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Background Sporobolomyces pararoseus is regarded as an oleaginous red yeast, which synthesizes numerous valuable compounds with wide industrial usages. This species hold biotechnological interests in biodiesel, food and cosmetics industries. Moreover, the ballistospores-shooting promotes the colonizing of S. pararoseus in most terrestrial and marine ecosystems. However, very little is known about the basic genomic features of S. pararoseus . To assess the biotechnological potential and ballistospores-shooting mechanism of S. pararoseus on genome-scale, the whole genome sequencing was performed by next-generation sequencing technology. Results Here, we used Illumina Hiseq platform to firstly assemble S. pararoseus genome into 20.9 Mb containing 54 scaffolds and 5963 predicted genes with a N50 length of 2,038,020 bp and GC content of 47.59%. Genome completeness (BUSCO alignment: 95.4%) and RNA-seq analysis (expressed genes: 98.68%) indicated the high-quality features of the current genome. Through the annotation information of the genome, we screened many key genes involved in carotenoids, lipids, carbohydrate metabolism and signal transduction pathways. A phylogenetic assessment suggested that the evolutionary trajectory of the order Sporidiobolales species was evolved from genus Sporobolomyces to Rhodotorula through the mediator Rhodosporidiobolus . Compared to the lacking ballistospores Rhodotorula toruloides and Saccharomyces cerevisiae , we found genes enriched for spore germination and sugar metabolism. These genes might be responsible for the ballistospores-shooting in S. pararoseus NGR. Conclusion These results greatly advance our understanding of S. pararoseus NGR in biotechnological potential and ballistospores-shooting, which help further research of genetic manipulation, metabolic engineering as well as its evolutionary direction.

The evolutionary relationships of proteobacteria, which comprise the largest and phenotypically most diverse division among prokaryotes, are examined based on the analyses of available molecular sequence data. Sequence alignments of different proteins have led to the identification of numerous conserved inserts and deletions (referred to as signature sequences), which either are unique characteristics of various proteobacterial species or are shared by only members from certain subdivisions of proteobacteria. These signature sequences provide molecular means to define the proteobacterial phyla and their various subdivisions and to understand their evolutionary relationships to the other groups of eubacteria as well as the eukaryotes. Based on signature sequences that are present in different proteins it is now possible to infer that the various eubacterial phyla evolved from a common ancestor in the following order: low-G+C Gram-positive⇒high-G+C Gram-positive⇒ Deinococcus-Thermus (green nonsulfur bacteria)⇒cyanobacteria⇒ Spirochetes⇒ Chlamydia-Cytophaga-Aquifex-green sulfur bacteria⇒Proteobacteria-1 (ϵ and δ)⇒Proteobacteria-2 (α)⇒Proteobacteria-3 (β)⇒Proteobacteria-4 (γ). An unexpected but important aspect of the relationship deduced here is that the main eubacterial phyla are related to each other linearly rather than in a tree-like manner, suggesting that the major evolutionary changes within Bacteria have taken place in a directional manner. The identified signatures permit placement of prokaryotes into different groups/divisions and could be used for determinative purposes. These signatures generally support the origin of mitochondria from an α-proteobacterium and provide evidence that the nuclear cytosolic homologs of many genes are also derived from proteobacteria.

Assembly sequence planning (ASP) has always been an important part of the product development process, and ASP problem can usually be understood as to determine the sequence of assembly. A good assembly sequence can reduce the time and cost of the manufacturing process. In view of the local convergence problem with basic discrete particle swarm optimization (DPSO) in ASP, this paper presents a hybrid algorithm to solve ASP problem. First, a chosen strategy of global optimal particle in DPSO is introduced, and then an improved discrete particle swarm optimization (IDPSO) is proposed for solving ASP problems. Through an example study, the results show that the IDPSO algorithm can obtain the global optimum efficiently, but it converges slowly compared with the basic DPSO. Subsequently, a modified evolutionary direction operator (MEDO) is used to accelerate the convergence rate of IDPSO. The results of the case study show that the new hybrid algorithm MEDO-IDPSO is more efficient for solving ASP problems, with excellent global convergence properties and fast convergence rate.

