Child court hearing in family cases: Questionnaire to assess the child needs during the juddes exploration

The basis of family law is the child's interest. This is related to the right to be listened to, but not as an obligation. As a consequence, there is a necessity for the judge to conduct a judicial exploration of the child. But, in general, the judges are not trained in this type of explorations, and they may consequently obtain erroneous information in their exploration. Therefore, in this work, we present the generation of a questionnaire that explores the judicial agents' necessities during judicial exploration of children. Five expert researchers in the subject participated in creating the questionnaire; five family judges participated in the pilot test; and in the final study, 63 family judges answered the final questionnaire. Global reliability was adequate (.858), as was the reliability for interviewer's skills, but it was not for the other areas of the questionnaire. An exploratory factor analysis showed a factor structure consisting of 5 factors that accounted for 46.12% of the total variance, but these five factors don't correspond to the factors provided by experts. But construct validity validated the structure provided by the experts (X^2 /df = 1.35; BBNNFI = .873; CFI = .879; IFI = .881; RMR = .139; SRMR = .153; RMSEA = .075). To sum up, we can say that the questionnaire could be improved, but the best areas are the stages of the interview and the interviewer's skills

Social Capital, Network Governance and Social Innovation: Towards a New Paradigm?

Limited knowledge and empirical evidence exist so far on how governance is related to social capital, and to comprehensively evaluate the effects of collaborative public-private partnerships in rural development actions, and whether these elements foster socially innovative actions. The book chapter begins to address these knowledge gaps. It highlights the conceptual framework linking social capital and network governance and identifies specific approaches to analysing governance. Moreover, it conceptually identifies the key elements for assessing governance mechanisms in the LEADER approach and explains its adoption in the evaluation method proposed in the book. The chapter concludes by outlining how social capital and governance may support social innovation, a topic which is developed more comprehensively in relation to LEADER's specific contribution in the final chapter of the same book

Mobility and Migrations in the Rural Areas of Mediterranean EU Countries

AbstractThis chapter focuses on the ambivalent nature of contemporary migrations in European rural areas. The growing presence of immigrants in these areas is a direct result of the restructuring of agriculture and global agri-food chains. Evidence indicates that while agricultural work and rural settings are decreasingly attractive to local populations, they represent a favourable environment to international newcomers, due to the higher chances to access livelihood resources. The non-visibility and informality that characterise rural settings and agricultural work arrangements provide on the one side opportunities for employment, while also fostering illegal labour practices and situations of harsh exploitation

In Search of Optimal Linkage Trees

Linkage-learning Evolutionary Algorithms (EAs) use linkage learning to construct a linkage model, which is exploited to solve problems efficiently by taking into account important linkages, i.e. dependencies between problem variables, during variation. It has been shown that when this linkage model is aligned correctly with the structure of the problem, these EAs are capable of solving problems efficiently by performing variation based on this linkage model [2]. The Linkage Tree Genetic Algorithm (LTGA) uses a Linkage Tree (LT) as a linkage model to identify the problem's structure hierarchically, enabling it to solve various problems very efficiently. Understanding the reasons for LTGA's excellent performance is highly valuable as LTGA is also able to efficiently solve problems for which a tree-like linkage model seems inappropriate. This brings us to ask what in fact makes a linkage model ideal for LTGA to be used

A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables

We propose a novel clustering-based model-building evolutionary algorithm to tackle optimization problems that have both binary and real-valued variables. The search space is clustered every generation using a distance metric that considers binary and real-valued variables jointly in order to capture and exploit dependencies between variables of different types. After clustering, linkage learning takes place within each cluster to capture and exploit dependencies between variables of the same type. We compare this with a model-building approach that only considers dependencies between variables of the same type. Additionally, since many real-world problems have constraints, we examine the use of different well-known approaches to handling constraints: constraint domination, dynamic penalty and global competitive ranking. We experimentally analyze the performance of the proposed algorithms on various unconstrained problems as well as a selection of well-known MINLP benchmark problems that all have constraints, and compare our results with the Mixed-Integer Evolution Strategy (MIES). We find that our approach to clustering that is aimed at the processing of dependencies between binary and real-valued variables can significantly improve performance in terms of required population size and function evaluations when solving problems that exhibit properties such as multiple optima, strong mixed dependencies and constraints

Ammonium Vanadium Bronze (NH4V4O10) as a High-Capacity Cathode Material for Nonaqueous Magnesium-Ion Batteries

Magnesium-ion batteries (MIBs) offer improved safety, lower cost, and higher energy capacity. However, lack of cathode materials with considerable capacities in conventional nonaqueous electrolyte at ambient temperature is one of the great challenges for their practical applications. Here, we present high magnesium-ion storage performance and evidence for the electrochemical magnesiation of ammonium vanadium bronze NH4V4O10 as a cathode material for MIBs. NH4V4O10 was synthesized via a conventional hydrothermal reaction. It shows reversible magnesiation with an initial discharge capacity of 174.8 mAh g-1 and the average discharge voltage of ∼2.31 V (vs Mg/Mg2+) using 0.5 M Mg(ClO4)2 in acetonitrile as the electrolyte. Cyclic voltammetry, galvanostatic, discharge-charge, FTIR, XPS, powder XRD, and elemental analyses unequivocally show evidence for the reversible magnesiation of the material and suggest that keeping the ammonium ions in the interlayer space of NH4V4O10 could be crucial for the structural stability with a sacrifice of initial capacity but much enhanced retention capacity. This is the first demonstration of electrochemical magnesiation with a high capacity above 2 V (vs Mg/Mg2+) using a conventional organic electrolyte with a relatively low water concentration. © 2018 American Chemical Society.1