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Despite digitization and platformization, mass media and established media companies still play a crucial role in the provision of journalistic content in democratic societies. Competition is one key driver of (media) company behavior and is considered to have an impact on the media’s performance. However, theory and empirical research are ambiguous about the relationship. The objective of this article is to empirically analyze the effect of competition on media performance in a cross-national context. We assessed media performance of media companies as the importance of journalistic goals within their stated corporate goal system. We conducted a content analysis of letters to the shareholders in annual reports of more than 50 media companies from 2000 to 2014 to operationalize journalistic goal importance. When employing a fixed effects regression analysis, as well as a fuzzy set qualitative comparative analysis, results suggest that competition has a positive effect on the importance of journalistic goals, while the existence of a strong public service media sector appears to have the effect of “crowding out” commercial media companies.
Conventional superplasticizers based on polycarboxylate ether (PCE) show an intolerance to clay minerals due to intercalation of their polyethylene glycol (PEG) side chains into the interlayers of the clay mineral. An intolerance to very basic media is also known. This makes PCE an unsuitable choice as a superplasticizer for geopolymers. Bio-based superplasticizers derived from starch showed comparable effects to PCE in a cementitious system. The aim of the present study was to determine if starch superplasticizers (SSPs) could be a suitable additive for geopolymers by carrying out basic investigations with respect to slump, hardening, compressive and flexural strength, shrinkage, and porosity. Four SSPs were synthesized, differing in charge polarity and specific charge density. Two conventional PCE superplasticizers, differing in terms of molecular structure, were also included in this study. The results revealed that SSPs improved the slump of a metakaolin-based geopolymer (MK-geopolymer) mortar while the PCE investigated showed no improvement. The impact of superplasticizers on early hardening (up to 72 h) was negligible. Less linear shrinkage over the course of 56 days was seen for all samples in comparison with the reference. Compressive strengths of SSP specimens tested after 7 and 28 days of curing were comparable to the reference, while PCE led to a decline. The SSPs had a small impact on porosity with a shift to the formation of more gel pores while PCE caused an increase in porosity. Throughout this research, SSPs were identified as promising superplasticizers for MK-geopolymer mortar and concrete.
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.
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.
The amount of adsorbed styrene acrylate copolymer (SA) particles on cementitious surfaces at the early stage of hydration was quantitatively determined using three different methodological approaches: the depletion method, the visible spectrophotometry (VIS) and the thermo-gravimetry coupled with mass spectrometry (TG–MS). Considering the advantages and disadvantages of each method, including the respectively required sample preparation, the results for four polymer-modified cement pastes, varying in polymer content and cement fineness, were evaluated.
To some extent, significant discrepancies in the adsorption degrees were observed. There is a tendency that significantly lower amounts of adsorbed polymers were identified using TG-MS compared to values determined with the depletion method. Spectrophotometrically generated values were lying in between these extremes. This tendency was found for three of the four cement pastes examined and is originated in sample preparation and methodical limitations.
The main influencing factor is the falsification of the polymer concentration in the liquid phase during centrifugation. Interactions in the interface between sediment and supernatant are the cause. The newly developed method, using TG–MS for the quantification of SA particles, proved to be suitable for dealing with these revealed issues. Here, instead of the fluid phase, the sediment is examined with regard to the polymer content, on which the influence of centrifugation is considerably lower.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
(2020)
Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.
In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text
The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.
