Resíduos Plásticos: capacitando a comunidade sobre reciclagem

Anais do 35º Seminário de Extensão Universitária da Região Sul - Área temática: Meio AmbienteA reciclagem, se conduzida de forma correta, traz inúmeros benefícios ambientais, sociais e econômicos. Nesse aspecto foi desenvolvido um projeto de pesquisa pelo LAPAM para a reciclagem de resíduos plásticos. Este projeto contou com atividades de extensão de forma a capacitar selecionadores de materiais recicláveis, comunidade acadêmica e setor empresarial através de treinamento teórico-prático e workshop. As capacitações permitiram o intercâmbio de experiências e conhecimentos, resultando na boa aceitação do público-alv

Fortschritte in der Biozuckerrübenproduktion

Nach dem schwierigen Start 2001 ist es im Jahr 2002 dem biologischen Zuckerrübenanbau wesentlich besser ergangen. Dies bestätigen die im Rahmen eines FiBL-Projektes erhobenen Informationen auf 21 Biobetrieben. In diesem Jahr werden Versuche im Bereich Anbautechnik und Unkrautregulierung angelegt, mit dem Ziel, den Handarbeitsaufwand weiter zu senken

Hyde: The First Open-Source, Python-Based, Gpu-Accelerated Hyperspectral Denoising Package

As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image denoising toolbox, which aims to provide a large set of methods with an easy-to-use environment. HyDe includes a variety of methods ranging from low-rank wavelet-based methods to deep neural network (DNN) models. HyDe's interface dramatically improves the interoperability of these methods and the performance of the underlying functions. In fact, these methods maintain similar HSI denoising performance to their original implementations while consuming nearly ten times less energy. Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i.e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne. To utilize the trained DNNs, we show a sliding window method to effectively denoise HSIs which would otherwise require more than 40 GB. The package can be found at: \url{https://github.com/Helmholtz-AI-Energy/HyDe}.Comment: 5 page

Feed-Forward Optimization With Delayed Feedback for Neural Networks

Backpropagation has long been criticized for being biologically implausible, relying on concepts that are not viable in natural learning processes. This paper proposes an alternative approach to solve two core issues, i.e., weight transport and update locking, for biological plausibility and computational efficiency. We introduce Feed-Forward with delayed Feedback (F$^3$), which improves upon prior work by utilizing delayed error information as a sample-wise scaling factor to approximate gradients more accurately. We find that F$^3$ reduces the gap in predictive performance between biologically plausible training algorithms and backpropagation by up to 96%. This demonstrates the applicability of biologically plausible training and opens up promising new avenues for low-energy training and parallelization

Culturally induced range infilling of eastern redcedar: a problem in ecology, an ecological problem, or both?

The philosopher John Passmore distinguished between (1) “problems in ecology,” or what we might call problems in scientific understanding of ecological change, and (2) “ecological problems,” or what we might call problems faced by societies due to ecological change. The spread of eastern redcedar (Juniperus virginiana) and conversion of the central and southern Great Plains of North America to juniper woodland might be categorized as a problem in ecology, an ecological problem, or both. Here, we integrate and apply two interdisciplinary approaches to problem-solving—social-ecological systems thinking and ecocriticism—to understand the role of human culture in recognizing, driving, and responding to cedar’s changing geographic distribution. We interpret the spread of cedar as a process of culturally induced range infilling due to the ongoing social-ecological impacts of colonization, analyze poetic literary texts to clarify the concepts that have so far informed different cultural values related to cedar, and explore the usefulness of diverse interdisciplinary collaborations and knowledge for addressing social-ecological challenges like cedar spread in the midst of rapidly unfolding global change. Our examination suggests that it is not only possible, but preferable, to address cedar spread as both a scientific and a social problem. Great Plains landscapes are teetering between grassland and woodland, and contemporary human societies both influence and choose how to cope with transitions between these ecological states. We echo previous studies in suggesting that human cultural values about stability and disturbance, especially cultural concepts of fire, will be primary driving factors in determining future trajectories of change on the Great Plains. Although invasion-based descriptors of cedar spread may be useful in ecological research and management, language based on the value of restraint could provide a common vocabulary for effective cross-disciplinary and interdisciplinary communication about the relationship between culture and cedar, as well as an ethical framework for cross-cultural communication, decision-making, and management

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations after each forward-backward pass. This synchronization is the central algorithmic bottleneck. We introduce the Distributed Asynchronous and Selective Optimization (DASO) method, which leverages multi-GPU compute node architectures to accelerate network training while maintaining accuracy. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to current optimized data parallel training methods

Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations

We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare Propulate to the established optimization tool Optuna. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulateComment: 18 pages, 5 figures submitted to ISC High Performance 202

Ten lessons from the Spanish model of organ donation and transplantation

The organ donation and transplantation program in Spain has long been considered the gold standard worldwide. An in-depth understanding of the Spanish program may promote the development and reform of transplant programs in other countries. Here, we present a narrative literature review of the Spanish organ donation and transplantation program supplemented by expert feedback and presented according to a conceptual framework of best practices in the field. Core features of the Spanish program include its three-tiered governing structure, close and collaborative relationships with the media, dedicated professional roles, a comprehensive reimbursement strategy, and intensive tailored training programs for all personnel. Several more sophisticated measures have also been implemented, including those focused on advanced donation after circulatory death (DCD) and expanded criteria for organ donation. The overall program is driven by a culture of research, innovation, and continuous commitment and complemented by successful strategies in prevention of end-stage liver and renal disease. Countries seeking ways to reform their current transplant systems might adopt core features and may ultimately aspire to include the aforementioned sophisticated measures. Countries intent on reforming their transplant system should also introduce programs that support living donation, an area of the Spanish program with potential for further improvement

The Janthinobacterium sp. HH01 genome encodes a homologue of the V. cholerae CqsA and L. pneumophila LqsA autoinducer synthases

Janthinobacteria commonly form biofilms on eukaryotic hosts and are known to synthesize antibacterial and antifungal compounds. Janthinobacterium sp. HH01 was recently isolated from an aquatic environment and its genome sequence was established. The genome consists of a single chromosome and reveals a size of 7.10 Mb, being the largest janthinobacterial genome so far known. Approximately 80% of the 5,980 coding sequences (CDSs) present in the HH01 genome could be assigned putative functions. The genome encodes a wealth of secretory functions and several large clusters for polyketide biosynthesis. HH01 also encodes a remarkable number of proteins involved in resistance to drugs or heavy metals. Interestingly, the genome of HH01 apparently lacks the N-acylhomoserine lactone (AHL)-dependent signaling system and the AI-2-dependent quorum sensing regulatory circuit. Instead it encodes a homologue of the Legionella- and Vibrio-like autoinducer (lqsA/cqsA) synthase gene which we designated jqsA. The jqsA gene is linked to a cognate sensor kinase (jqsS) which is flanked by the response regulator jqsR. Here we show that a jqsA deletion has strong impact on the violacein biosynthesis in Janthinobacterium sp. HH01 and that a jqsA deletion mutant can be functionally complemented with the V. cholerae cqsA and the L. pneumophila lqsA genes