Within the context of an energy transition towards achieving a renewable low-impact energy consumption system, this study analyses how bottom-up initiatives can contribute to state driven top-down efforts to achieve the sustainability related goals of (1) reducing total primary energy consumption; (2) reducing residential electricity and heat consumption; and (3) increasing generated renewable energy and even attaining self-sufficiency. After identifying the three most cited German bottom-up energy transition cases, the initiatives have been qualitatively and quantitatively analysed. The case study methodology has been used and each initiative has been examined in order to assess and compare these with the German national panorama. The novel results of the analysis demonstrate the remarkable effects of communal living, cooperative investment and participatory processes on the creation of a new sustainable energy system. The study supports the claim that bottom-up initiatives could also contribute to energy sustainability goals together within the state driven plans. Furthermore, the research proves that the analysed bottom-up transitions are not only environmentally and socially beneficial but they can also be economically feasible, at least in a small scale, such as the current German national top-down energy policy panorama.
The amount and variety of data generated through social media sites has increased along with the widespread use of social media sites. In addition, the data production rate has increased in the same way. The inclusion of personal information within these data makes it important to process the data and reach meaningful information within it. This process can be called intelligence and this meaningful information may be for commercial, academic, or security purposes.An example application is developed in this study for intelligence on Twitter. Crimes in Turkey are classified according to Turkish Statistical Institute criminal data and keywords are defined according to this data. A total of 150,000 tweet data in the Turkish language are collected from Twitter between specified dates and processed by Turkish Zemberek natural language processing. It is seen that 56% of the people are talking about terrorist attacks and bombing attacks on the study dates. The words "bomb," "terror," "attack," "organization", and "explode" have percentages of 24%, 12%, 8%, 6%, and 6%, respectively. Moreover, associations between words and situations are found. Correlations are important to create new subclusters like "terror" and "rape" in this study with 0.90 correlation. Bigger masses can be accessible by expanding keyword groups to have a clear picture of the real situation.
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