Design of Virtual Objects Using Transformation Optics

Two structures of virtual targets filled with metamaterials are investigated through transformation optics to tailor the specific electromagnetic fields into desired spatial patterns. One virtual structure is a square column object transformed from a dielectric cylinder and the other virtual structure is a cylinder object transformed from a dielectric square column. Because the electromagnetic parameters in the virtual objects are obtained from real objects by the method of transformation optics, the scattering fields of virtual structures are the same as those of the real objects. The numerical simulations further prove the correction of theoretical results

Strengthening climate prevention through economic globalization, clean energy, and financial development in N11 countries: evidence from advance panel estimations

This study evaluates the relevancy of economic globalization, financial development, and clean energy, in strengthening the environmental sustainability of the next 11 economies over a time period pertaining to 1995–2018. In order to achieve the objective of this study, the advanced panel estimation techniques of unit root testing, and the cointegration analysis have been applied due to the presence of the cross-sectional-dependence and heterogeneity of the slope parameters in the panel data. The long-run output coefficients have been estimated through the Cross-Sectional Autoregressive Distributive Lag Model (CS-ARDL). Moreover, the causality test for a heterogeneous panel has also been employed in order to determine the causal relationships among the variables that are under study. Our empirical findings of these tests indicate that financial development and economic globalization tend to contribute to the deterioration of environmental quality, but clean energy is productive for its improvement. The bi-directional causal relationship is recognized to exist between CO2 emission and all the variables. Based on these findings, the study recommends adopting economic growth policies that are aligned with the defined environmental regulations, thus promoting the use of more clean energy resources. These include resources such as renewable energy and incorporating the environmental welfare goals into financial development plans in N11 economies

Internet Of Rights(IOR) In Role Based Block Chain

A large amount of data has been accumulated. with the development of the Internet industry. Many problems have been exposed with data explosion: 1. The contradiction between data privacy and data collaborations; 2. The contradiction between data ownership and the right of data usage; 3. The legality of data collection and data usage; 4. The relationship between the governance of data and the governance of rules; 5. Traceability of evidence chain. In order to face such a complicated situation, many algorithms were proposed and developed. This article tries to build a model from the perspective of blockchain to make some breakthroughs.Internet Of Rights(IOR) model uses multi-chain technology to logically break down the consensus mechanism into layers, including storage consensus, permission consensus, role consensus, transaction consensus etc. thus to build a new infrastructure, which enables data sources with complex organizational structures and interactions to collaborate smoothly on the premise of protecting data privacy. With blockchain's nature of decentralization, openness, autonomy, immutability, and controllable anonymity, Internet Of Rights(IOR) model registers the ownership of data, enables applications to build ecosystem based on responsibilities and rights. It also provides cross-domain processing with privacy protection, as well as the separation of data governance and rule governance. With the processing capabilities of artificial intelligence and big data technology, as well as the ubiquitous data collection capabilities of the Internet of Things, Internet Of Rights(IOR) model may provide a new infrastructure concept for realizing swarm intelligence and building a new paradigm of the Internet, i.e. intelligent governance

Ilexonin A Promotes Neuronal Proliferation and Regeneration via Activation of the Canonical Wnt Signaling Pathway after Cerebral Ischemia Reperfusion in Rats

Aims. Ilexonin A (IA), a component of the Chinese medicine Ilex pubescens, has been shown to be neuroprotective during ischemic injury. However, the specific mechanism underlying this neuroprotective effect remains unclear. Methods. In this study, we employed a combination of immunofluorescence staining, western blotting, RT-PCR, and behavioral tests, to investigate the molecular mechanisms involved in IA regulation of neuronal proliferation and regeneration after cerebral ischemia and reperfusion in rodents. Results. Increases in β-catenin protein and LEF1 mRNA and decreases in GSK3β protein and Axin mRNA observed in IA-treated compared to control rodents implicated the canonical Wnt pathway as a key signaling mechanism activated by IA treatment. Furthermore, rodents in the IA treatment group showed less neurologic impairment and a corresponding increase in the number of Brdu/nestin and Brdu/NeuN double positive neurons in the parenchymal ischemia tissue following middle cerebral artery occlusion compared to matched controls. Conclusion. Altogether, our data indicate that IA can significantly diminish neurological deficits associated with cerebral ischemia reperfusion in rats as a result of increased neuronal survival via modulation of the canonical Wnt pathway

CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning

Copy-move forgery detection aims at detecting duplicated regions in a suspected forged image, and deep learning based copy-move forgery detection methods are in the ascendant. These deep learning based methods heavily rely on synthetic training data, and the performance will degrade when facing new tasks. In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks. CMFDFormer consists of a MiT (Mix Transformer) backbone network and a PHD (Pluggable Hybrid Decoder) mask prediction network. The MiT backbone network is a Transformer-style network which is adopted on the basis of comprehensive analyses with CNN-style and MLP-style backbones. The PHD network is constructed based on self-correlation computation, hierarchical feature integration, a multi-scale cycle fully-connected block and a mask reconstruction block. The PHD network is applicable to feature extractors of different styles for hierarchical multi-scale information extraction, achieving comparable performance. Last but not least, we propose a PCSD continual learning framework to improve the forgery detectability and avoid catastrophic forgetting when handling new tasks. Our continual learning framework restricts intermediate features from the PHD network, and takes advantage of both cube pooling and strip pooling. Extensive experiments on publicly available datasets demonstrate the good performance of CMFDFormer and the effectiveness of the PCSD continual learning framework.Comment: 12pages,7 figure

Inter-Particle Electronic and Ionic Modifications of the Ternary Ni-Co-Mn Oxide for Efficient and Stable Lithium Storage

A combined electronic and ionic interparticular modification strategy is designed for the improvement of lithium storage in the layer structured ternary Ni-Co-Mn oxide (LiNi0.6Co0.2Mn0.2O2) in the form of spherical particles. In this design, a thin layer of the ion conducting polypropylene carbonate is applied to wrap the individual oxide particles for three purposes: (1) prevention of direct stacking and packing between oxide particles that will otherwise impede or block ions from accessing all the surface of the oxide particles, (2) provision of additional ionic pathways between the oxide particles, and (3) stabilization of the oxide particles during lithium storage and release. The design includes also the use of nitrogen doped carbon nanotubes for electronic connection between the polymer coated individual spheres of the layered nickel-rich LiNi0.6Co0.2Mn0.2O2. According to the physicochemical and electrochemical characterizations, and laboratory battery tests, it can be concluded that the LiNi0.6Co0.2Mn0.2O2 composite has a unique porous structure that is assembled by the polymer coated ternary oxide microspheres and the nitrogen-doped carbon nanotube networks. Significant improvements are achieved in both the ionic and electronic conductivities (double or more increase), and in discharge specific capacity (201.3 mAh·g−1 at 0.1 C, improved by 13.28% compared to the non-modified LiNi0.6Co0.2Mn0.2O2), rate performance and cycling stability (94.40% in capacity retention after 300 cycles at 1.0 C)