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Multi-dimensional deconstruction and theme evolution of China’s energy policy

Energy policies are vital tools used by countries to regulate economic and social development as well as guarantee national security. To address the problems of fragmented policy objectives, conflicting tools, and overlapping initiatives, the internal logic and evolutionary trends of energy policies must be explored using the policy content. This study uses 38,277 energy policies as a database and summarizes the four energy policy objectives: clean, low-carbon, safe, and efficient. Using the TextCNN model to classify and deconstruct policies, the LDA + Word2vec theme conceptualization and similarity calculations were compared with the EISMD evolution framework to determine the energy policy theme evolution path. Results indicate that the density of energy policies has increased. Policies have become more comprehensive, barriers between objectives have gradually been broken, and low-carbon objectives have been strengthened. The evolution types are more diversified, evolution paths are more complicated, and the evolution types are often related to technology, industry, and market maturity. Traditional energy themes evolve through inheritance and merger; emerging technology and industry themes evolve through innovation, inheritance, and splitting. Moreover, this study provides a replicable analytical framework for the study of policy evolution in other sectors and evidence for optimizing energy policy design

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Multi-dimensional deconstruction and theme evolution of China’s energy policy

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Analysis of civic initiatives

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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An introduction of accidents’ classification based on their outcome control

Most safety oriented organizations have established their accidents classification taking into account the magnitude of the combined adverse outcomes on humans, assets and the environment without considering the accidents‟ potential and the actual attempts of the involved persons to intervene with the accident progress. The specific research exploited a large sample of an aviation organization accident records for an11 years‟ time period and employed frequency and chi-square analyses to test a new accident classification scheme based on the distinction among the safety events with or without human intervention on the accident scene, indicating the management or not of their ultimate consequences. Furthermore, the research depicted the effectiveness of personnel strains to alleviate the accident potential outcomes and studied the contribution of time, local and complexity factors on the accident control attempt and the humans‟ positive or negative interference. The specific newly proposed accident classification successfully addressed the “controlled” or “uncontrolled” traits of the safety events studies, prior their severities consideration, and unveiled the effectiveness of personnel efforts to compensate for the adverse accident march. The portion between controlled and uncontrolled accidents in terms of the human intervention along with the effectiveness of the later may comprise a useful safety performance indicator that can be adopted by any industry sector and may be recommended through international and state safety related authorities.

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An introduction of accidents’ classification based on their outcome control

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A Classification of Modification Categories for Business Rules

Author supplied Business rules play a critical role in an organization’s daily activities. With the increased use of business rules (solutions) the interest in modelling guidelines that address the manageability of business rules has increased as well. However, current research on modelling guidelines is mainly based on a theoretical view of modifications that can occur to a business rule set. Research on actual modifications that occur in practice is limited. The goal of this study is to identify modifications that can occur to a business rule set and underlying business rules. To accomplish this goal we conducted a grounded theory study on 229 rules set, as applied from March 2006 till June 2014, by the National Health Service. In total 3495 modifications have been analysed from which we defined eleven modification categories that can occur to a business rule set. The classification provides a framework for the analysis and design of business rules management architectures.

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A Classification of Modification Categories for Business Rules

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Classification of dwelling characteristics with machine learning algorithms based on smart meter & weather data

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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Classification of dwelling characteristics with machine learning algorithms based on smart meter & weather data

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The quality of teacher educators in the European policy debate: actions and measures to improve the professionalism of teacher educators

This study examines how the contemporary European policy debate addresses the further development of the quality of teacher educators. A classification framework based on the literature on professionalism was used to compare European and Member State policy actions and measures on the quality of teacher educators through an analysis of seven European policy documents and a questionnaire completed by key policy-makers in 16 European countries. The findings show that European Union policy documents pay limited attention to the quality of teacher educators. However, the professionalism of teacher educators receives more policy attention at the level of individual Member States. Most of these policies are part of general policies for higher education teachers, while the initiative lies with governments and teacher education institutes. The role of the professionals themselves in developing policies to strengthen their professionalism seems very limited.

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A comprehensive approach for mitigating impersonation in online assessment

The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.

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A comprehensive approach for mitigating impersonation in online assessment

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Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

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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