Critérios e indicadores para avaliação da sustentabilidade de empresa florestal em Tailândia, Pará, na Amazônia brasileira.

O objetivo deste trabalho foi gerar fundamentos de um grupo de critérios e indicadores (C&I) adequados para serem utilizados em monitoramento empresarial e em auditagem de sustentabilidade das atividades de indústria madeireira na Amazônia oriental brasileira. O estudo se baseia na suposição de que C&I práticos e viáveis dependem das considerações dos interesses, visões e valores dos principais grupos de atores envolvidos no sistema de uso florestal. A ideia central do estudo foi identificar diferenças entre quatro grupos de atores (atores locais, governo, grupo operacional e pesquisadores) na avaliação de um conjunto de C&I definidos por pesquisadores internacionais em um Workshop, e usar as diferenças detectadas para elaborar, em forma participativa, os critérios adequados para monitoramento e auditagem. Um elemento metodológico importante foi a avaliação da sustentabilidade das atividades de uma empresa florestal pela aplicação pratica de C&I. O estudo confirmou o grande potencial desses critérios para avaliação da sustentabilidade das atividades de avaliação entre os diversos grupos. Pode-se afirmar que para os usuários potenciais, a praticidade e a simplicidade dos C&I são muito importantes, como também a relação entre custos e benefícios da aplicação dos mesmos. Conclui-se que o resultado da avaliação da sustentabilidade através dos C&I depende muito dos métodos utilizados para levantar informações. Para facilitar a aplicação de C&I em ferramentas de monitoramento e auditagem, os esforços futuros devem dimensionar-se na busca de verificadores inquestionáveis, de métodos de avaliação e definição de recomendações especificas introduzidas de resultados de avaliação.bitstream/item/63186/1/Oriental-Doc34.pd

Diagnóstico sócio-econômico da indústria madeireira Peracchi, no município de Tailândia, Estado do Pará.

O principal objetivo do diagnostico foi obter uma visão geral sobre a serraria da empresa Peracchi, no município de Tailândia, PA, como um dos parceiros potenciais do projeto "Manejo florestal sustentável em escala comercial na Amazônia brasileira", realizando através do convenio entre a Embrapa Amazônia Oriental e o Centro Internacional de Pesquisa Florestal (Cifor). O levantamento considerou a descrição geral da região e dos atores envolvidos no processo de uso da floresta, bem como a analise financeira da serraria. As informações de campo foram levantadas em duas semanas, em outubro de 1998, pelos técnicos envolvidos no projeto e por um grupo de estudantes de engenharia florestal da Faculdade de Ciências Agrarias do Para (FCAP). O diagnostico mostrou que Tailândia, como região tipica de fronteira, dependeu muito do setor florestal. Depois de mais de dez anos de exploração da madeira e da sua industrialização por mais de 50 serrarias, os recursos próximos da cidade já diminuíram bastante. Como resultado do grande numero de incentivos legais e ilegais do uso da terra e de grande flutuação, o sistema dos atores relacionados com o uso dos recursos florestais era bem heterogêneos e complexo. A serraria da empresa Peracchi era uma das poucas que produziam também para o mercado externo. A empresa começou um projeto de manejo de aproximadamente 12.000 ha, aprovado pelo Ibama, aplicando técnicas de exploração convencional. As atividades mostravam grandes deficiências ecológicas, sociais e econômicas. Os colonos que moravam próximos ao projeto não foram fortemente atingidos pelas atividades de exploração. Foram calculados custos de US$15,4/m3 para a exploração de 32.000 m3/ano. O transporte foi terceirizado e causou custos de US$ 9,05/m3. A serraria consumiu 27.500 m3 Francon/ano e produziu 12.000 m3 de tabuas. Assim, a taxa de aproveitamento relacionado com o volume real das toras foi de 35%. A produção de um metro cubico serrado custava, em media, US$84. O custo total de exploração. transporte e serraria era de mais de US$ 2 milhões/ano. Motivado pela esperança de certificação, o proprietário mostrou grande interesse em ser parceiro do convenio Embrapa/Cifor. Relacionado ao projeto de manejo sustentável, detectados, principalmente do pessoal, falta de estrategias para garantir o abastecimento da serraria com madeira, insuficiência de documentação legal e perigo de invasão.bitstream/item/65639/1/Oriental-Doc33.pd

Is speech the new blood? Recent progress in AI-based disease detection from audio in a nutshell

In recent years, advancements in the field of artificial intelligence (AI) have impacted several areas of research and application. Besides more prominent examples like self-driving cars or media consumption algorithms, AI-based systems have further started to gain more and more popularity in the health care sector, however whilst being restrained by high requirements for accuracy, robustness, and explainability. Health-oriented AI research as a sub-field of digital health investigates a plethora of human-centered modalities. In this article, we address recent advances in the so far understudied but highly promising audio domain with a particular focus on speech data and present corresponding state-of-the-art technologies. Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders and neurodegenerative disorders. Our selection of presented literature shows that the recent success of deep learning methods in other fields of AI also more and more translates to the field of digital health, albeit expert-designed feature extractors and classical ML methodologies are still prominently used. Limiting factors, especially for speech-based disease detection systems, are related to the amount and diversity of available data, e. g., the number of patients and healthy controls as well as the underlying distribution of age, languages, and cultures. Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection and faulty prediction

Vocalisation repertoire at the end of the first year of life: an exploratory comparison of Rett syndrome and typical development

Rett syndrome (RTT) is a rare, late detected developmental disorder associated with severe deficits in the speech-language domain. Despite a few reports about atypicalities in the speech-language development of infants and toddlers with RTT, a detailed analysis of the pre-linguistic vocalisation repertoire of infants with RTT is yet missing. Based on home video recordings, we analysed the vocalisations between 9 and 11 months of age of three female infants with typical RTT and compared them to three age-matched typically developing (TD) female controls. The video material of the infants had a total duration of 424 min with 1655 infant vocalisations. For each month, we (1) calculated the infants’ canonical babbling ratios with CBR(UTTER), i.e., the ratio of number of utterances containing canonical syllables to total number of utterances, and (2) classified their pre-linguistic vocalisations in three non-canonical and four canonical vocalisation subtypes. All infants achieved the milestone of canonical babbling at 9 months of age according to their canonical babbling ratios, i.e. CBR(UTTER) ≥ 0.15. We revealed overall lower CBRs(UTTER) and a lower proportion of canonical pre-linguistic vocalisations consisting of well-formed sounds that could serve as parts of target-language words for the RTT group compared to the TD group. Further studies with more data from individuals with RTT are needed to study the atypicalities in the pre-linguistic vocalisation repertoire which may portend the later deficits in spoken language that are characteristic features of RTT

The acoustic dissection of cough: diving into machine listening-based COVID-19 analysis and detection

OBJECTIVES: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19′s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. METHODS: By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. RESULTS: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). CONCLUSIONS: Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models

Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome

Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)–(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling