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Year of publication
- 2020 (57) (remove)
A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
This paper proposes a practice-theoretical journalism research approach for an alternate and innovative perspective of digital journalism’s current empirical challenges. The practice-theoretical approach is introduced by demonstrating its explanatory power in relation to demarcation problems, technological changes, economic challenges and challenges to journalism’s legitimacy. Its respective advantages in dealing with these problems are explained and then compared to established journalism theories. The particular relevance of the theoretical perspective is due to (1) its central decision to observe journalistic practices, (2) the transgression of conventional journalistic boundaries, (3) the denaturalization of journalistic norms and laws, (4) the explicit consideration of a material, socio-technical dimension of journalism, (5) a focus on the conflicting relationship between journalistic practices and media management practices, and (6) prioritizing order generation over stability.
The performance of ductless personalized ventilation (DPV) was compared to the performance of a typical desk fan since they are both stand-alone systems that allow the users to personalize their indoor environment. The two systems were evaluated using a validated computational fluid dynamics (CFD) model of an office room occupied by two users. To investigate the impact of DPV and the fan on the inhaled air quality, two types of contamination sources were modelled in the domain: an active source and a passive source. Additionally, the influence of the compared systems on thermal comfort was assessed using the coupling of CFD with the comfort model developed by the University of California, Berkeley (UCB model). Results indicated that DPV performed generally better than the desk fan. It provided better thermal comfort and showed a superior performance in removing the exhaled contaminants. However, the desk fan performed better in removing the contaminants emitted from a passive source near the floor level. This indicates that the performance of DPV and desk fans depends highly on the location of the contamination source. Moreover, the simulations showed that both systems increased the spread of exhaled contamination when used by the source occupant.
Welfare‐state transformation and entrepreneurial urban politics in Western welfare states since the late 1970s have yielded converging trends in the transformation of the dominant Fordist paradigm of social housing in terms of its societal function and institutional and spatial form. In this article I draw from a comparative case study on two cities in Germany to show that the resulting new paradigm is simultaneously shaped by the idiosyncrasies of the country's national housing regime and local housing policies. While German governments have successively limited the societal function of social housing as a legitimate instrument only for addressing exceptional housing crises, local policies on providing and organizing social housing within this framework display significant variation. However, planning and design principles dominating the spatial forms of social housing have been congruent. They may be interpreted as both an expression of the marginalization of social housing within the restructured welfare housing regime and a tool of its implementation according to the logics of entrepreneurial urban politics.
This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.
Acoustic travel-time TOMography (ATOM) allows the measurement and reconstruction of air temperature distributions. Due to limiting factors, such as the challenge of travel-time estimation of the early reflections in the room impulse response, which heavily depends on the position of transducers inside the measurement area, ATOM is applied mainly outdoors. To apply ATOM in buildings, this paper presents a numerical solution to optimize the positions of transducers. This optimization avoids reflection overlaps, leading to distinguishable travel-times in the impulse response reflectogram. To increase the accuracy of the measured temperature within tomographic voxels, an additional function is employed to the proposed numerical method to minimize the number of sound-path-free voxels, ensuring the best sound-ray coverage of the room. Subsequently, an experimental set-up has been performed to verify the proposed numerical method. The results indicate the positive impact of the optimal positions of transducers on the distribution of ATOM-temperatures.
The contribution explores the migratory situation on the Balkans and more specifically in the so-called Refugee District in Belgrade from a spatial perspective. By visualizing the areas of tensions in the Refugee District, the city of Belgrade, Serbia and Europe it aims to disentangle the political and socio-spatial levels that lead to the stuck situation of in-betweenness at the gates of the European Union.
In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
Der Beitrag verbindet die Diskussion um die postpolitische Stadt mit der zunehmenden wissenschaftlichen und aktivistischen Auseinandersetzung mit dem Anthropozän, ein Konzept, das die ökologischen und sozialpolitischen Implikationen menschlichen Handelns auf die Erdoberfläche beschreibt. Anhand von drei ausgewählten Fallstudien erkunden wir,
wie die spezifisch anthropogene, also menschengemachte, Krise urbaner Luftverschmutzung in künstlerischen Positionen problematisiert wird. Im Kontext des potenziellen Vormarschs von Postpolitik besprechen wir, wie der ambivalente Diskurs des Anthropozäns einerseits Depolitisierung begünstigt und andererseits neue Möglichkeiten für die Repolitisierung
globaler Umweltherausforderungen ermöglicht.