Failure Analysis: Crop production on the Lunar surface

We have sought to optimize conditions for crop yield for many years, but optimal conditions will not always be cost effective. More importantly, environmental control systems routinely fail, and we need to learn how to gracefully recover from these failures. Failures of the power supply system are among the most common and most detrimental of all system failures. A battery back-up could supply a small amount of power during a power outage, but we need to know how to best utilize the back-up power. Early in this project, it became clear that the detrimental effects of a power loss could be mitigated by reducing temperature and adding low light. This was so effective that we began to investigate crop production using natural light on the lunar surface. This requires keeping plants alive and healthy during the 14.7 day-long interval on the dark side of the Moon

Failure Analysis Research Summary: Mitigating the Effects of Prolonged Darkness With Low Temperature and Low Light

Power loss is a common failure in controlled environments. The duration of power loss can be several days – and even weeks – in space environments. Long-duration power loss and the resulting darkness can cause plants to die unless remedial measures are taken during the power outage. Emergency back-up power from batteries could provide low light and reduced air temperature. Plant metabolism and growth are reduced in low temperature. As metabolism slows, energy requirements are reduced and less light is needed. The temperature should be maintained above the chilling temperature for the plant, which is species dependent. The addition of light will allow the plant to continue to expend energy on maintenance and some growth. Here we show that low light and cool temperatures can be used to maintain plants through the 14.7 days on the dark side of the Moon. Growth resumes immediately after the light is restored

FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter

In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)

A Novel Approach to Encrypt and Decrypt Color Images

Abstract - The proposed cryptosystem avoids all the crypto graphical weaknesses of earlier chaos-based encryption systems. Number of security analysis were carried out on the new algorithm and simulation results show that encryption and decryption are good and the proposed chaotic algorithm is proven to be the good procedure in terms of robustness

Tuna Swarm Optimization with 3D-chaotic map and DNA encoding for image encryption with lossless image compression based on FPGA

Images and video-based multimedia data are growing rapidly due to communication network technology. During image compression and transmission, images are inevitably corrupted by noise due to the influence of the environment, transmission channels, and other factors, resulting in the damage and degradation of digital images. Numerous real-time applications, such as digital photography, traffic monitoring, obstacle detection, surveillance applications, automated character recognition, etc are affected by this information loss. Therefore, the efficient and safe transmission of data has become a vital study area. In this research, an image compression–encryption system is proposed to achieve security with low bandwidth and image de-noising issues during image transmission. The Chevrolet transformation is proposed to improve image compression quality, reduce storage space, and enhance de- noising. A 3D chaotic logistic map with DNA encoding and Tuna Swarm Optimization is employed for innovative image encryption. This optimization approach may significantly increase the image\u27s encryption speed and transmission security. The proposed system is built using the Xilinx system generator tool on a field-programmable gate array (FPGA). Experimental analysis and experimental findings show the reliability and scalability of the image compression and encryption technique designed. For different images, the security analysis is performed using several metrics and attains 32.33 dB PSNR, 0.98 SSIM, and 7.99721 information entropy. According to the simulation results, the implemented work is more secure and reduces image redundancy more than existing methods

Large‐scale pathogenicity prediction analysis of cancer‐associated kinase mutations reveals variability in sensitivity and specificity of computational methods

Abstract Background Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants. Aims Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations. Materials and methods We employed a set of computational tools to predict the pathogenicity of over forty‐two thousand mutations and deposited the kinase‐wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]). Results Mutations are more likely to be drivers when being present in the kinase domain (vs. non‐kinase domain) and belonging to hotspot residues (vs. non‐hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen‐2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy. Discussion The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented. Conclusion Primary‐structure‐based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations