Die vorliegende Arbeit beschäftigt sich mit dem Konzept der radial nicht-oszillierenden, zeitlich stabilen ultrakurzen Bessel ähnlichen Strahlen oder "Nadelstrahlen" ("needle beams"), die zu einer Klasse von optischen hochlokalisierten Wellenpaketen generalisiert werden. Hierbei wird die Theorie über das räumlich-zeitlichen Ausbreitungsverhaltens von nicht auseinanderdriftenden Nadelstrahlen mit Pulsdauern von kleiner als 10 fs näher diskutiert. Dies wird durch eine systematische Darstellung der Methoden zur Generierung und Detektierung von lokalisierten Wellen komplettiert, die ein optischen Drehmoment tragen. Für die Erzeugung von HLWs kommen räumliche Lichtmodulatoren zum Einsatz, die ein flexibles Zuschneiden von Wellenpaketen mit der Dauer weniger Zyklen des EM-Feldes erlauben. Es wird gezeigt, dass solche optischen Pulse sich über beträchtliche Entfernungen ausbreiten, ohne dass sich dabei signifikant der Strahldurchmesser vergrößert oder der Puls zeitlich verbreitert. In variabler Weise werden verschiedene geometrische (z.B. ringförmige) Lichtverteilungen erzeugt. Anwendungspotential findet sich insbesondere in den Techniken der räumlichen Pulsformung und Diagnostik. Als besonders wichtiger Ansatz ist der Zeit-Wellenfront-Sensor zu erwähnen, welcher die nichtlineare, mehrkanalige Autokorrelation, die Wellenfrontdetektion mittels nichtdiffraktiver Teilstrahlen nach dem Shack-Hartmann-Prinzip und eine adaptive Funktionalität miteinander vorteilhaft verbindet. Das enorme Potential solcher Ansätze wird durch die hohe Genauigkeit orts-, winkel- und zeitabhängiger Rekonstruktionen der Wellenpakete nachgewiesen. Darüber hinaus ermöglicht das räumliche Kodieren und anschließende Verfolgen der Teilstrahlen eine wesentliche Verbesserung der Identifikation relevanter Parameter von Verteilungsfunktionen. Schließlich werden erste Schritte zur experimentellen Generation von optischen "light bullets" mit ganzzahligen und fraktalen orbitalen Drehmomenten präsentiert. / This thesis deals with the concept of radially non-oscillating, temporally stable ultrashort-pulsed Bessel-like beams or "needle pulses", which are an example of a highly localized wave packet (HLW). HLWs are the closest approximation of linear-optical light bullets and provide specific benefits compared to conventional Gaussian-like light bullets. The spatio-temporally nonspreading propagation behavior of few-cycle needle beams of less than 10 fs duration will be theoretically discussed in detail. An overview of the generation and detection of localized waves carrying an orbital angular momentum is also given. High fidelity spatial light modulators are used for the generation of HLWs. The flexible tailoring of few-cycle wave packets at near-infrared wavelengths is reported. It is shown that such pulses propagate over a huge depth of focus, neither significantly changing their spot size or nor the pulse duration. Variable geometrical distributions like circular disks, rings, or bars of light are shaped and exploited as building blocks for structures of higher complexity. Another section of the thesis emphasizes the numerous potential applications of related techniques for an optimized two-dimensional spatial pulse shaping and diagnostics (reduce ambiguities) based on localized waves. As a particularly important example, time-wavefront sensing is used to combine nonlinear multichannel autocorrelation with Shack-Hartmann wavefront sensing by means of localized sub-beams and adaptive functionality. The capabilities of such devices are illustrated by the results of angular and temporal mapping of few-cycle wave packets. Moreover, spatial encoding and subsequent tracking of individual sub-beams, even at incident angles of up to 50°, enables to significantly improve the spot recognition. Finally, first steps towards the generation of optical light bullets carrying integer or non-integer orbital angular momenta are presented.

digitization, digital transformation, innovation, energy industry, HR, work with students, trainee program, trainee scheme, corporate culture, project management, onboarding, gen Z, generation Z

