Rapamycin reverses age-related increases in mitochondrial ROS production at complex I, oxidative stress, accumulation of mtDNA fragments inside nuclear DNA, and lipofuscin level, and increases autophagy, in the liver of middle-aged mice

Rapamycin consistently increases longevity in mice although the mechanism of action of this drug is unknown. In the present investigation we studied the effect of rapamycin on mitochondrial oxidative stress at the same dose that is known to increase longevity in mice (14 mg of rapamycin/kg of diet). Middle aged mice (16 months old) showed significant age-related increases in mitochondrial ROS production at complex I, accumulation of mtDNA fragments inside nuclear DNA, mitochondrial protein lipoxidation, and lipofuscin accumulation compared to young animals (4 months old) in the liver. After 7 weeks of dietary treatment all those increases were totally or partially (lipofuscin) abolished by rapamycin, middle aged rapamycin-treated animals showing similar levels in those parameters to young animals. The decrease in mitochondrial ROS production was due to qualitative instead of quantitative changes in complex I. The decrease in mitochondrial protein lipoxidation was not due to decreases in the amount of highly oxidizable unsaturated fatty acids. Rapamycin also decreased the amount of RAPTOR (of mTOR complex) and increased the amounts of the PGC1-α and ATG13 proteins. The results are consistent with the possibility that rapamycin increases longevity in mice at least in part by lowering mitochondrial ROS production and increasing autophagy, decreasing the derived final forms of damage accumulated with age which are responsible for increased longevity. The decrease in lipofuscin accumulation induced by rapamycin adds to previous information suggesting that the increase in longevity induced by this drug can be due to a decrease in the rate of aging. © 2016 Elsevier Inc.

Advances in integration of photovoltaic power and energy production in practical systems

This thesis presents advances in integration of photovoltaic (PV) power and energy in practical systems, such as existing power plants in buildings or directly integrated in the public electrical grid. It starts by providing an analyze of the current state of PV power and some of its limitations. The work done in this thesis begins by providing a model to compute mutual shading in large PV plants, and after provides a study of the integration of a PV plant in a biogas power plant. The remainder sections focus on the work done for project PVCROPS, which consisted on the construction and operation of two prototypes composed of a PV system and a novel battery connected to a building and to the public electrical grid. These prototypes were then used to test energy management strategies and validate the suitability of the two advanced batteries (a lithium-ion battery and a vanadium redox ow battery) for households (BIPV) and PV plants. This thesis is divided in 7 chapters: Chapter 1 provides an introduction to explain and develop the main research questions studied for this thesis; Chapter 2 presents the development of a ray-tracing model to compute shading in large PV elds (with or without trackers); Chapter 3 shows the simulation of hybridizing a biogas plant with a PV plant, using biogas as energy storage; Chapters 4 and 5 present the construction, programming, and initial operation of both prototypes (Chapter 4), EMS testing oriented to BIPV systems (Chapter 5). Finally, Chapters 6 provides some future lines of investigation that can follow this thesis, and Chapter 7 shows a synopsis of the main conclusions of this work; Resumo: Avanços na integracão de potência fotovoltaica e producão de energia em sistemas práticos Esta tese apresenta avanços na integração de potência e energia fotovoltaica (PV) em sistemas práticos, tais como centrais existentes ou a rede eléctrica pública. Come ça por analisar o estado corrente do fotovoltaico no mundo e aborda algumas das suas limitações. O trabalho feito para esta tese de doutoramento começou pelo desenvolvimento de um modelo para calcular os sombreamentos que ocorrem em grandes campos fotovoltaicos, e depois apresenta um estudo sobre a integração um sistema fotovoltaico em uma central eléctrica a bióg as. As ultimas secções da tese focam-se no trabalho feito para o projecto PVCROPS, que consistiu na construção e operação de dois demonstratores, cada um formado por um sistema fotovoltaico e bateria conectados a um edíficio e a rede eléctrica pública. Estes protótipos foram posteriormente utilizados para testar estratégias de gestão de energia (EMS) e para validar a operação de duas baterias avançadas (bateria de Iões de Li tio e bateria de Fluxo Redox de Van adio) e a sua utiliza ção para habitações e centrais PV. A tese está dividida em 7 capitulos: O capitulo 1 apresenta uma introdução para explicar e desenvolver as principais questões que foram investigadas nesta tese; O capitulo 2 mostra o desenvolvimento de um modelo baseado em traçados de raios para calcular sombreamentos mútuos em grandes centrais PV (com e sem seguidores); O capitulo 3 mostra a simulação da hibridização de uma central electrica a biogas com uma central PV, e utilizando o biógas como armazenamento de energia. Os capitulos 4 e 5 apresentam a construção, programação e operação inicial dos dois demonstradores (Capitúlo 4), o teste de EMS orientadas para sistemas PV em habitações (Capítulo 5). Finalmente, o capítulo 6 sugere algumas futuras linhas de investigação que poderão seguir esta tese, e o Capítulo 7 faz uma sinopse das principais conclusões deste trabalho.

