A survey on the feasibility of sound classification on wireless sensor nodes

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem

Intramuscular EMG versus Surface EMG of Lumbar Multifidus and Erector Spinae in Healthy Participants

Study Design: Cross-sectional design. Objective: The aim of this study was to investigate the correlation between intramuscular EMG (iEMG) and surface EMG (sEMG) from lumbar multifidus and erector spinae muscles during (submaximal) voluntary contraction tests in healthy participants. Summary of Background Data: Low back muscle function is a key component in the stability of the lumbar spine in which an important role is attributed to the lumbar multifidus (LM). Impairments in this stabilization system are held responsible for (chronic) low back pain. LM function can be measured by iEMG and sEMG; however, in earlier studies, results from iEMG and sEMG were inconsistent. Methods: Fifteen healthy adults were included. The intervention consisted of five clinical tests: resting, submaximal contraction tests of the lower back, abdominal contraction, and a biofeedback test in which LM and erector spinae (ES) activities were compared by iEMG and sEMG. Correlations were calculated with regard to original signal, co-contraction ratio, and cross-talk ratio. Correlation coefficients for each combination of iEMG and sEMG signals were calculated, to identify original signal (i.e., activity of only the targeted muscle) and possible cross-talk. Correlations >0.75 were considered as good concurrent validity. Results: The original signals of LM showed fair to high correlation coefficients (r: 0.3–0.8). Co-contraction of LM and ES was observed during all tests, but iEMG shows more variation in the correlations (r: 0.1–0.8) compared to sEMG (r: 0.3–0.8). Significant cross-talk was observed in all tests, particularly during the biofeedback test of iEMGESversus sEMGLM and iEMGLMversus sEMGES (r = 0.8). Conclusion: Surface EMG of ES and LM are no adequate representation of LM and ES activity measured by iEMG because of moderate/high cross-talk and co-contractions. Clinical tests that aim to assess LM activity do not represent isolated LM activity. This should be taken into account in future clinical studies

Effect of biochar on the mirobial enzymes activity of soil with Eucalyptus.

Session 5- Bacterial and Plant Physiology - Poster V.5

Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health:An Example Involving Periodic Workers' Health Assessments

Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs) in the context of occupational health. Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario from a workers' health assessment to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. Results Respect for autonomy is affected by uncertainty about what future consequences the worker is consenting to as a result of the fluctuating nature of ML-DSTs and validity evidence used to inform the worker. A beneficent advisory process is influenced because the three elements of evidence based practice are affected through use of a ML-DST. The principle of non-maleficence is challenged by the balance between group-level benefits and individual harm, the vulnerability of the worker in the occupational context, and the possibility of function creep. Justice might be empowered when the ML-DST is valid, but profiling and discrimination are potential risks. Conclusions Implications of ethical considerations have been described for the socially responsible design of ML-DSTs. Three recommendations were provided to minimize undesirable adverse effects of the development and implementation of ML-DSTs

Promoção do crescimento de milho por novas estirpes de bactérias associativas: resultados de ensaios em rede conduzidos pelo instituto nacional de ciência e tecnologia da Fixação Biológica do Nitrogênio (INCT-FBN).

Foi implementada uma rede de ensaios pelo Instituto Nacional de Ciência e Tecnologia da Fixação Biológica do Nitrogênio (INCT-FBN) para a validação de novas estirpes de bactérias associativas com capacidade de promover o crescimento vegetal. A rede de ensaios formada abrange cinco estações experimentais, sendo quatro localizadas no Estado do Paraná e uma em Goiás.Editores técnicos: Mariangela Hungria, Fábio Martins Mercante. RELARE 2014