, and fracture susceptibility. It commonly occurs in older people, especially postmenopausal women. As global ageing increases, osteoporosis has become a global burden. There are a number of medications available for the treatment of osteoporosis, categorized as anabolic and anti-resorptive. Unfortunately, there is no drugs which have dual influence on bone, while all drugs have limitations and adverse events. Some serious adverse events include jaw osteonecrosis and atypical femoral fracture. Recently, a novel medication has appeared that challenges this pattern. Romosozumab is a novel drug monoclonal antibody to sclerostin encoded by the SOST gene. It has been used in Japan since 2019 and has achieved promising results in treating osteoporosis. However, it is also accompanied by some controversy. While it promotes rapid bone growth, it may cause serious adverse events such as cardiovascular diseases. There has been scepticism about the drug since its inception. Therefore, the present review comprehensively covered romosozumab from its inception to its clinical application, from animal studies to human studies, and from safety to cost. We hope to provide a better understanding of romosozumab for its clinical application

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data

Modeling the Impact of Investment and National Planning Policies on Future Land Use Development: A Case Study for Myanmar

Land use change (LUC) can be affected by investment growth and planning policies under the context of regional economic cooperation and development. Previous studies on land use simulation mostly emphasized the effects of local socioeconomic factors and planning constraint areas that prevent land conversions. However, investment and national planning policies that trigger regional LUC were often ignored. This study aims to couple the economic theory-based Computable General Equilibrium of Land Use Change (CGELUC) model and the cellular automata-based Future Land Use Simulation (FLUS) model to incorporate macroscopic impacts of investment into land use simulation, while proposing an updated mechanism that integrates into the FLUS model to consider the local impacts of planning policies. Taking Myanmar as a case, the method was applied to project the land use patterns (LUPs) during 2017–2050 under three scenarios: baseline, fast, and harmonious development. Specifically, the simulated land use structure (LUS) in 2018 acquired by the CGELUC model was verified by the existing data, and the future LUSs under different scenarios were projected later. Simultaneously, the consistencies between the results simulated by the FLUS model and land use maps in 2013, 2015, and 2017 were represented by the kappa coefficient. The updated mechanism was applied to update the Probability-of-Occurrence (PoO) surfaces based on the planning railway networks and special economic zone. Lastly, the LUPs under different scenarios were projected based on the future LUSs and updated PoO surfaces. Results reveal that the validation accuracy reaches 96.87% for the simulated LUS, and satisfactory accuracies of the simulated LUPs are obtained (kappa coefficients > 0.83). The updated mechanism increases the mean PoO values of built-up land in areas affected by planning policies (increasing by 0.01 to 0.21), indicating the importance of the planning policies in simulation. The cultivated land and built-up land increase with investment increasing under all three scenarios. The harmonious development scenario, showing the least forest encroachment and the highest diversity of LUP, is the optimal approach to achieve land sustainability. This study highlights the impacts of investment and planning policies on future LUCs of Myanmar, and a dynamic simulation process is expected to minimize the uncertainties of the input data and model in the future work

Land cover dataset of the China Central-Asia West-Asia Economic Corridor from 1993 to 2018

Abstract Land Cover (LC) maps offer vital knowledge for various studies, ranging from sustainable development to climate change. The China Central-Asia West-Asia Economic Corridor region, as a core component of the Belt and Road initiative program, has been experiencing some of the most severe LC change tragedies, such as the Aral Sea crisis and Lake Urmia shrinkage, in recent decades. Therefore, there is a high demand for producing a fine-resolution, spatially-explicit, and long-term LC dataset for this region. However, except China, such dataset for the rest of the region (Kyrgyzstan, Turkmenistan, Kazakhstan, Uzbekistan, Tajikistan, Turkey, and Iran) is currently lacking. Here, we constructed a historical set of six 30-m resolution LC maps between 1993 and 2018 at 5-year time intervals for the seven countries where nearly 200,000 Landsat scenes were classified into nine LC types within Google Earth Engine cloud computing platform. The generated LC maps displayed high accuracies. This publicly available dataset has the potential to be broadly applied in environmental policy and management

A Simple and Automatic Method for Detecting Large-Scale Land Cover Changes Without Training Data

Automatic and accurate extraction of land use/cover change (LUCC) is essential for various applications, particularly studies on climate change and sustainable development. However, the automatic and accurate detection of LUCC in large-scale regions remains challenging due to the complexity of LUCC and the high cost of training data acquisition. To address this research gap, a simple and practical unsupervised change detection method based on multi-indices and bitemporal remote sensing image pairs (USCD-MiBi) was proposed to automatically extract LUCC in two heterogeneous experimental sites without training data. Single-index change analysis (SICA), multi-index change analysis (MICA), and bitemporal change analysis (BTCA) form the core of the USCD-MiBi method. In this method, all the selected change indices were integrated by the SICA and MICA to identify potential change regions. The potential false changes, caused by inconsistencies in atmospheric conditions and phenology, were further removed by the BTCA. Verification experiments revealed that the detection accuracy of USCD-MiBi method can exceed 90%, whether the LUCC is concentrated in urban regions or scattered in mountainous areas. It was also observed that the proposed method had higher detection performance than several other change detection methods. The proposed USCD-MiBi method offers high flexibility and adaptability, allowing users to choose the most suitable change indices based on the characteristics of their study area or the selected data sources. This study provides a simple solution for automatically detecting large-scale LUCC without training samples, making it accessible to a wider range of users

Preliminary assessment of ecosystem risk based on IUCN criteria in a hierarchy of spatial domains: A case study in Southwestern China

World ecosystems are suffering great losses from anthropogenic and natural pressures. To meet the demand of ecosystem-level risk assessment for biodiversity conservation, the IUCN has adopted a global standard to assess the risk to ecosystems. The IUCN Red List of Ecosystems (IUCN RLE) is a growing tool to raise the public awareness of ecosystem conservation and provide reasonable strategies to managers. As for managing ecosystems with the RLE, the spatial information of degraded patches is important for the efficient allocation of conservation resources. In this paper, a method for assessment across hierarchy of spatial domains was designed to provide spatial information for the IUCN RLE. 105 natural ecosystems of vegetation in Southwestern China were systematically assessed at the hierarchy of spatial domains. According to the results, the declining distributions of most ecosystems have slowed down recently due to protection policies. All vegetative ecosystems containing nationally protected species are threatened, supporting the robustness of this protocol. Limited distributions and degradation in area are the key risk to threatened ecosystems, as threatened ecosystems account for only 1.55% of the total area and the mean of its degradation in area is almost 45%. With the assessment in the hierarchy of spatial domains, the IUCN RLE was featured with spatial information of degraded patches. The hierarchical assessment markedly improved the practical applications of RLE and led to an efficient allocation of conservation resources. The scale effect of hierarchical assessment was significant and the representativeness of ecosystems in systematic assessments is extremely valuable