Concise and Efficient Multi-Identity Fully Homomorphic Encryption Scheme

Combining multi-key fully homomorphic encryption (MKFHE) and identity-based encryption (IBE) to construct multi-identity based fully homomorphic encryption (MIBFHE) scheme can not only realize homomorphic operations and flexible access control on identity ciphertexts but also reduce the burden of public key certification management. However, MKFHE schemes used to construct MIBFHE usually have complex construction and large computational complexity, which also causes the same problem for MIBFHE schemes. To solve this problem, we construct a concise and efficient MIBFHE scheme based on the learning with errors (LWE) problem. Firstly, we construct an MKFHE scheme using a new method called “the decomposition method”. Secondly, we make a suitable deformation of the current IBE scheme. Finally, we combine the above MKFHE scheme with IBE scheme to construct our MIBFHE scheme and prove its IND-sID-CPA security under the LWE assumption in the random oracle model. The analysis results show that our MIBFHE scheme can generate the extended ciphertext directly from the encryption algorithm, without generating fresh ciphertext in advance. In addition, the noise expansion rate is reduced from the polynomial of lattice dimension n and modulus q to the constant K of the maximum number of users. The scale of introduced auxiliary ciphertexts is reduced from $\tilde {O}(n^{4}L^{4})$ to $\tilde {O}(n^{2}L^{4})$ when generating the extended ciphertext

Optimized Parameters for Detecting Multiple Forest Disturbance and Recovery Events and Spatiotemporal Patterns in Fast-Regrowing Southern China

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 105 km2 during 1990–2020 (4931 km2/year); however, most of these disturbed forests were recovered and only 6.39 × 104 km2 was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 105 km2, among which, the forest gain area after loss was 8.94 × 104 km2, and the net gain area from afforestation was 4.55 × 105 km2. The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China

Phytoremediation of copper-contaminated soils by rapeseed (Brassica napus L.) and underlying molecular mechanisms for copper absorption and sequestration

High levels of copper released in the soil, mainly from anthropogenic activity, can be hazardous to plants, animals, and humans. The present research aimed to estimate the suitability and effectiveness of rapeseed (Brassica napus L.) as a possible soil remediation option and to uncover underlying adaptive mechanisms A pot experiment was conducted to explore the effect of copper stress on agronomic and yield traits for 32 rapeseed genotypes. The copper-tolerant genotype H2009 and copper-sensitive genotype ZYZ16 were selected for further physiological, metabolomic, and transcriptomic analyses. The results exhibited a significant genotypic variation in copper stress tolerance in rapeseed. Specifically, the ratio of seed yield under copper stress to control ranged from 0.29 to 0.74. Furthermore, the proline content and antioxidant enzymatic activities in the roots were greater than those in the shoots. The accumulated copper in the roots accounted for about 50% of the total amount absorbed by plants; thus, the genotypes possessing high root volumes can be used for rhizofiltration to uptake and sequester copper. Additionally, the pectin and hemicellulose contents were significantly increased by 15.6% and 162%, respectively, under copper stress for the copper-tolerant genotype, allowing for greater sequestration of copper ions in the cell wall and lower oxidative stress. Comparative analysis of transcriptomes and metabolomes revealed that excessive copper enhanced the up-regulation of functional genes or metabolites related to cell wall binding, copper transportation, and chelation in the copper-tolerant genotype. Our results suggest that copper-tolerant rapeseed can thrive in heavily copper-polluted soils with a 5.85% remediation efficiency as well as produce seed and vegetable oil without exceeding food quality standards for the industry. This multi-omics comparison study provides insights into breeding copper-tolerant genotypes that can be used for the phytoremediation of heavy metal-polluted soils

开放科学中文社区(Chinese Open Science Network)

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