This paper focuses on the topical and problematic area of social innovations. The aim of this paper is to develop an original approach to the allocation of social innovations, taking into account characteristics such as the degree of state participation, the scope of application, the type of initiations as well as the degree of novelty, which will be elaborated on further in this article. In order to achieve this goal, the forty-two most successful social innovations were identified and systematized. The results of this study demonstrated that 73.5% of social innovations are privately funded, most of them operating on an international level with a high degree of novelty. Moreover, 81% of all social innovations are civic initiatives. Social innovations play an important role in the growth of both developed and less developed countries alike as highlighted in our extensive analysis
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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In this thesis we analyzed clinimetric measurement properties of physical fitness tests in wheelchair-using youth with SB. Furthermore, the amount of physical behavior in wheelchair-using youth with SB was quantified and associations with age, gender, VO2peak and Hoffer classification were evaluated. Finally, we described the factors associated with physical behavior in youth with SB and youth with physical disabilities, after which the evidence of interventions to improve physical behavior in youth with physical disabilities was analyzed. This last chapter presents the theoretical and clinical implications. At the end, methodological considerations and directions for further research will be discussed after which the overall conclusion is presented.
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Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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This paper outlines an investigation into the updating of fatigue reliability through inspection data by means of structural correlation. The proposed methodology is based on the random nature of fatigue fracture growth and the probability of damage detection and introduces a direct link between predicted crack size and inspection results. A distinct focus is applied on opportunities for utilizing inspection information for the updating of both inspected and uninspected (or uninspectable) locations.
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Abstract: The key challenge of managing Floating Production Storage and Offloading assets (FPSOs) for offshore hydrocarbon production lies in maximizing the economic value and productivity, while minimizing the Total Cost of Ownership and operational risk. This is a comprehensive task, considering the increasing demands of performance contracting, (down)time reduction, safety and sustainability while coping with high levels of phenomenological complexity and relatively low product maturity due to the limited amount of units deployed in varying operating conditions. Presently, design, construction and operational practices are largely influenced by high-cycle fatigue as a primary degradation parameter. Empirical (inspection) practices are deployed as the key instrument to identify and mitigate system anomalies and unanticipated defects, inherently a reactive measure. This paper describes a paradigm-shift from predominant singular methods into a more holistic and pro-active system approach to safeguard structural longevity. This is done through a short review of several synergetic Joint Industry Projects (JIP’s) from different angles of incidence on enhanced design and operations through coherent a-priori fatigue prediction and posteriori anomaly detection and -monitoring.
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The sense of safety and security of older people is a widely acknowledged action domain for policy and practice in age-friendly cities. Despite an extensive body of knowledge on the matter, the theory is fragmented, and a classification is lacking. Therefore, this study investigated how older people experience the sense of safety and security in an age-friendly city. A total of four focus group sessions were organised in The Hague comprising 38 older people. Based on the outcomes of the sessions, the sense of safety and security was classified into two main domains: a sense of safety and security impacted by intentional acts and negligence (for instance, burglary and violence), and a sense of safety and security impacted by non-intentional acts (for instance, incidents, making mistakes online). Both domains manifest into three separate contexts, namely the home environment, the outdoor environment and traffic and the digital environment. In the discussions with older people on these derived domains, ideas for potential improvements and priorities were also explored, which included access to information on what older people can do themselves to improve their sense of safety and security, the enforcement of rules, and continuous efforts to develop digital skills to improve safety online. Original article at MDPI; DOI: https://doi.org/10.3390/ijerph19073960
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BACKGROUND: The design and manufacturing of effective foot orthoses is a complex multidisciplinary problem involving biomedical and gait pattern aspects, technical material and geometric design elements as well as psychological and social contexts. This complexity contributes to the current trial-and-error and experience-based orthopedic footwear practice in which a major part of the expertise is implicit. This hampers knowledge transfer, reproducibility and innovation. OBJECTIVE/METHODS: A systematic review of literature has been performed to find evidence of explicit knowledge, quantitative guidelines and design motivations of pedorthists. RESULTS: 17 studies have been included. No consensus is found on which measurable parameters ensure proper foot and ankle functioning. Parameters suggested are: neutral foot positioning and control of rearfoot motion, maximum arch, but also tibial internal/external rotation as well as a three point force system. Also studies evaluating foot orthoses centering on the diagnosis or orthosis type find no clear guidelines for treatment or for measuring the effectiveness. CONCLUSIONS: A gap in the translation from diagnosis to a specific, customized and quantified effective orthosis design is identified. Suggested solutions are both top-down, fitting of patient data in simulations, as well as bottom-up, quantifying current practices of pedorthists in order to develop new practical guidelines and evidence-based procedures.
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