The Students’ Perspective of Geometry Teaching and Learning in High School

The purpose of this article is to present the results obtained from a questionnaire applied to Costa Rican high school students, in order to know their perspectives about geometry teaching and learning. The results show that geometry classes in high school education have been based on a traditional system of teaching, where the teacher presents the theory; he presents examples and exercises that should be solved by students, which emphasize in the application and memorization of formulas. As a consequence, visualization processes, argumentation and justification don’t have a preponderant role. Geometry is presented to students like a group of definitions, formulas, and theorems completely far from their reality and, where the examples and exercises don’t possess any relationship with their context. As a result, it is considered not important, because it is not applicable to real life situations. Also, the students consider that, to be successful in geometry, it is necessary to know how to use the calculator, to carry out calculations, to have capacity to memorize definitions, formulas and theorems, to possess capacity to understand the geometric drawings and to carry out clever exercises to develop a practical ability.

Fertilizers with coated urea in upland rice production and nitrogen apparent recovery.

Urea is the most used N fertilizer for upland rice, however, a great percentage of N loss can occur with the use of this fertilizer. The use of products that provide reduction of N loss for urea fertilizers can contribute to increase N use efficiency. The objective of this study was to determine the effect of N rates applied in the form of coated urea in the content and accumulation of N in dry biomass, apparent recovery of nitrogen and grain yield of upland rice. The experimental design was a randomized complete blocks arranged in a 4 x 3 + 1 factorial scheme. The treatments consisted of four sources of N fertilizer [1. Common urea; 2. Polymer-coated urea for slow release of N (PCU); 3. urea with the urease inhibitor N-(n-Butyl) thiophosphoric triamide (NBPT); and 4. urea coated with copper sulfate and boric acid as urease inhibitors (UCCB)], with three fertilization rates (30, 60 and 90 kg ha-1 of N). In addition, we included a control treatment without N application. Coated urea did not provide increases in rice grain yield in relation to common urea. The increasing amount of N resulted in significant increases in rice grain yield (from 3217 to 5548 kg ha-1, 2010/11, and from 3392 to 4560 kg ha-1, 2011/12). The apparent nitrogen recovery rate decreased with the increase in N applied doses.

Comparison of Explicit and Implicit Methods of Cross-Cultural Learning in an International Classroom

The paper addresses a gap in the literature concerning the difference between enhanced and not enhanced cross-cultural learning in an international classroom. The objective of the described research was to clarify if the environment of international classrooms could enhance cross-cultural competences significantly enough or if additional focus on cross-cultural learning as an explicit objective of learning activities would add substantially to the experience. The research question was defined as “how can a specific exercise focused on cross-cultural learning enhance the cross-cultural skills of university students in an international classroom?”. Surveys were conducted among interna- tional students in three leading Central-European Universities in Lithuania, Poland and Hungary to measure the increase of their cross-cultural competences. The Lithuanian and Polish classes were composed of international students and concentrated on International Management/Business topics (explicit method). The Hungarian survey was done in a general business class that just happened to be international in its composition (implicit method). Overall, our findings prove that the implicit method resulted in comparable, somewhat even stronger effectiveness than the explicit method. The study method included the analyses of students’ individual increases in each study dimension and construction of a compound measure to note the overall results. Our findings confirm the power of the international classroom as a stimulating environment for latent cross-cultural learning even without specific exercises focused on cross-cultural learning itself. However, the specific exercise did induce additional learning, especially related to cross-cultural awareness and communication with representatives of other cultures, even though the extent of that learning may be interpreted as underwhelming. The main conclusion from the study is that the diversity of the students engaged in a project provided an environment that supported cross-cultural learning, even without specific culture-focused reflections or exercises.

