Worldwide, actors hold high hopes about the use of technologies in education. Evaluat- ing literature on different aspects related to the use of technologies in education, this paper aims at bringing together the dispersed knowledge in this field. It addresses the question: What is the potential of information and communication technologies (ICTs) to improve education in low and middle income countries (LMICs)? Based on findings from past and current ICT projects in education in LMICs, this paper determines five fac- tors that seem to be decisive for the success of such initiatives: People, technology, ped- agogy, financial cost, and organization. Focusing on primary and secondary education, the author argues that considering the elaborated aspects, ICTs can contribute to ad- vance towards the internationally declared education targets by improving access to ed- ucation, inclusion, and gender equality as well as quality. Furthermore, experiences on different ICT-initiatives indicate that in some LMIC-contexts, traditional technologies are more promising than recent ones.
In 1963, Anton Kotzig famously conjectured that Kn, the complete graph of order n, where n is even, can be decomposed into n-1 perfect matchings such that every pair of these matchings forms a Hamilton cycle. The problem is still wide open and here we consider a variant of it for the binomial random graph G(n, p). We prove that, for every fixed k, there exists a constant C = C(k) such that, when p >= Clog n/ n , with high probability, G(n, p) contains k edge-disjoint perfect matchings with the property that every pair of them forms a Hamilton cycle. In fact, our main result is a very precise counting result for K-n. We show that, given any kedge-disjoint perfect matchings M-1, ... , M-k, the probability that a uniformly random perfect matching M-& lowast; in K-n has the property that M & lowast; U Mi forms a Hamilton cycle for each i is an element of [k] is Theta(k)(n(-k/2)). This is proved by building on a variety of methods, including a random process analysis, the absorption method, the entropy method and the switching method. The result on the binomial random graph follows from a slight strengthening of our counting result via the recent breakthroughs on the expectation threshold conjecture.
This paper presents a new column-and-constraint generation method for two-stage robust mixed-integer programs with finite uncertainty sets. Our method combines and extends speed-up techniques used in previous column-and-constraint generation methods and introduces several new techniques. In particular, it uses dual bounds for second-stage problems in order to allow a faster identification of the next promising scenario to be added to the master problem. Moreover, adaptive time limits are imposed to avoid getting stuck on particularly hard second-stage problems, and a gap propagation between master problem and second-stage problems is used to stop solving them earlier if only a given non-zero optimality gap is to be reached overall. This makes our method particularly effective for problems where solving the second-stage problem is computationally challenging. To evaluate the method's performance, we compare it to two recent column-and-constraint generation methods from the literature on two applications: a robust capacitated location routing problem and a robust integrated berth allocation and quay crane assignment and scheduling problem. The first problem features a particularly hard second stage, and we show that our method is able to solve considerably more and larger instances in a given time limit. Using the second problem, we verify the general applicability of our method, even for problems where the second stage is relatively easy.
While ecosystem recovery is widely recognized to vary, valuations of ecosystem services often assume guaranteed outcomes. We examine whether omitting information on restoration failure risk causes bias in the utility estimates of ecosystem services. Further, we examine how two personal attitudes (connectedness to nature and place attachment) influence risk perception. In comparison to previous studies, we integrate both risk (known outcomes and their probabilities of ecosystem service provision following grassland restoration) and uncertainty (unknown outcomes and probabilities) within a single framework. We find that omitting information on risk leads to overestimation of the utility derived from ecosystem services. Controlling for subjective uncertainty and risk aversion using a hybrid choice model, we find no influence of connectedness to nature on risk perception, whereas the negative effect of high restoration failure risk on utility becomes weaker when place attachment is high. Both latent attitudes decrease the utility of uncertainty, i.e., of the opt-out alternative no restoration. Given the substantial utility implications of restoration failure risk, the EU should complement Member State-level monitoring under the Nature Restoration Regulation with project-level monitoring, open data sharing, and transparent reporting of restoration outcomes to the public.
We study the complexity of approximating the finite-dimensional vector space embedding ℓpm↪ℓqm for 2≤p<q≤∞ based on non-adaptive randomized algorithms that use up to n arbitrary linear functionals as information on a problem instance x∈Rm, where n≪m. We prove lower bounds on the non-adaptive randomized approximation error with a joint dependence on (n,m) matching previously known upper bounds.
