A Machine Learning Method for Modeling Wind Farm Fatigue Load

Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farm-level fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment

Orsay virus CP-δ adopts a novel β-bracelet structural fold and incorporates into virions as a head fiber

Fiber proteins are commonly found in eukaryotic and prokaryotic viruses, where they play important roles in mediating viral attachment and host cell entry. They typically form trimeric structures and are incorporated into virions via noncovalent interactions. Orsay virus, a small RNA virus which specifically infects the laboratory model nematod

Functional interplay between SA1 and TRF1 in telomeric DNA binding and DNA-DNA pairing

Proper chromosome alignment and segregation during mitosis depend on cohesion between sister chromatids. Cohesion is thought to occur through the entrapment of DNA within the tripartite ring (Smc1, Smc3 and Rad21) with enforcement from a fourth subunit (SA1/SA2). Surprisingly, cohesin rings do not play a major role in sister telomere cohesion. Instead, this role is replaced by SA1 and telomere binding proteins (TRF1 and TIN2). Neither the DNA binding property of SA1 nor this unique telomere cohesion mechanism is understood. Here, using single-molecule fluorescence imaging, we discover that SA1 displays two-state binding on DNA: searching by one-dimensional (1D) free diffusion versus recognition through subdiffusive sliding at telomeric regions. The AT-hook motif in SA1 plays dual roles in modulating non-specific DNA binding and subdiffusive dynamics over telomeric regions. TRF1 tethers SA1 within telomeric regions that SA1 transiently interacts with. SA1 and TRF1 together form longer DNA-DNA pairing tracts than with TRF1 alone, as revealed by atomic force microscopy imaging. These results suggest that at telomeres cohesion relies on the molecular interplay between TRF1 and SA1 to promote DNA-DNA pairing, while along chromosomal arms the core cohesin assembly might also depend on SA1 1D diffusion on DNA and sequence-specific DNA binding

A New Blockchain-Based Multi-Level Location Secure Sharing Scheme

Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes

A New Blockchain-Based Multi-Level Location Secure Sharing Scheme

Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes

Development Trend and Path of Industrial Internet Security Industry in China

Industrial Internet security is a prerequisite and guarantee for the high-quality development of China’s industrial Internet industry; it is also important for enhancing China’s cyber and manufacturing industries. This study aims at the future development of the industrial Internet security industry in China. First, we analyze the development status of the industry in terms of industrial policies, standards system, industrial structure, and industrial scale. Subsequently, we elaborate on the opportunities and trends of the industry and propose the development path for the next-generation industrial Internet security industry in China. To explore a sustainable development path suitable for China’s industrial Internet security industry, top-level design and policy guidance should be enhanced; technological innovation and transformation should be reinforced; advantages of enterprises and organizations should be complemented to construct a healthy development ecosystem; the security and stability of the supply chain should be emphasized; and personnel training and team building should be promoted to support the research collaboration among government, industry, universities, research institutes, and application. 

Comprehensive prediction of immune microenvironment and hot and cold tumor differentiation in cutaneous melanoma based on necroptosis-related lncRNA

Abstract As per research, causing cancer cells to necroptosis might be used as a therapy to combat cancer drug susceptibility. Long non-coding RNA (lncRNA) modulates the necroptosis process in Skin Cutaneous Melanoma (SKCM), even though the precise mechanism by which it does so has yet been unknown. RNA sequencing and clinical evidence of SKCM patients were accessed from The Cancer Genome Atlas database, and normal skin tissue sequencing data was available from the Genotype-Tissue Expression database. Person correlation analysis, differential screening, and univariate Cox regression were successively utilized to identify necroptosis-related hub lncRNAs. Following this, we adopt the least absolute shrinkage and selection operator regression analysis to construct a risk model. The model was evaluated on various clinical characteristics using many integrated approaches to ensure it generated accurate predictions. Through risk score comparisons and consistent cluster analysis, SKCM patients were sorted either high-risk or low-risk subgroups as well as distinct clusters. Finally, the effect of immune microenvironment, m7G methylation, and viable anti-cancer drugs in risk groups and potential clusters was evaluated in further detail. Included USP30-AS1, LINC01711, LINC00520, NRIR, BASP1-AS1, and LINC02178, the 6 necroptosis-related hub lncRNAs were utilized to construct a novel prediction model with excellent accuracy and sensitivity, which was not influenced by confounding clinical factors. Immune-related, necroptosis, and apoptosis pathways were enhanced in the model structure, as shown by Gene Set Enrichment Analysis findings. TME score, immune factors, immune checkpoint-related genes, m7G methylation-related genes, and anti-cancer drug sensitivity differed significantly between the high-risk and low-risk groups. Cluster 2 was identified as a hot tumor with a better immune response and therapeutic effect. Our study may provide potential biomarkers for predicting prognosis in SKCM and provide personalized clinical therapy for patients based on hot and cold tumor classification