Ces dernières années, l'intelligence artificielle a été considérablement avancée et l'apprentissage en profondeur, où des réseaux de neurones profonds sont utilisés pour tenter d'imiter vaguement le cerveau humain, y a contribué de manière significative. Les réseaux de neurones profonds sont désormais capables d'obtenir un grand succès sur la base d'une grande quantité de données et de ressources de calcul suffisantes. Malgré leur succès, leur capacité à s'adapter rapidement à de nouveaux concepts, tâches et environnements est assez limitée voire inexistante. Dans cette thèse, nous nous intéressons à la façon dont les réseaux de neurones profonds peuvent s'adapter à des circonstances en constante évolution ou totalement nouvelles, de la même manière que l'intelligence humaine, et introduisons en outre des modules architecturaux adaptatifs et dynamiques ou des cadres de méta-apprentissage pour que cela se produise de manière efficace sur le plan informatique. Cette thèse consiste en une série d'études proposant des méthodes pour utiliser des calculs adaptatifs et dynamiques pour aborder les problèmes d'adaptation qui sont étudiés sous différentes perspectives telles que les adaptations au niveau de la tâche, au niveau temporel et au niveau du contexte. Dans le premier article, nous nous concentrons sur l'adaptation rapide des tâches basée sur un cadre de méta-apprentissage. Plus précisément, nous étudions l'incertitude du modèle induite par l'adaptation rapide à une nouvelle tâche avec quelques exemples. Ce problème est atténué en combinant un méta-apprentissage efficace basé sur des gradients avec une inférence variationnelle non paramétrique dans un cadre probabiliste fondé sur des principes. C'est une étape importante vers un méta-apprentissage robuste que nous développons une méthode d'apprentissage bayésienne à quelques exemples pour éviter le surapprentissage au niveau des tâches. Dans le deuxième article, nous essayons d'améliorer les performances de la prédiction de la séquence (c'est-à-dire du futur) en introduisant une prédiction du futur sauteur basée sur la taille du pas adaptatif. C'est une capacité critique pour un agent intelligent d'explorer un environnement qui permet un apprentissage efficace avec une imagination sauteur futur. Nous rendons cela possible en introduisant le modèle hiérarchique d'espace d'état récurrent (HRSSM) qui peut découvrir la structure temporelle latente (par exemple, les sous-séquences) tout en modélisant ses transitions d'état stochastiques de manière hiérarchique. Enfin, dans le dernier article, nous étudions un cadre qui peut capturer le contexte global dans les données d'image de manière adaptative et traiter davantage les données en fonction de ces informations. Nous implémentons ce cadre en extrayant des concepts visuels de haut niveau à travers des modules d'attention et en utilisant un raisonnement basé sur des graphes pour en saisir le contexte global. De plus, des transformations au niveau des caractéristiques sont utilisées pour propager le contexte global à tous les descripteurs locaux de manière adaptative. / Over the past few years, artificial intelligence has been greatly advanced, and deep learning, where deep neural networks are used to attempt to loosely emulate the human brain, has significantly contributed to it. Deep neural networks are now able to achieve great success based on a large amount of data and sufficient computational resources. Despite their success, their ability to quickly adapt to new concepts, tasks, and environments is quite limited or even non-existent. In this thesis, we are interested in how deep neural networks can become adaptive to continually changing or totally new circumstances, similarly to human intelligence, and further introduce adaptive and dynamic architectural modules or meta-learning frameworks to make it happen in computationally efficient ways. This thesis consists of a series of studies proposing methods to utilize adaptive and dynamic computations to tackle adaptation problems that are investigated from different perspectives such as task-level, temporal-level, and context-level adaptations. In the first article, we focus on task-level fast adaptation based on a meta-learning framework. More specifically, we investigate the inherent model uncertainty that is induced from quickly adapting to a new task with a few examples. This problem is alleviated by combining the efficient gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. It is an important step towards robust meta-learning that we develop a Bayesian few-shot learning method to prevent task-level overfitting. In the second article, we attempt to improve the performance of sequence (i.e. future) prediction by introducing a jumpy future prediction that is based on the adaptive step size. It is a critical ability for an intelligent agent to explore an environment that enables efficient option-learning and jumpy future imagination. We make this possible by introducing the Hierarchical Recurrent State Space Model (HRSSM) that can discover the latent temporal structure (e.g. subsequences) while also modeling its stochastic state transitions hierarchically. Finally, in the last article, we investigate a framework that can capture the global context in image data in an adaptive way and further process the data based on that information. We implement this framework by extracting high-level visual concepts through attention modules and using graph-based reasoning to capture the global context from them. In addition, feature-wise transformations are used to propagate the global context to all local descriptors in an adaptive way.

