ABSTRACT
The sun and outer space are two crucial renewable thermodynamic resources that work together to maintain the delicate energy balance of our planet. The challenge lies in harvesting both resources synergistically and converting them into high-quality electricity. Here, we introduce a photovoltaic thermoelectric radiative cooling (PV-TE-RC) system. This system uses the full spectrum of the sun and the atmospheric window to generate electricity and achieve high-quality collaborative utilization of solar energy and space energy. Outdoor experiments have demonstrated the system's capacity to operate efficiently around the clock. Notably, during the peak solar concentration, the thermoelectric generator (TEG) and the system achieved power outputs of 870 mW/m2 and 85.87 W/m2, respectively. We have further developed a three-dimensional transient coupled simulation model, which can accurately predict its operational limits. Therefore, this study provides practical insights and recommendations for large-scale and efficient collaborative power generation using these two thermodynamic resources.
ABSTRACT
In this paper, an Adaptive Fusion Transformer (AFT) is proposed for unsupervised pixel-level fusion of visible and infrared images. Different from the existing convolutional networks, transformer is adopted to model the relationship of multi-modality images and explore cross-modal interactions in AFT. The encoder of AFT uses a Multi-Head Self-attention (MSA) module and Feed Forward (FF) network for feature extraction. Then, a Multi-head Self-Fusion (MSF) module is designed for the adaptive perceptual fusion of the features. By sequentially stacking the MSF, MSA, and FF, a fusion decoder is constructed to gradually locate complementary features for recovering informative images. In addition, a structure-preserving loss is defined to enhance the visual quality of fused images. Extensive experiments are conducted on several datasets to compare our proposed AFT method with 21 popular approaches. The results show that AFT has state-of-the-art performance in both quantitative metrics and visual perception.
ABSTRACT
With the large-scale application of composite materials in military aircraft, various composite material detection technologies with infrared nondestructive and ultrasonic nondestructive testing as the core have played an important role in detecting composite material component damage in military aircraft. At present, the damage of composite materials is mostly recognized manually, which is time-consuming, laborious, and inefficient. It can effectively improve detection efficiency and accuracy by using intelligent detection methods to detect and recognize damage. Moreover, the effect of infrared detection is significantly reduced with increasing detection depth, while ultrasonic detection has shallow-blind areas. A cascade fusion R-CNN network is proposed in order to comprehensively identify composite material damage. This network realizes the intelligent fusion recognition of infrared and ultrasonic damage images of composite materials. The network is based on a cascade R-CNN network, using fusion modules and BiFPN for improvement. For the infrared image and ultrasonic C-scan image data set established in this paper, the algorithm can identify the type and location of damage detected by infrared and ultrasonic testing. Its recognition accuracy is 99.3% and mean average precision (mAP) is 90.4%. In the detection process, the characteristics of infrared and ultrasonic images are used to realize the recognition of damage depth. Compared to SSD, YOLOv4, faster R-CNN and cascade R-CNN, the network proposed in this paper is better and more effective.
ABSTRACT
BACKGROUND: The creation of the Global Fund to Fight AIDS, Tuberculosis and Malaria, also known as the Global Fund, was prompted by the lack of a timely and effective global response, and the need for financing to fight against three devastating diseases: HIV/AIDS, tuberculosis, and malaria. During the formation of the Global Fund, necessary anti-corruption, transparency, and accountability (ACTA) structures were not put in place to prevent fraud and corruption in its grants, which resulted in the misuse of funds by grant recipients and an eventual loss of donor confidence in 2011. The Global Fund has instituted various ACTA mechanisms to address this misuse of funding and the subsequent loss of donor confidence, and this paper seeks to understand these implementations and their impacts over the past decade, in an effort to probe ACTA more deeply. RESULTS: By restructuring the governing committees in 2011, and the Audit and Finance; Ethics and Governance; and Strategy Committees in 2016, the Global Fund has delineated committee mandates and strengthened the Board's oversight of operations. Additionally, the Global Fund has adopted a rigorous risk management framework which it has worked into all aspects of its functioning. An Ethics and Integrity Framework was adopted in 2014 and an Ethics Office was established in 2016, resulting in increased conflict of interest disclosures and greater considerations of ethics within the organization. The Global Fund's Office of the Inspector General (OIG) has effectively performed internal and external audits and investigations on fraud and corruption, highlighted potential risks for mitigation, and has implemented ACTA initiatives, such as the I Speak Out Now! campaign to encourage whistleblowing and educate on fraud and corruption. CONCLUSIONS: From 2011 onwards, the Global Fund has developed a number of ACTA mechanisms which, in particular, resulted in reduced grant-related risks and procurement fraud as demonstrated by the decreased classification from high to moderate in 2017, and the reduction of investigations in 2018 respectively. However, it is crucial that the Global Fund continues to evaluate the effectiveness of these mechanisms; monitor for potential perverse impacts; and make necessary changes, when and where they are needed.