According to the actual requirements, profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling. Because of mechanical wear, roll diameter has some uncertainty during the rolling process, ignoring which will cause poor robustness of rolling schedule. In order to solve this problem, a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established. A differential evolution algorithm based on the evolutionary direction was proposed. The algorithm calculated the horizontal angle of the vector, which was used to choose mutation vector. The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm. Efficiency of the proposed algorithm was verified by two benchmarks. Meanwhile, in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution, a modified Latin Hypercube Sampling process was proposed. Finally, the proposed algorithm was applied to the model above. Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule. Meanwhile, robustness of solutions was ensured.

Abstract The evolutionary relationships of proteobacteria, which comprise the largest and phenotypically most diverse division among prokaryotes, are examined based on the analyses of available molecular sequence data. Sequence alignments of different proteins have led to the identification of numerous conserved inserts and deletions (referred to as signature sequences), which either are unique characteristics of various proteobacterial species or are shared by only members from certain subdivisions of proteobacteria. These signature sequences provide molecular means to define the proteobacterial phyla and their various subdivisions and to understand their evolutionary relationships to the other groups of eubacteria as well as the eukaryotes. Based on signature sequences that are present in different proteins it is now possible to infer that the various eubacterial phyla evolved from a common ancestor in the following order: low-G+C Gram-positive⇒high-G+C Gram-positive⇒Deinococcus-Thermus (green nonsulfur bacteria)⇒cyanobacteria⇒Spirochetes⇒Chlamydia-Cytophaga-Aquifex-green sulfur bacteria⇒Proteobacteria-1 (ε and δ)⇒Proteobacteria-2 (α)⇒Proteobacteria-3 (β)⇒Proteobacteria-4 (γ). An unexpected but important aspect of the relationship deduced here is that the main eubacterial phyla are related to each other linearly rather than in a tree-like manner, suggesting that the major evolutionary changes within Bacteria have taken place in a directional manner. The identified signatures permit placement of prokaryotes into different groups/divisions and could be used for determinative purposes. These signatures generally support the origin of mitochondria from an α-proteobacterium and provide evidence that the nuclear cytosolic homologs of many genes are also derived from proteobacteria.

With the increase in the usage of the Internet, a large amount of information is exchanged between different communicating devices. The data should be communicated securely between the communicating devices and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems (IDSs) are therefore widely used along with other security mechanisms such as firewall and access control. Many research ideas have been proposed pertaining to the IDS using machine learning (ML) techniques, deep learning (DL) techniques, and swarm and evolutionary algorithms (SWEVO). These methods have been tested on the datasets such as DARPA, KDD CUP 99, and NSL-KDD using network features to classify attack types. This paper surveys the intrusion detection problem by considering algorithms from areas such as ML, DL, and SWEVO. The survey is a representative research work carried out in the field of IDS from the year 2008 to 2020. The paper focuses on the methods that have incorporated feature selection in their models for performance evaluation. The paper also discusses the different datasets of IDS and a detailed description of recent dataset CIC IDS-2017. The paper presents applications of IDS with challenges and potential future research directions. The study presented, can serve as a pedestal for research communities and novice researchers in the field of network security for understanding and developing efficient IDS models.

Evolutionary Reinforcement Learning (ERL) has garnered widespread attention in recent years due to its inherent robustness and parallelism. However, the integration of Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) remains relatively rudimentary and lacks dynamism, which can impact the convergence performance of ERL algorithms. In this study, a dynamic adaptive module is introduced to balance the Evolution Strategies (ES) and RL training within ERL. By incorporating elite strategies, this module leverages advantageous individuals to elevate the overall population's performance. Additionally, RL strategy updates often lack guidance from the population. To address this, we incorporate the strategies of the best individuals from the population, providing valuable policy direction. This is achieved through the formulation of a loss function that employs either L1 or L2 regularization to facilitate RL training. The proposed framework is referred to as Adaptive Evolutionary Reinforcement Learning (AERL). The effectiveness of our framework is evaluated by adopting Soft Actor-Critic (SAC) as the RL algorithm and comparing it with other algorithms in the MuJoCo environment. The results underscore the outstanding convergence performance of our proposed Adaptive Evolutionary Soft Actor-Critic (AESAC) algorithm. Furthermore, ablation experiments are conducted to emphasize the necessity of these two improvements. It is worth noting that the enhancements in AESAC are realized at the population level, enabling broader exploration and effectively reducing the risk of falling into local optima.