The dissertation analyses job-related education, training and development in Soweto since 1940. The effect which the interference of the National Party government had on jobrelated education for black South Africans in Soweto is examined, as it is of importance to know that qualifiers were only permitted to operate as trades people in the bantustans and not in the "white" cities. It is discussed that prior to the assassination of prime minister Dr H F Verwoerd in 1966, the NP government had little interest in promoting urban black upliftment. Also mentioned is the economic situation at that time, which forced the N P government to introduce the Manpower Training Act, permitting Africans to qualify in trades which were until 1981 reserved for whites only. At the eentre of the discussion are the few Sowetan colleges which deal with job-related education. training and development in the African township. Despite the demand for skills training of black South Africans, training deteriorated and the dissertation investigates the reasons surrounding the loss of interest in the communities and why interest groups were no longer concerned about this type of training. The dissertation concludes with a possible future perspective which needs to be implemented to enable job-related education, training and development in Soweto to expand. The need to train Sowetans in their own colleges as opposed to colleges outside Soweto is examined. / History / M.A. (History)

The dissertation analyses job-related education, training and development in Soweto since 1940. The effect which the interference of the National Party government had on jobrelated education for black South Africans in Soweto is examined, as it is of importance to know that qualifiers were only permitted to operate as trades people in the bantustans and not in the "white" cities. It is discussed that prior to the assassination of prime minister Dr H F Verwoerd in 1966, the NP government had little interest in promoting urban black upliftment. Also mentioned is the economic situation at that time, which forced the N P government to introduce the Manpower Training Act, permitting Africans to qualify in trades which were until 1981 reserved for whites only. At the eentre of the discussion are the few Sowetan colleges which deal with job-related education. training and development in the African township. Despite the demand for skills training of black South Africans, training deteriorated and the dissertation investigates the reasons surrounding the loss of interest in the communities and why interest groups were no longer concerned about this type of training. The dissertation concludes with a possible future perspective which needs to be implemented to enable job-related education, training and development in Soweto to expand. The need to train Sowetans in their own colleges as opposed to colleges outside Soweto is examined. / History / M.A. (History)

A fundamental change in societal values and economic structures is required to address increasing pressures on ecosystems and natural resources. Transition research has developed in the last decades to analyze the co-dynamics of technological, institutional, social and economic elements in the provision of key functions such as energy, water and food supply. This doctoral dissertation provides conceptual and methodological contributions to the pro-active governance of sustainability transitions. Three research gaps are identified that are addressed in this dissertation. First, a comprehensive conceptualization of learning in sustainability transitions is currently missing that comprises learning at multiple societal levels (ranging from individuals to policy-actors). Learning concepts are often not explicitly discussed in transition research even though learning is considered as fundamental for innovation processes, niche formation and development as well as breakthrough and diffusion of innovations. Second, methods for the analysis and design of transition governance processes are lacking that specify case-specific intervention points and roles of actors in the implementation of innovations. Third, participatory modeling approaches are only applied to a limited extent in transition research despite a high potential for supporting communication and learning. The conceptualization of multi-level learning developed in this doctoral research conceptualizes learning at different societal levels as specific learning contexts ranging from individual and group contexts to organizational and policy contexts. The conceptual framework further differentiates between learning processes, intensity, objects, outcomes, subjects and factors, allowing for a more detailed analysis of learning within and across learning contexts. Thus, learning contexts can be linked by processes that involve actors from different learning contexts (e.g., community groups and policy-makers), as well as exchanges of physical aspects, institutions and knowledge (in the form of ‘learning factors’). This research has also provided a classification of model uses in transition research that supports a purposeful discussion of the opportunities of modeling and promising future research directions. The methodology developed in this doctoral research aims at the analysis and design of transition governance processes by specifying the various opportunities to contribute to sustainability transitions through purposeful action at different societal levels, as well as related roles of stakeholders in implementing such processes of change. The methodology combines different streams of previous research: 1) a participatory modeling approach to identify problem perceptions, case-specific sustainability innovations as well as related implementation barriers, drivers and responsibilities; 2) a systematic review to identify supportive and impeding learning factors from the general literature that can complement case-specific factors; and 3) a method for the analysis and design of case-specific transition governance processes. Three case studies in Canada (topic: sustainable food systems), Cyprus (water-energy-food nexus) and Germany (sustainable heating supply) have been selected to test and iteratively develop the methodology described above. The results for each case study reveal that there are learning objects (i.e., learning requirements) in all learning contexts, which underscores the importance of multi-level learning in sustainability transitions, ranging from the individual to the group, organizational and policy levels. Actors have various opportunities to actively facilitate societal transformations towards sustainable development either directly through actions at their particular societal levels (i.e., context-internal learning) or indirectly through actions that influence learning at other societal levels. In fact, most of the learning factors require cooperation across learning contexts during the implementation process. The comparing of learning factors across case studies underline the importance of several factor categories, such as ‘physical a ‘disturbance or crisis’, ‘information and knowledge’. Of the 206 factors identified by stakeholders, 40 factors are case-specific and not contained in the general, review-based factor list. This underscores the value of participatory research, as general, top-down analyses might have overlooked these case-specific factors. The methodology presented in this dissertation allows for the identification and analysis of case-specific intervention points for sustainability transitions at multiple societal levels. The methodology furthermore permits the analysis of interplay between individual, group, organizational and policy actions, which is a first step towards their coordination. The focus on sustainability innovations links the broad topic of sustainability transitions to a set of opportunities for practical interventions and overcoming their implementation barriers. The methodology presented allows for the analysis and design of these interlinkages between learning contexts. While the methodology cannot provide any ‘silver bullets’ for inducing sustainability transitions, it is flexible enough to identify an appropriate abstraction level for analyzing and designing transition governance processes. The methodology developed in this doctoral research also provides several contributions for the development of participatory modeling methods in transition research. Thus, the participatory method supports an integrated analysis of barriers and drivers of sustainability innovations, and allows application in practice and education. The concepts and methods developed in this research project allow for reflection on transition governance processes from a systemic viewpoint. Experiences in the case studies underline the applicability of the concepts and methods developed for the analysis of case-specific transition governance processes. Despite substantial differences in the geographic location, culture and topics addressed, all case studies include promising sustainability innovations and the engagement of multiple actors in their implementation. The diversity and multitude of initiatives in the case study regions provides an optimistic outlook on future opportunities for large-scale sustainability transitions.

