Exploring Lebanese Teachers’ Engagement in a Low-Cost, Technology-Enhanced, Problem-Solving, Orientated Learning Intervention with Refugee Children

The research explored what learning was fostered when 41 Lebanese teachers from 21 schools engaged in a low-cost, problem-oriented, technology-enhanced situated learning intervention with refugee children to understand teacher agency in practice within challenging contexts. The research is at the intersection of pedagogy, technology, curriculum, and teacher professional development, and explored a situated teacher professional development (TPD) intervention in the context of refugee education. The low-cost technologies utilised in the intervention share similarities with those used in maker-spaces and are taught within a pedagogical process rooted in knowledge-building in the real world. The research is informed by complexity thinking, socioconstructivist, and interpretivist epistemologies and underpinned by a conceptual framework drawing on Mezirow’s transformative theory and Habermas’s (1985) communicative action around teachers’ lifeworld (subjective, objective, and social) as a shared experience. Conversations around teachers’ lifeworld draw on Laurillard’s conversational framework to help teachers design projects through experiential and discursive conversations. Qualitative data was collected through interviews and observations which were analysed thematically using Pachler et al.’s ecological sociocultural framework. The research adopted an understanding of agency as situated, temporal, and rooted in teachers’ past, present, and projected future experiences, drawing on Gidden’s structuration theory. Teacher agency in practice appeared as ecological, temporal, complex, and intertwined in dialectic relationships around emergent knowledge-building pedagogy, between collective and individual dimensions in the situated intervention, and in actions driven by moral values, in school and in the community. The research revealed that situated transformative TPD models can be used even in challenging, post-conflict contexts and may contribute to generating contextually relevant solutions

Learning for influence methodology

The Learning for Influence methodology uses narratives and storytelling within a complexity framing to generate and capture experiences that may be missed by other monitoring, evaluation, and learning (MEL) approaches. Learning for Influence can complement other MEL approaches, such as outcome mapping, outcome harvesting, Most Significant Change, and impact stories. The methodology focuses on four dimensions that shape how influencing policy and practice happens: context, organizational approach(es), examples from practice, and stories of impact. It also aims to generate multiple perspectives within an organization and can also be used for collaborative learning across a network of organizations working together. Therefore, the methodology should involve a variety of individuals within organizations working to influence policy and practice

Actively Learning Costly Reward Functions for Reinforcement Learning

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to several hours for standard benchmark tasks. However, while rewards in simulated environments are well-defined and easy to compute, reward evaluation becomes the bottleneck in many real-world environments, e.g., in molecular optimization tasks, where computationally demanding simulations or even experiments are required to evaluate states and to quantify rewards. Therefore, training might become prohibitively expensive without an extensive amount of computational resources and time. We propose to alleviate this problem by replacing costly ground-truth rewards with rewards modeled by neural networks, counteracting non-stationarity of state and reward distributions during training with an active learning component. We demonstrate that using our proposed ACRL method (Actively learning Costly rewards for Reinforcement Learning), it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions to real-world optimization problems in chemistry, materials science and engineering

Modeling the human knee joint using the Proper Generalized Decomposition

Nowadays, human joints specifically movable are active research topics. The lack of effective replacements and the inefficient natural healing of these joints hinders any athlete from pursuing his career if injured in his joints. Therefore, researchers are testing innovative soft materials and biphasic materi- als as replacements of human joints. However, the lack of effective mechanical modeling is slowing the development of new replacements. In this work, we tackle the mechanical modeling of the synovial joint in a human knee. The tibiofemoral joint is modelled during impact. This joint is basically made of a cartilage, a meniscus (both a biphasic material) and the synovial fluid. The modeling is performed using Brinkman equation. However, the rich physics in- volved in the thickness direction requires a large number of degrees of freedom in the mesh to represent the physical phenomenon taking place in a knee joint. Thus, the use of model order reduction techniques appears to be an appealing approach in this situation. In fact, the proper generalized decomposition re- duced the number of degrees of freedom by using domain decomposition. The result of this work shows the pressure and fluid flow in the synovial joint under impact. A post treatment of the solution estimates the force held by each of the fluid and solid components of the cartilage joint. This model could be used to the human knee to estimate its components’ velocities and pressure fields while performing an activity

Regio- and stereo-selectivity in the intramolecular quenching of the excited benzoylthiophene chromophore by tryptophan

Laser flash photolysis studies on the photobehaviour of a series of bichromophoric derivatives bearing benzoylthiophene and tryptophan groups have shown that the efficiency of the intramolecular quenching process depends on both the stereochemistry of the chiral centers and the relative ketone versus tryptophan orientation.Perez Prieto, Julia, [email protected]

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs

Neurological Tremor: Sensors, Signal Processing and Emerging Applications

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions

Population growth of Mexican free-tailed bats (Tadarida brasiliensis mexicana) predates human agricultural activity

Background Human activities, such as agriculture, hunting, and habitat modification, exert a significant effect on native species. Although many species have suffered population declines, increased population fragmentation, or even extinction in connection with these human impacts, others seem to have benefitted from human modification of their habitat. Here we examine whether population growth in an insectivorous bat (Tadarida brasiliensis mexicana) can be attributed to the widespread expansion of agriculture in North America following European settlement. Colonies of T. b. mexicana are extremely large (~106 individuals) and, in the modern era, major agricultural insect pests form an important component of their food resource. It is thus hypothesized that the growth of these insectivorous bat populations was coupled to the expansion of agricultural land use in North America over the last few centuries. Results We sequenced one haploid and one autosomal locus to determine the rate and time of onset of population growth in T. b. mexicana. Using an approximate Maximum Likelihood method, we have determined that T. b. mexicana populations began to grow ~220 kya from a relatively small ancestral effective population size before reaching the large effective population size observed today. Conclusions Our analyses reject the hypothesis that T. b. mexicana populations grew in connection with the expansion of human agriculture in North America, and instead suggest that this growth commenced long before the arrival of humans. As T. brasiliensis is a subtropical species, we hypothesize that the observed signals of population growth may instead reflect range expansions of ancestral bat populations from southern glacial refugia during the tail end of the Pleistocene