LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

浅谈农业科研档案管理存在的问题及建议

基于当前农业科研档案管理的概况,其中存在着一些问题和不足。通过分析这些问题,进而提出针对性的解决对策和建议,进一步提高农业科研档案管理效率,为各项工作开展提供更好的保障。     

Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection

In a deregulated multi-area electrical power system the objective is to determine the most economical generation dispatch strategy that could satisfy the area load demands, the tie-line limits and other operating constraints. Usually, economic dispatch (ED) deals only with the cost minimization, but minimization of emission content has also become an equally important concern due to the mandatory requirement of pollution reduction for environmental protection. Environmental economic dispatch (EED) is a complex multi-objective optimization (MOO) problem with conflicting goals. Normally a fuzzy ranking is employed to rank the large number of Pareto solutions obtained after solving a MOO problem. But in this paper the preference of the decision maker (DM) is used to guide the search and to select the population for the next generation. An improved differential evolution (DE) method is proposed where the selection operation is modified to reduce the complexity of multi-attribute decision making with the help of a fuzzy framework. Solutions are assigned a fuzzy rank on the basis of their level of satisfaction for different objectives before the population selection and then the fuzzy rank is used to select and pass on better solutions to the next generation. A well distributed Pareto-front is obtained which presents a large number of alternate trade-off solutions for the power system operator. A momentum operation is also included to prevent stagnation and to create Pareto diversity. Studies are carried out on three test cases and results obtained are found to be better than some previous literature.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

A Dijkstra-like method computing all extreme supported non-dominated solutions of the biobjective shortest path problem

We address the problem of determining all extreme supported solutions of the biobjective shortest path problem. A novel Dijkstra-like method generalizing Dijkstra׳s algorithm to this biobjective case is proposed. The algorithm runs in O(N(m+n log n)) time to solve one-to-one and one-to-all biobjective shortest path problems determining all extreme supported non-dominated points in the outcome space and one supported efficient path associated with each one of them. Here n is the number of nodes, m is the number of arcs and N is the number of extreme supported points in outcome space for the one-to-all biobjective shortest path problem. The memory space required by the algorithm is O(n+m) for the one-to-one problem and O(N+m) for the one-to-all problem. A computational experiment comparing the performance of the proposed methods and state-of-the-art methods is included.     

Neurological and Epigenetic Implications of Nutritional Deficiencies on Psychopathology: Conceptualization and Review of Evidence

In recent years, a role for epigenetic modifications in the pathophysiology of disease has received significant attention. Many studies are now beginning to explore the gene–environment interactions, which may mediate early-life exposure to risk factors, such as nutritional deficiencies and later development of behavioral problems in children and adults. In this paper, we review the current literature on the role of epigenetics in the development of psychopathology, with a specific focus on the potential for epigenetic modifications to link nutrition and brain development. We propose a conceptual framework whereby epigenetic modifications (e.g., DNA methylation) mediate the link between micro- and macro-nutrient deficiency early in life and brain dysfunction (e.g., structural aberration, neurotransmitter perturbation), which has been linked to development of behavior problems later on in life.     

Does cyberbullying overlap with school bullying when taking modality of involvement into account?

Education professionals and researchers are concerned by school bullying and cyberbullying because of its repercussions on students’ health and the school climate. However, only a few studies investigating the impact of school versus cyberbullying have systematically explored whether student victims and perpetrators are involved in school bullying only, cyberbullying only, or both. The aim of the present study was thus to examine the possible overlap, as well as the similarities and/or differences, between these two forms of bullying when taking modality of involvement into account. Individual interviews were conducted with 1422 junior high- and high-school students (girls = 43%, boys = 57%, mean age = 14.3 ± 2.7 years). Results showed that cyberbullying and school bullying overlapped very little. The majority of students involved in cyberbullying were not simultaneously involved in school bullying. Moreover, results indicated that psychosocial problems (psychological distress, social disintegration, general aggression) varied according to the form of bullying. Victims of school bullying had greater internalizing problems than cybervictims, while school bullies were more aggressive than cyberbullies. Given the sizable proportion of adolescents involved in bullying (school and cyber) and its significant relationship with mental health, the issue warrants serious attention from school and public health authorities.     

电厂汽轮机组振动问题分析与解决对策

电厂中的汽轮发电机组在其中所占据的地位极为关键重要。在日常运行中,汽轮机组系统或多或少的存在一定程度的振动问题,此振动问题在一定参数值范围内也是能够允许的。但如果振动问题一旦超过其所允许的范围,则会对整个汽轮机组的工作及电厂的顺利运行带来极为不利的影响。文章首先对电厂汽轮机组振动问题的特点进行了阐述说明,之后结合实例着重对其发生原因及相关的解决对策进行了全面化的分析研究。     

Exploring design trade-offs for achieving social inclusion in multi-tiered design problems

ABSTRACT

The digital age of the future is ‘not out there to be discovered’, but it needs to be ‘designed’. The design challenge has to address questions about how we want to live, work, and learn (as individuals and as communities) and what we value and appreciate, e.g.: reflecting on quality of life and creating inclusive societies. An overriding design trade-off for the digital age is whether new developments will increase the digital divide or will create more inclusive societies. Sustaining inclusive societies means allowing people of all ages and all abilities to exploit information technologies for personally meaningful activities. Meta-design fosters the design of socio-technical environments that end-user developers can modify and evolve at use time to improve their quality of life and favour their inclusion in the society. This paper describes three case studies in the domain of assistive technologies in which end users themselves cannot act as end-user developers, but someone else (e.g.: a caregiver or a clinician) must accept this role requiring multi-tiered architectures. The design trade-offs and requirements for meta-design identified in the context of the case studies and other researchers’ projects are described to inform the development of future socio-technical environments focused on social inclusion.