Die Mahlung als Zerkleinerungsprozess stellt seit den Anfängen der Menschheit eine der wichtigsten Verarbeitungsformen von Materialien aller Art dar - von der Getreidemahlung, über das Aufschließen von Heilkräutern in Mörsern bis hin zur Herstellung von Tonern für Drucker und Kopierer. Besonders die Zementmahlung ist in modernen Gesellschaften sowohl ein wirtschaftlicher als auch ein ökologischer Faktor. Mehr als zwei Drittel der elektrischen Energie der Zementproduktion werden für Rohmehl- und Klinker- bzw. Kompositmaterialmahlung verbraucht. Dies ist nur ein Grund, warum der Mahlprozess zunehmend in den Fokus vieler Forschungs- und Entwicklungsvorhaben rückt. Die Komplexität der Zementmahlung steigt im zunehmenden Maße an. Die simple „Mahlung auf Zementfeinheit“ ist seit langem obsolet. Zemente werden maßgeschneidert, mit verschiedensten Kombinationsprodukten, getrennt oder gemeinsam, in unterschiedlichen Mahlaggregaten oder mit ganz neuen Ansätzen gefertigt. Darüber hinaus gewinnt auch der Sektor des Baustoffrecyclings, mit allen damit verbundenen Herausforderungen, immer mehr an Bedeutung. Bei der Fragestellung, wie der Mahlprozess einerseits leistungsfähige Produkte erzeugen kann und andererseits die zunehmenden Anforderungen an Nachhaltigkeit erfüllt, steht das Mahlaggregat im Mittelpunkt der Betrachtungen. Dementsprechend gliedert sich, neben einer eingehenden Literaturrecherche zum Wissensstand, die vorliegende Arbeit in zwei übergeordnete Teile:
Im ersten Teil werden Untersuchungen an konventionellen Mahlaggregaten mit in der Zementindustrie verwendeten Kernprodukten wie Portlandzementklinker, Kalkstein, Flugasche und Hüttensand angestellt. Um eine möglichst effektive Mahlung von Zement und Kompositmaterialien zu gewährleisten, ist es wichtig, die Auswirkung von Mühlenparametern zu kennen. Hierfür wurde eine umfangreiche Versuchsmatrix aufgestellt und
abgearbeitet. Das Spektrum der Analysemethoden war ebenfalls umfangreich und wurde sowohl auf die gemahlenen Materialien als auch auf die daraus hergestellten Zemente und Betone angewendet. Es konnte gezeigt werden, dass vor allem die Unterscheidung zwischen Mahlkörpermühlen und mahlkörperlosen Mühlen entscheidenden Einfluss auf die Granulometrie und somit auch auf die Zementperformance hat. Besonders stark wurden die Verarbeitungseigenschaften, insbesondere der Wasseranspruch und damit auch das Porengefüge und schließlich Druckfestigkeiten sowie Dauerhaftigkeitseigenschaften der aus diesen Zementen hergestellten Betone, beeinflusst. Bei Untersuchungen zur gemeinsamen Mahlung von Kalkstein und Klinker führten ungünstige Anreicherungseffekte des gut mahlbaren Kalksteins sowie tonigen Nebenbestandteilen zu einer schlechteren Performance in allen Zementprüfungen.
Der zweite Teil widmet sich der Hochenergiemahlung. Die dahinterstehende Technik wird seit Jahrzehnten in anderen Wirtschaftsbranchen, wie der Pharmazie, Biologie oder auch Lebensmittelindustrie angewendet und ist seit einiger Zeit auch in der Zementforschung anzutreffen. Beispielhaft seien hier die Planeten- und Rührwerkskugelmühle als Vertreter genannt. Neben grundlegenden Untersuchungen an Zementklinker
und konventionellen Kompositmaterialien wie Hüttensand und Kalkstein wurde auch die Haupt-Zementklinkerphase Alit untersucht. Die Hochenergiemahlung von konventionellen Kompositmaterialien generierte zusätzliche Reaktivität bei gleicher Granulometrie gegenüber der herkömmlichen Mahlung. Dies wurde vor allem bei per se reaktivem Zementklinker als auch bei latent-hydraulischem Hüttensand beobachtet. Gemahlene Flugaschen konnten nur im geringen Maße weiter aktiviert werden. Der generelle Einfluss von Oberflächenvergrößerung, Strukturdefekten und Relaxationseffekten eines Mahlproduktes wurden eingehend untersucht und gewichtet. Die Ergebnisse bei der Hochenergiemahlung von Alit zeigten, dass die durch Mahlung eingebrachten Strukturdefekte eine Erhöhung der Reaktivität zur Folge haben. Hierbei konnte festgestellt werden, das maßgeblich Oberflächendefekte, strukturelle (Volumen-)defekte und als Konterpart Selbstheilungseffekte die reaktivitätsbestimmenden Faktoren sind. Weiterhin wurden Versuche zur Mahlung von Altbetonbrechsand durchgeführt. Im Speziellen wurde untersucht, inwieweit eine Rückführung von Altbetonbrechsand, als unverwertbarer Teil des Betonbruchs, in Form eines Zement-Kompositmaterials in den Baustoffkreislauf möglich ist. Die hierfür verwendete Mahltechnik umfasst sowohl konventionelle Mühlen als auch Hochenergiemühlen. Es wurden Kompositzemente mit variiertem Recyclingmaterialanteil hergestellt und auf grundlegende Eigenschaften untersucht. Zur Bewertung der Produktqualität wurde der sogenannte „Aktivierungskoeffizient“ eingeführt. Es stellte sich heraus, dass die Rückführung von Altbetonbrechsand als potentielles Kompositmaterial wesentlich vom Anteil des Zementsteins abhängt. So konnte beispielsweise reiner Zementstein als aufgemahlenes Kompositmaterial eine bessere Performance gegenüber dem mit Gesteinskörnung beaufschlagtem Altbetonbrechsand ausweisen. Bezogen auf die gemessenen Hydratationswärmen und Druckfestigkeiten nahm der Aktivierungskoeffzient mit fallendem Abstraktionsgrad ab. Ebenfalls sank der Aktivierungskoeffizient mit steigendem Substitutionsgrad. Als Vergleich wurden dieselben Materialien in konventionellen Mühlen aufbereitet. Die hier erzielten Ergebnisse können teilweise der Hochenergiemahlung als gleichwertig beurteilt werden. Folglich ist bei der Aktivierung von Recyclingmaterialien weniger die Mahltechnik als der Anteil an aktivierbarem Zementstein ausschlaggebend.
Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.