Principal hierarchy of Frobenius manifolds associated with rational and trigonometric Landau-Ginzburg superpotentials

In this paper, we provide explicit formulations for the principal hierarchy of Frobenius manifolds associated with both rational and trigonometric Landau-Ginzburg superpotentials. Additionally, we explore the principal hierarchy of generalized Frobenius manifolds, which underlie the dispersionless bihamiltonian structures of the Ablowitz-Ladik hierarchy and the q-deformed GelfandDickey hierarchy

Study on corrosion resistance of Portland cement-calcium sulphoaluminate cement binary system in a sodium chloride environment

Portland cement is widely used in civil engineering. However, Portland cement-based materials are easy to be corroded by seawater in marine environment. Many research show the corrosion resistance of Portland cement mortar can be improved by add appropriate amount of mineral admixture.Sulphoaluminate cement have high strength and good corrosion seawater resistance. However, the short setting time and high hydration heat of sulphoaluminate cement limit its application in civil engineering.Portland cement-sulphoaluminate cement (PC-CSA) not only have high strength and corrosion resistance but also long setting time.In this work, the sulphoaluminate cement was used to partially replace Portland cement. The replacement level of sulphoaluminate cement was 10 %, 20 % and 30 % by weight of Portland cement. Mortar specimens was soaked in sodium chloride solution under standard curing after 28 days. The concentration of sodium chloride solution was 3.5wt %. Mechanical properties , corrosion resistance and setting time of PC-CSA binary system were tested in the research. The hydration behavior of binary system was determined by isothermal calorimetry and X-ray diffraction methods. Microstructure of the binary system at different ages were analyzed by scanning electron microscope. The strength of PC-CSA binary system was tested at different curing ages up to 28 days.The results show when replacement level of sulphoaluminate cement is 20%, the comprehensive strength up to 50MPa and higher than other groups at 28 days soaked in corrosion solution.when replacement level of sulphoaluminate cement is 20%,the corrosion resistance is best,and penetration depth of chloride ions is the least

Multi-objective Dwarf Mongoose Optimization Algorithm with Leader Guidance and Dominated Solution Evolution Mechanism

In the face of the increasingly complex multi-objective optimization problems, it is necessary to develop novel multi-objective optimization algorithms to meet the challenges. This paper proposes a multi-objective dwarf mongoose optimization algorithm (MODMO) with leader guidance and dominated solution dynamic reduction evolution mechanism. In the leader guidance mechanism, a dynamic trade-off factor is introduced to regulate the search radius of the scout mongoose exploring the mound. At the same time, an external archive is constructed with a non-inferior solution set and the leader is determined according to the non-dominated ranking level, and then the scout mongoose is guided to advance to the multi-objective frontier to improve the convergence of the algorithm. The dominant solution dynamic reduction evolution strategy is constructed to overcome the redundancy problem in the process of maintaining the external archive of non-inferior solutions. It dynamically selects the dominant solutions based on the dominance relationship and crowding distance and stores them in the external archive. The dominant solution information is integrated into the population evolution to realize the mining of multi-objective potential frontier and enhance the diversity of the algorithm. Compared with five representative algorithms on ZDT, DTLZ and WFG benchmark functions, experimental results show that MODMO algorithm has significant advantages in convergence and diversity

Comprehensive transcriptome analysis reveals novel genes involved in cardiac glycoside biosynthesis and mlncRNAs associated with secondary metabolism and stress response in Digitalis purpurea

<p>Abstract</p> <p>Background</p> <p><it>Digitalis purpurea </it>is an important ornamental and medicinal plant. There is considerable interest in exploring its transcriptome.</p> <p>Results</p> <p>Through high-throughput 454 sequencing and subsequent assembly, we obtained 23532 genes, of which 15626 encode conserved proteins. We determined 140 unigenes to be candidates involved in cardiac glycoside biosynthesis. It could be grouped into 30 families, of which 29 were identified for the first time in <it>D. purpurea</it>. We identified 2660 mRNA-like npcRNA (mlncRNA) candidates, an emerging class of regulators, using a computational mlncRNA identification pipeline and 13 microRNA-producing unigenes based on sequence conservation and hairpin structure-forming capability. Twenty five protein-coding unigenes were predicted to be targets of these microRNAs. Among the mlncRNA candidates, only 320 could be grouped into 140 families with at least two members in a family. The majority of <it>D. purpurea </it>mlncRNAs were species-specific and many of them showed tissue-specific expression and responded to cold and dehydration stresses. We identified 417 protein-coding genes with regions significantly homologous or complementary to 375 mlncRNAs. It includes five genes involved in secondary metabolism. A positive correlation was found in gene expression between protein-coding genes and the homologous mlncRNAs in response to cold and dehydration stresses, while the correlation was negative when protein-coding genes and mlncRNAs were complementary to each other.</p> <p>Conclusions</p> <p>Through comprehensive transcriptome analysis, we not only identified 29 novel gene families potentially involved in the biosynthesis of cardiac glycosides but also characterized a large number of mlncRNAs. Our results suggest the importance of mlncRNAs in secondary metabolism and stress response in <it>D. purpurea</it>.</p

Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO_x emission concentration

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

STGAT-MAD: Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGATMAD). The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks. Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales. Experiments on a new wind turbine dataset (built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times