Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

CD74:a prospective marker for reactive microglia?

Cluster of differentiation 74(CD74),also called as major histocompatibility complex class Ⅱ(MHCⅡ)invariant chain,is involved in trafficking MHCⅡ cell surface molecules on antigen-presenting cells and has been implicated in many signaling pathways.For example,the interaction between CD74 and macrophage migration inhibitory factor cyto kine(MIF) leads to the activation of a plethora of pathways such as extracellular regulated protein kinases,phosphoinositide 3-kinase.

The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding

Grapevine is one of the most economically important crops worldwide.However,the previous versions of the grapevine reference genome tipically consist of thousands of fragments with missing centromeres and telomeres,limiting the accessibility of the repetitive sequences,the centromeric and telomeric regions,and the study of inheritance of important agronomic traits in these regions.Here,we assembled a telomere-to-telomere(T2T)gap-free reference genome for the cultivar PN40024 using PacBio HiFi long reads.The T2T reference genome(PN_T2T)is 69 Mb longer with 9018 more genes identified than the 12X.v0 version.We annotated 67%repetitive sequences,19 centromeres and 36 telomeres,and incorporated gene annotations of previous versions into the PN_T2T assembly.We detected a total of 377 gene clusters,which showed associations with complex traits,such as aroma and disease resistance.Even though PN40024 derives from nine generations of selfing,we still found nine genomic hotspots of heterozygous sites associated with biological processes,such as the oxidation–reduction process and protein phosphorylation.The fully annotated complete reference genome therefore constitutes an important resource for grapevine genetic studies and breeding programs.

广州市户籍人口与流动人口就医行为差异及影响因素分析

为了解广州市户籍人口和流动人口就医行为的差异及其影响因素,在广州市随机选取户籍人口613人、流动人口258人进行问卷调查。户籍人口与流动人口最近一次患病未就诊的比例分别为46.66%、55.04%。Logistic回归分析发现流动人口以及认为就医费用贵、自评健康状况更好、所患疾病更轻微的居民患病后更倾向于不就医,而距离就医机构更方便的居民患病后更倾向于就医。

德国养老保险制度的改革:重构福利国家的边界

论文衔接欧洲福利国家的理论路径,分析德国传统的养老保险制度的核心特征,包括一个中等程度的"去商品化系数"、较高的法团主义系数以及一个带有阶层区分特征的养老体系。进入21世纪以来,德国社会养老保险制度方面的一系列改革极大地改变了其自身的历史原貌。在基本制度设计方面,德国将过去的唯一支柱模式转化为三支柱模式,其核心改革措施为建立国家鼓励的私人基金积累的养老制度,其改革的标志事件是建立"里斯特"养老金。与英伦国家和许多其他国家的基金积累模式不同的是,德国创造性地建立了一个国家严格监管的、严格控制市场准入和投资的社会福利市场,打破了过去"国家"和"市场"相互区分的二元模式。

Taylor法则的实证研究:以1990年代的香港为例

本文研究90年代香港利率的决定模式。近年来,Taylor(泰勒)法则越来越多地被应用于货币研究。该法则认为短期利率可定义为产出缺口和通胀目标偏离的线性函数。尽管这一法则适用于很多经济体,但未必适用于香港。因为香港起码在两个方面跟别的经济体不同:没有与其他经济体相同的央行(或货币当局),与此同时,它又是一个国际金融中心。通过实证研究我们得出如下结论:与其他经济体不同,虽然通胀偏离和产出缺口在香港利率决定中的作用不可忽视,但金融变量如股价似乎在利率决定中起更大的作用。

三苯三戊并烯及其衍生物的合成研究进展

三苯三戊并烯类化合物含三个相互稠合的茚满单元具有独特的刚性骨架,C_(3v)对称性,凹凸杯状结构.该类化合物的合成研究具有重要的理论意义和应用前景.结合自己的研究工作综述了近年来三苯三戊并烯类化合物的合成进展.

Marketing Management for Consumer Products in the Era of the Internet of Things

In the Internet of Things (IoT) consumer products like coffee machines and smoke detectors are connected with the Internet, which effectively expands the Internet to the physical world. Such products have the ability to collect and share data from the user’s environment and, thus, their broad emergence will affect well-established concepts presented in the extant marketing literature. In order to provide a distinct contribution, we focus on customer relationship management, product life cycle management, as well as business model development and discuss implications of the enhanced capabilities of IoT products in these fields. By means of an extensive analysis of current developments in theory and practice, we systematically deduce ten research propositions. The paper concludes with a synthesis of findings and an outlook to promising directions for further research in IoT-oriented marketing management.

Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of LSMOPs.By contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for LSMOPs.Currently,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored.In this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method.On the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front.On the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent.In comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

Regularization by Intrinsic Plasticity and Its Synergies with Recurrence for Random Projection Methods

Neural networks based on high-dimensional random feature generation have become popular under the notions extreme learning machine (ELM) and reservoir computing (RC). We provide an in-depth analysis of such networks with respect to feature selection, model complexity, and regularization. Starting from an ELM, we show how recurrent connections increase the effective complexity leading to reservoir networks. On the contrary, intrinsic plasticity (IP), a biologically inspired, unsupervised learning rule, acts as a task-specific feature regularizer, which tunes the effective model complexity. Combing both mechanisms in the framework of static reservoir computing, we achieve an excellent balance of feature complexity and regularization, which provides an impressive robustness to other model selection parameters like network size, initialization ranges, or the regularization parameter of the output learning. We demonstrate the advantages on several synthetic data as well as on benchmark tasks from the UCI repository providing practical insights how to use high-dimensional random networks for data processing.

Spatio-Temporal Kinematic Decomposition of Movements

We propose the new experimental method for investigating and approximating the organization and structure of movements with given accuracy. The composition of approximating trajectories illuminating the movement traits discloses the level of movement expertise in dancers and golf players. The method allows estimating the level of movement expertise, drawing the detailed structure of movements, and classifying movements into a given repertoire automatically.

MARTINGALE INEQUALITIES UNDER G-EXPECTATION AND THEIR APPLICATIONS

In this paper,we study the martingale inequalities under G-expectation and their applications.To this end,we introduce a new kind of random time,called G-stopping time,and then investigate the properties of a G-martingale(supermartingale)such as the optional sampling theorem and upcrossing inequalities.With the help of these properties,we can show the martingale convergence property under G-expectation.

The Effects of Parental ADHD Symptoms on Parenting Behaviors

Adults suffering from attention-deficit/hyperactivity-disorder (ADHD) often display high levels of inattention, hyperactivity and impulsivity. These symptoms might interfere with skills that are necessary for optimal parenting such as consequent and emotionally responsive behavior towards the child. Therefore, the present review aims at investigating how parental ADHD symptoms influence parenting, thereby including specific parental behaviors of both effective behavior control and emotional responsiveness. In order to identify eligible studies, a systematic search was conducted. Studies were included in this review if at least some of the investigated parents suffered from ADHD or heightened ADHD symptoms, and if the studies focused on specific parenting behaviors as outcome measures. 14 studies yielded the inclusion criteria. Across studies, parental ADHD symptoms were negatively associated with consistent discipline, parental involvement and positive parenting, and positively associated with lax and over-reactive parenting, intrusiveness and negative emotions. The core symptom of inattention had stronger negative effects on parenting than impulsivity and hyperactivity. Across studies, the gender of parents had inconsistent effects. All in all, the present review shows that parental ADHD is associated with serious impairments in parenting. Therefore, parents with ADHD should be specially addressed and trained in the context of children ADHD treatment.

The Relation of Sleep, Distress, and Coping Strategies—What Male and Female Students Can Learn from Each Other?

Sleep quality, distress, and coping strategies differ between male and female students. However, effects of gender on their relation have not been evaluated. Therefore, the primary aim of this study was to confirm gender differences on sleep quality, chronic distress, and various coping strategies, as well as to examine gender differences in their relation to each other. A cross-sectional online study including several sleep-related self-report measures was completed by 6379 German students. After excluding all cases with missing data on the variables gender, psychiatric disorder, and medication, the final sample consisted of 5889 students with a mean age of 23.10 years (SD = 2.67) for men and 22.64 years (SD = 2.56) for women. Data from the Pittsburgh Sleep Quality Index, the Trier Inventory for Chronic Stress, and the Proactive Coping Inventory were analyzed. Results showed that women reported to have a poorer sleep quality, a higher level of chronic distress, and use social support more often than men. The hypothesized model revealed gender differences on the model level. However, these differences only occurred between avoidance coping and distress, as well as between various coping strategies. The biological gender influenced each of those three variables, but barely their relation to each other. Participants’ gender role might explain gender differences in coping strategies and their impact on distress. Furthermore, the type of stressor and subjective or objective measured sleep parameters might show more gender differences on this relation. Conclusively, gender-specific trainings or interventions are not necessary, however, gender differences should be considered during the implementation process.