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.

For many years, community colleges that chose to offer community college baccalaureate (CCB) programs were looked upon in a negative light (Rice, 2015). However, as the need for specialized baccalaureates within specific fields and job markets have continued to grow (McKee, 2005), CCB programs are becoming more widely accepted throughout the United States. In spite of this paradigm shift, Mississippi is one of the remaining states that have not embraced the idea of CCB programs, in spite of its statistical deficiency in regards to baccalaureate degree holding citizens (Williams, 2010). The focus of this study was to explore the perceptions of community college administrators in Mississippi with regards to the influence of CCB programs to the community college mission of institutions in their state. This study indicates that administrators in Mississippi recognize the benefits of offering CCB programs, but do not want CCB programs to take away from the well-established statewide higher education system through mission creep. Many of the strong position statements received overwhelmingly neutral responses. In contrast, Administrators who chose to give their opinion indicated that they are not familiar with how CCB programs are implemented, and do not believe that Mississippi is ready for CCB programs across the state. However, respondents felt that the community college mission is always evolving, should meet students’ needs, and varies from location to location. The findings also show that administrators are favorable to the piloting of CCB programs at a few (1-2) institutions, even though they believe the programs will take funding away from current programs and do not want community colleges evolving into 4-year institutions. The study also concludes that there is a significant difference between institution size and survey questions #18 and #20. There is also a significant difference between length of time in the community college sector and survey questions #15, #17, and #18.

A comercialização de produtos florestais não madeireiros (PFNMs) popularizou-se como atividade promotora do desenvolvimento socioeconômico de comunidades florestais com baixo impacto ambiental. Há evidências, contudo, de que a exploração possa produzir efeitos ecológicos e socioeconômicos negativos, sugerindo que é necessário monitorar tais iniciativas. A comercialização frequentemente ocorre em áreas remotas e em contextos de pobreza, como é o caso de muitas daquelas da Amazônia brasileira. Desta forma, é necessário que o monitoramento restrinja-se a avaliar os efeitos evidenciados como mais comuns em estudos anteriores. Este estudo revisa e sintetiza as evidências científicas dos efeitos da exploração de PFNMs sobre parâmetros ecológicos e socioeconômicos e, a partir daí, indica aqueles mais relevantes ao monitoramento. O estudo também levanta até que ponto o monitoramento é implementado no contexto da Amazônia brasileira e avalia quais os parâmetros importantes e viáveis de monitoramento neste caso específico. Para isso, foram realizadas revisões sistemáticas da literatura e a consulta a profissionais da área através do método Delphi. Os resultados indicam que efeitos ecológicos negativos são frequentes, principalmente quando se coletam folhas ou cascas. Alterações em órgãos ou processos fisiológicos e a taxa de sobrevivência dos espécimes explorados são parâmetros que devem ser monitorados, em particular quando se coletam frutos e partes vegetativas. Para todos os tipos de PFNMs, o tamanho e a estrutura populacional são parâmetros prioritários ao monitoramento. A riqueza de espécies da comunidade explorada merece atenção, principalmente quando se coletam frutos. Para os aspectos socioeconômicos, efeitos positivos foram mais frequentes que negativos. A contribuição da renda monetária obtida com o comércio na renda total, a regularidade de ingresso desta renda e o papel dos PFNMs como recursos de salvaguarda são parâmetros do capital financeiro prioritários ao monitoramento. Para o capital social, o empoderamento feminino, a coesão de grupo e o acesso aos benefícios gerados pela comercialização devem ser monitorados. Na Amazônia brasileira são poucas as iniciativas de implementação do monitoramento da comercialização de PFNMs. Embora este seja considerado importante, existem dificuldades que derivam principalmente da falta de apoio institucional, políticas de incentivo e de recursos financeiros. Os profissionais participantes do Delphi consideram que os parâmetros ecológicos mais importantes a monitorar neste contexto são o tamanho e a estrutura populacional do recurso explorado, o aumento da taxa de mortalidade, a quantidade total de recurso extraída e a técnica de coleta utilizada. Para os parâmetros econômicos, aspectos do mercado, como o preço pago ao coletor, a demanda e a qualidade do produto, bem como a renda monetária obtida pelos indivíduos são os parâmetros considerados mais importantes. Por fim, para os aspectos sociais, os efeitos na cultura, na qualidade de vida e na organização interna da comunidade foram priorizados. Os profissionais indicam que é viável estabelecer o monitoramento dos parâmetros levantados. / Amazon, non-timber forest products, ecological effects, socioeconomic effects, monitoring.

A comercialização de produtos florestais não madeireiros (PFNMs) popularizou-se como atividade promotora do desenvolvimento socioeconômico de comunidades florestais com baixo impacto ambiental. Há evidências, contudo, de que a exploração possa produzir efeitos ecológicos e socioeconômicos negativos, sugerindo que é necessário monitorar tais iniciativas. A comercialização frequentemente ocorre em áreas remotas e em contextos de pobreza, como é o caso de muitas daquelas da Amazônia brasileira. Desta forma, é necessário que o monitoramento restrinja-se a avaliar os efeitos evidenciados como mais comuns em estudos anteriores. Este estudo revisa e sintetiza as evidências científicas dos efeitos da exploração de PFNMs sobre parâmetros ecológicos e socioeconômicos e, a partir daí, indica aqueles mais relevantes ao monitoramento. O estudo também levanta até que ponto o monitoramento é implementado no contexto da Amazônia brasileira e avalia quais os parâmetros importantes e viáveis de monitoramento neste caso específico. Para isso, foram realizadas revisões sistemáticas da literatura e a consulta a profissionais da área através do método Delphi. Os resultados indicam que efeitos ecológicos negativos são frequentes, principalmente quando se coletam folhas ou cascas. Alterações em órgãos ou processos fisiológicos e a taxa de sobrevivência dos espécimes explorados são parâmetros que devem ser monitorados, em particular quando se coletam frutos e partes vegetativas. Para todos os tipos de PFNMs, o tamanho e a estrutura populacional são parâmetros prioritários ao monitoramento. A riqueza de espécies da comunidade explorada merece atenção, principalmente quando se coletam frutos. Para os aspectos socioeconômicos, efeitos positivos foram mais frequentes que negativos. A contribuição da renda monetária obtida com o comércio na renda total, a regularidade de ingresso desta renda e o papel dos PFNMs como recursos de salvaguarda são parâmetros do capital financeiro prioritários ao monitoramento. Para o capital social, o empoderamento feminino, a coesão de grupo e o acesso aos benefícios gerados pela comercialização devem ser monitorados. Na Amazônia brasileira são poucas as iniciativas de implementação do monitoramento da comercialização de PFNMs. Embora este seja considerado importante, existem dificuldades que derivam principalmente da falta de apoio institucional, políticas de incentivo e de recursos financeiros. Os profissionais participantes do Delphi consideram que os parâmetros ecológicos mais importantes a monitorar neste contexto são o tamanho e a estrutura populacional do recurso explorado, o aumento da taxa de mortalidade, a quantidade total de recurso extraída e a técnica de coleta utilizada. Para os parâmetros econômicos, aspectos do mercado, como o preço pago ao coletor, a demanda e a qualidade do produto, bem como a renda monetária obtida pelos indivíduos são os parâmetros considerados mais importantes. Por fim, para os aspectos sociais, os efeitos na cultura, na qualidade de vida e na organização interna da comunidade foram priorizados. Os profissionais indicam que é viável estabelecer o monitoramento dos parâmetros levantados. / Amazon, non-timber forest products, ecological effects, socioeconomic effects, monitoring.