Metaheuristic algorithms are increasingly utilized to solve complex optimization problems because they can efficiently explore large solution spaces. The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, drawing inspiration from the asexual reproduction, sexual reproduction, and vegetative reproduction of moss, two novel search strategies, namely spore dispersal search and dual propagation search, are proposed for exploration and exploitation, respectively. Finally, the cryptobiosis mechanism alters the traditional metaheuristic algorithm’s approach of directly modifying individuals’ solutions, preventing the algorithm from getting trapped in local optima. In experiments, a thorough investigation is undertaken on the characteristics, parameters, and time cost of the MGO algorithm to enhance the understanding of MGO. Subsequently, MGO is compared with 10 original and advanced CEC 2017 and CEC 2022 algorithms to verify its performance advantages. Lastly, this paper applies MGO to four real-world engineering problems to validate its effectiveness and superiority in practical scenarios. The results demonstrate that MGO is a promising algorithm for tackling real challenges. The source codes of the MGO are available at https://aliasgharheidari.com/MGO.html and other websites. Graphical Abstract Graphical Abstract

In addition to traditional tasks such as prediction, classification and translation, deep learning is receiving growing attention as an approach for music generation, as witnessed by recent research groups such as Magenta at Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is in using the capacity of deep learning architectures and training techniques to automatically learn musical styles from arbitrary musical corpora and then to generate samples from the estimated distribution. However, a direct application of deep learning to generate content rapidly reaches limits as the generated content tends to mimic the training set without exhibiting true creativity. Moreover, deep learning architectures do not offer direct ways for controlling generation (e.g., imposing some tonality or other arbitrary constraints). Furthermore, deep learning architectures alone are autistic automata which generate music autonomously without human user interaction, far from the objective of interactively assisting musicians to compose and refine music. Issues such as control, structure, creativity and interactivity are the focus of our analysis. In this paper, we select some limitations of a direct application of deep learning to music generation and analyze why the issues are not fulfilled and how to address them by possible approaches. Various examples of recent systems are cited as examples of promising directions.

This research tries to find the best operation strategies for a reservoir system with the Flow Direction Algorithm (FDA), which was recently introduced. This study evaluates the implementation of the FDA, for the first time, for optimizing the hydropower operation of the Karun-4 reservoir in Iran for 106 months (from October 2010 to July 2019) and for the multi-reservoir systems for 12 months. Multi-Reservoir systems which are hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The results are compared to those of the three most commonly used evolutionary algorithms, namely the Particle Swarm Optimization Algorithm (PSO), the Weed Algorithm (WA), and the Genetic Algorithm (GA). The multi-reservoir results indicated that the absolute optimal solution was 308.292 in the four-reservoir benchmark system (FRBS) and 1194.441 in the ten-reservoir benchmark system (TRBS), and according to these results, FDA outperformed three other algorithms. In the Karun-4 reservoir, the best approach was chosen with the analytical hierarchy process (AHP) method, and according to the results, the FDA outperformed PSO, WA, and GA. The reliability percentage for FDA, PSO, WA, and GA was 95%, 86%, 78%, and 64%, respectively. The average optimal objective function value generated by FDA was 0.138, compared with PSO, WA, and GA, with the values of 0.322, 0.631, and 1.112, respectively, being better. The hydropower produced by FDA was more than three other algorithms in less time, with the lowest coefficient of variation value, which demonstrates the power of the FDA.