Mediation and the Best Interests of the Child from the Child Law Perspective

What is the best interests of the child in family mediation and is mediation in the best interests of the child? In this article, I use child law and the United Nations Convention on the Rights of the Child combined with mediation theory to discuss these questions. Both mediation and the best interests of the child are open for multiple interpretations. Using facilitative and evaluative mediation theory and the legal concept ‘the best interests of the child’, I explore and compare the understandings of these concepts as they apply to family mediation. This includes a discussion of the advantages and disadvantages of facilitative as well as evaluative mediation orientations in terms of protecting the best interests of the child. Finnish court-connected family mediation is a combination of both mediation orientations, and the mediator is obliged to secure the best interests of the child. From a theoretical point of view, this seems to be a challenging combination.Peer reviewe

Agriculture in the Trans-Pacific Partnership

The Trans-Pacific Partnership is a trade and investment agreement under negotiation by 12 countries in the Pacific Rim. This report assesses this partnership's potential impacts on the region's agriculture in 2025

Cost model for a 5G smart light pole network

The adoption of 5G smart light poles can facilitate the massive deployment of communications equipment in the urban environment, accelerating the advent of new smart city services. In this article, we define a cost model for a 5G smart light pole network that includes (1) four pole configurations with different hardware components, (2) a grid-based deployment structure that assigns poles to zones with different demand requirements, and (3) the evolution of key cost items due to prototype improvement, volume sale discounts, and price erosion. The model estimates the total deployment cost (TDC), including capital and operational expenses. We estimate a TDC of 4.84 M€/km 2 for a minimum deployment providing uniform coverage of basic services. We also estimate a TDC of 6.57 M€/km 2 for a massive deployment providing heterogeneous coverage of advanced services. These values can potentially decrease to 3.23 M€/km 2 and 4.05M€/km 2 when cost evolution is considered. Although more than 30 % cost reduction might be possible, this is mainly caused by the improvement of prototype components, given that public works are less sensible to cost evolution. Therefore, we recommend cities to promptly start civil works and to select upgrade-able pole designs.Peer reviewe

Modelling vegetation understory cover using LiDAR metrics.

Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches