8 research outputs found

    Socio-Environmental Vulnerability Assessment for Sustainable Management

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    This Special Issue explores the cross-disciplinary approaches, methodologies, and applications of socio-environmental vulnerability assessment that can be incorporated into sustainable management. The volume comprises 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields. The articles collected in this volume come from all over the world and present the current state of the world’s environmental and social systems at a local, regional, and national level. New approaches and analytical tools for the assessment of environmental and social systems are studied. The practical implementation of sustainable development as well as progressive environmental and development policymaking are discussed. Finally, the authors deliberate about the perspectives of social–environmental systems in a rapidly changing world

    Benefit Sharing in the Arctic

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    This book provides a first-of-its-kind review and analysis of benefit sharing frameworks between extractive industries and Indigenous and local communities in different parts of the Arctic. The authors describe a wealth of case studies in order to examine predominant practices, policies, arrangements, mechanisms and impact assessment methodologies. They also discuss possible ways to improve and advance existing benefit sharing regimes, in order to attain fair and equitable benefit sharing and support sustainable development. Among the topics covered in the book are corporate social responsibility and social license to operate, principles and methodologies of determining compensation, legal and informal frameworks of benefit sharing, community response to extractive activities, and global-to-local linkages that shape benefit sharing processes. The book will be of interest to academics, industry experts, legal specialists, policymakers, community members concerned with industrial activities, and anyone interested in sustainable development in the Arctic

    Prevention strategies and modifiable risk factors for sport-related concussions and head impacts:A systematic review and meta-analysis

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    Objectives: To evaluate prevention strategies, their unintended consequences and modifiable risk factors for sport-related concussion (SRC) and /or head impact risk. Design: This systematic review and meta-analysis was registered on PROSPERO (CRD42019152982) and conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data sources: Eight databases (MEDLINE, CINAHL, APA PsycINFO, Cochrane (Systematic Review and Controlled Trails Registry), SPORTDiscus, EMBASE, ERIC0 were searched in October 2019 and updated in March 2022, and references searched from any identified systematic review. Eligibility criteria: Study inclusion criteria were as follows: (1) original data human research studies, (2) investigated SRC or head impacts, (3) evaluated an SRC prevention intervention, unintended consequence or modifiable risk factor, (4) participants competing in any sport, (5) analytic study design, (6) systematic reviews and meta-analyses were included to identify original data manuscripts in reference search and (7) peer-reviewed. Exclusion criteria were as follows: (1) review articles, pre-experimental, ecological, case series or case studies and (2) not written in English. Results: In total, 220 studies were eligible for inclusion and 192 studies were included in the results based on methodological criteria as assessed through the Scottish Intercollegiate Guidelines Network high ('++') or acceptable ('+') quality. Evidence was available examining protective gear (eg, helmets, headgear, mouthguards) (n=39), policy and rule changes (n=38), training strategies (n=34), SRC management strategies (n=12), unintended consequences (n=5) and modifiable risk factors (n=64). Meta-analyses demonstrated a protective effect of mouthguards in collision sports (incidence rate ratio, IRR 0.74; 95% CI 0.64 to 0.89). Policy disallowing bodychecking in child and adolescent ice hockey was associated with a 58% lower concussion rate compared with bodychecking leagues (IRR 0.42; 95% CI 0.33 to 0.53), and evidence supports no unintended injury consequences of policy disallowing bodychecking. In American football, strategies limiting contact in practices were associated with a 64% lower practice-related concussion rate (IRR 0.36; 95% CI 0.16 to 0.80). Some evidence also supports up to 60% lower concussion rates with implementation of a neuromuscular training warm-up programme in rugby. More research examining potentially modifiable risk factors (eg, neck strength, optimal tackle technique) are needed to inform concussion prevention strategies. Conclusions: Policy and rule modifications, personal protective equipment, and neuromuscular training strategies may help to prevent SRC. PROSPERO registration number CRD42019152982.</p

    Ocean Thermal Energy Conversion (OTEC)

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    The 21st century is characterized as an era of natural resource depletion, and humanity is faced with several threats due to the lack of food, energy, and water. Climate change and sea-level rise are at unprecedented levels, being phenomena that make predicting the future of ocean resources more complicated. Oceans contain a limitless amount of water with small (but finite) temperature differences from their surfaces to their floors. To advance the utilization of ocean resources, this book readdresses the past achievements, present developments, and future progress of ocean thermal energy, from basic sciences to sociology and cultural aspects

    Autonomous system control in unknown operating conditions

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    Autonomous systems have become an interconnected part of everyday life with the recent increases in computational power available for both onboard computers and offline data processing. The race by car manufacturers for level 5 (full) autonomy in self-driving cars is well underway and new flying taxi service startups are emerging every week, attracting billions in investments. Two main research communities, Optimal Control and Reinforcement Learning stand out in the field of autonomous systems, each with a vastly different perspective on the control problem. Controllers from the optimal control community are based on models and can be rigorously analyzed to ensure the stability of the system is maintained under certain operating conditions. Learning-based control strategies are often referred to as model-free and typically involve training a neural network to generate the required control actions through direct interactions with the system. This greatly reduces the design effort required to control complex systems. One common problem both learning- and model- based control solutions face is the dependency on a priori knowledge about the system and operating conditions such as possible internal component failures and external environmental disturbances. It is not possible to consider every possible operating scenario an autonomous system can encounter in the real world at design time. Models and simulators are approximations of reality and can only be created for known operating conditions. Autonomous system control in unknown operating conditions, where no a priori knowledge exists, is still an open problem for both communities and no control methods currently exist for such situations. Multiple model adaptive control is a modular control framework that divides the control problem into supervisory and low-level control, which allows for the combination of existing learning- and model-based control methods to overcome the disadvantages of using only one of these. The contributions of this thesis consist of five novel supervisory control architectures, which have been empirically shown to improve a system’s robustness to unknown operating conditions, and a novel low- level controller tuning algorithm that can reduce the number of required controllers compared to traditional tuning approaches. The presented methods apply to any autonomous system that can be controlled using model-based controllers and can be integrated alongside existing fault-tolerant control systems to improve robustness to unknown operating conditions. This impacts autonomous system designers by providing novel control mechanisms to improve a system’s robustness to unknown operating conditions
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