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Federated learning technology solves the problems of data islands and privacy leakage to a certain extent.However it has shortcomings such as high communication cost,unstable communication,and uneven distribution of participant performance.In order to overcome these shortcomings and achieve a balance between model effectiveness,fairness,and communication costs,an improved NSGA-III algorithm for multi-objective optimization of federated learning is proposed.First,a federated learning multi-objective optimization model is constructed to maximize the accuracy of the global model,minimize the variance of the global mo-del accuracy distribution and minimize the communication cost of participant,and an improved NSGA-III algorithm based on fast greedy initialization is proposed,which improves the efficiency of NSGA-III for multi-objective optimization of federated learning.Experimental results show that the proposed optimization method can obtain a better Pareto solution than the classical multi-objective evolutionary algorithm.Compared with the standard model of federated learning,the optimized model can effectively lower the communication cost and the variance of the global model accuracy distribution while ensuring the accuracy of the global model.

Based on the idea of crowdsourcing,mobile crowdsensing(MCS) collects mobile sensing devices to sense the surroun-ding environment,which can make environment sensing and information collection more flexible,convenient and efficient.Whe-ther the task allocation strategy is reasonable or not directly affects the success of the sensing task.Therefore,formulating a reasonable task allocation strategy is a hotspot and focus in the research of MCS.At present,most of the task allocation methods in MCS systems are offline and targeted at single type tasks.However,in practice,online multi-type task allocation is more common.Therefore,this paper studies the task allocation method in MCS for multiple types of tasks,and proposes an online task allocation strategy oriented to system benefits combined with the characteristics of MCS technology in the military field.In this paper,a long-term,dynamic online task allocation system model is established,and the problem is solved based on Lyapunov optimization theory with the system benefit as the optimization goal,so that the online dynamic control of task admission strategy and task allocation scheme is realized.Experiment shows that the online task allocation algorithm proposed in this paper is effective and feasible.It can reasonably allocate the tasks arriving at the MCS system online,ensure the stability of the task queue,and increase the system utility by adjusting the parameter value.

Astaxanthin is a naturally occurring xanthophyll with powerful: antioxidant, antitumor, and antibacterial properties that are widely employed in food, feed, medicinal and nutraceutical industries. Currently, chemical synthesis dominates the world's astaxanthin market, but the increasing demand for natural products is shifting the market for natural astaxanthin. Haematococcus pluvialis (H. pluvialis) is the factory source of natural astaxanthin when grown in optimal conditions. Currently, various strategies for the production of astaxanthin have been proposed or are being developed in order to meet its market demand. This up-to-date review scrutinized the current approaches or strategies that aim to increase astaxanthin yield from H. pluvialis. We have emphasized the genetic and environmental parameters that increase astaxanthin yield. We also looked at the transcriptomic dynamics caused by environmental factors (phytohormones induction, light, salt, temperature, and nutrient starvation) on astaxanthin synthesizing genes and other metabolic changes. Genetic engineering and culture optimization (environmental factors) are effective approaches to producing more astaxanthin for commercial purposes. Genetic engineering, in particular, is accurate, specific, potent, and safer than conventional random mutagenesis approaches. New technologies, such as CRISPR-Cas9 coupled with omics and emerging computational tools, may be the principal strategies in the future to attain strains that can produce more astaxanthin. This review provides accessible data on the strategies to increase astaxanthin accumulation natively. Also, this review can be a starting point for new scholars interested in H. pluvialis research. [ABSTRACT FROM AUTHOR]

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]