Perceptions of online tutorials for distance learning in mathematics and computing

We report on student and staff perceptions of synchronous online teaching and learning sessions in mathematics and computing. The study is based on two surveys of students and tutors conducted 5 years apart, and focusses on the educational experience as well as societal and accessibility dimensions. Key conclusions are that both staff and students value online sessions, to supplement face-to-face sessions, mainly for their convenience, but interaction within the sessions is limited. Students find the recording of sessions particularly helpful in their studies.

Nurturing global collaboration and networked learning in higher education

We consider the principles of communities of practice (CoP) and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the exploration and evaluation of mobile web tools to engage in participatory curriculum development and supporting students in developing international collaboration and cooperation skills. This article explores the interplay of collaboration and cooperation, CoP and networked learning; describes how this interplay has operated in iCollab; and highlights opportunities and challenges of learning, teaching and interacting with students in networked publics in higher education.

Responding to ‘nurturing global collaboration and networked learning in higher education’

Letter to the Editor No abstract available

A review of predictive factors of student success in and satisfaction with online learning

Students perceive online courses differently than traditional courses. Negative perceptions can lead to unfavourable learning outcomes including decreased motivation and persistence. Throughout this review, a broad range of factors that affect performance and satisfaction within the online learning environment for adult learners will be examined including learning outcomes, instructional design and learner characteristics, followed by suggestions for further research, and concluding with implications for online learning pertinent to administrators, instructors, course designers and students. Online learning may not be appropriate for every student. Identifying particular characteristics that contribute to online success versus failure may aid in predicting possible learning outcomes and save students from enrolling in online courses if this type of learning environment is not appropriate for them. Furthermore, knowing these learner attributes may assist faculty in designing quality online courses to meet students’ needs. Adequate instructional methods, support, course structure and design can facilitate student performance and satisfaction.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Net greenhouse gas fluxes in Brazilian ethanol production systems

Biofuels are both a promising solution to global warming mitigation and a potential contributor to the problem. Several life cycle assessments of bioethanol have been conducted to address these questions. We performed a synthesis of the available data on Brazilian ethanol production focusing on greenhouse gas (GHG) emissions and carbon (C) sinks in the agricultural and industrial phases. Emissions of carbon dioxide (CO(2)) from fossil fuels, methane (CH(4)) and nitrous oxide (N(2)O) from sources commonly included in C footprints, such as fossil fuel usage, biomass burning, nitrogen fertilizer application, liming and litter decomposition were accounted for. In addition, black carbon (BC) emissions from burning biomass and soil C sequestration were included in the balance. Most of the annual emissions per hectare are in the agricultural phase, both in the burned system (2209 out of a total of 2398 kg C(eq)), and in the unburned system (559 out of 748 kg C(eq)). Although nitrogen fertilizer emissions are large, 111 kg C(eq) ha-1 yr-1, the largest single source of emissions is biomass burning in the manual harvest system, with a large amount of both GHG (196 kg C(eq) ha-1 yr-1). and BC (1536 kg C(eq) ha-1 yr-1). Besides avoiding emissions from biomass burning, harvesting sugarcane mechanically without burning tends to increase soil C stocks, providing a C sink of 1500 kg C ha-1 yr-1 in the 30 cm layer. The data show a C output: input ratio of 1.4 for ethanol produced under the conventionally burned and manual harvest compared with 6.5 for the mechanized harvest without burning, signifying the importance of conservation agricultural systems in bioethanol feedstock production.