Simplifying the Analysis of Enzyme Kinetics of Cytochrome <i>c</i>Oxidase by the Lambert-W Function

Conventional analysis of enzyme-catalyzed reactions uses a set of initial rates of product formation or substrate decay at a variety of substrate concentrations. Alternatively to the conventional methods, attempts have been made to use an integrated Michaelis-Menten equation to assess the values of the Michaelis-Menten KM and turnover kcat constants directly from a single time course of an enzymatic reaction. However, because of weak convergence, previous fits of the integrated Michaelis-Menten equation to a single trace of the reaction have no proven records of success. Here we propose a reliable method with fast convergence based on an explicit solution of the Michaelis-Menten equation in terms of the Lambert-W function with transformed variables. Tests of the method with stopped-flow measurements of the catalytic reaction of cytochrome c oxidase, as well as with simulated data, demonstrate applicability of the approach to de termine KM and kcat constants free of any systematic errors. This study indicates that the approach could be an alternative solution for the characterization of enzymatic reactions, saving time, sample and efforts. The single trace method can greatly assist the real time monitoring of enzymatic activity, in particular when a fast control is mandatory. It may be the only alternative when conventional analysis does not apply, e.g. because of limited amount of sample.

Nurses/Midwifery Trainees’Knowledge,Attitudes and Utilisation of HIV Testing/Counselling Service in the Central Region of Ghana

Background and purpose:Despite HIV testing and counselling(HTC)being recognized as important elements of any effective prevention,detection,care,and management programmes across many societies as part of their primary health care package,it is surprising that research evidence on related issues,especially in developing countries like Ghana is sparse.This study examined the extent of knowledge,attitudes and utilisation of HIV testing,and counselling services among trainee nurses of the public nursing and midwifery training colleges in the Central Region of Ghana.Methods and results:A descriptive cross-sectional design was employed to collect data from 375 nursing and midwifery students using multistage sampling procedures.Findings showed that students’HTC knowledge was high(85%)whereas attitudes toward HTC were rated positive(95%).However,HTC utilisation was low(47%).Further results revealed a statistically significant difference between class level and HTC utilisation(χ2[1,N=375]=14.263,p=0.000).In contrast,no statistically significant differences in student nurses’class level and HTC knowledge(χ2[1,N=375]=0.624,p=0.475)as well as class level and attitudes toward HTC services(χ2[1,N=375]=2.334,p=0.158)were realized.Conclusion:The low HTC utilisation among the student nurses may potentially lead to missing opportunities for early diagnoses,care,treatment,and support services for primary,secondary or tertiary prevention modes.Organisation of programmes by college authorities on the importance of HTC and the need for student nurses to utilise these services is crucial.

Infatuation and Lovesickness on Sleep Quality and Dreams in Adolescence

Background: Infatuation and lovesickness are widespread and significant experiences in adolescence. Less is known about the connection between infatuation/lovesickness and sleep. The few studies, examining the link between infatuation and sleep quality show inconsistent results. The link between lovesickness and sleep as well as the link between infatuation/lovesickness and dreams has not been investigated yet. The aim of this study was to examine whether infatuation and lovesickness are linked to sleep quality and dreams in adolescents. Methods: A self-assessment online questionnaire was constructed to assess adolescents’ infatuation, lovesickness, sleep quality and dreams. In total, data of 630 adolescents and young adults (150 males, 480 females;aged 16 - 21) were analyzed in this study. Results: Infatuation did not relate to overall sleep quality and dreams. Sleep disturbances, as a component of overall sleep quality, were more frequent in infatuated adolescents. Adolescents currently suffering from lovesickness reported a significantly lower sleep quality, more negative dreams and nightmares. Furthermore, nightmares influenced them more strongly the next day. Conclusions: The associations between infatuation/lovesickness and sleep provide evidence for the far reaching effects of infatuation and lovesickness in adolescents’ lives. The fact that lovesickness leads to lower sleep quality and more negative dreams should be integrated in new approaches of insomnia treatment.

A survey on computationally efficient neural architecture search

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally intensive.To solve this major limitation of NAS,improving the computational efficiency is essential in the design of NAS.However,a systematic overview of computationally efficient NAS(CE-NAS)methods still lacks.To fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities.The remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

Isolation, purification and structure elucidation of three newbioactive secondary metabolites from Streptomyces lividans AM

Microorganisms are a huge mine of bioactive metabolites,and actinomycetes are one of the very active groups in this area.In this article,we are concerned about the full taxonomical characterization of Streptomyces lividans AM,isolated from Egyptian soil.This isolate produced three new bioactive metabolites,namely:1-Nona-decanoyl,4-oleyl disuccinate(1),filoboletic acid;(9Z,11E)-8,13-dihydroxy octadeca-9,11-dienoic acid(2),and sitosteryl-3β-D-glucoside(3).Extensive 1D and 2D NMR and HR-mass spectrometry were used to elucidate the structures of the three compounds.Moreover,ten known compounds were also identified.The antimicrobial activity of the producing organism and newly reported compounds(1–3)was investigated against a selected group of pathogenic microorganisms.A full taxonomical characterization of the strain was described as well.