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Bauhaus-Gastprofessorin Mirjam Wenzel referierte am 30. Juni 2021 im Audimax der Bauhaus-Universität Weimar zur Entstehungsgeschichte und Konzeption Jüdischer Museen. Dabei ging sie darauf ein, inwiefern diese Museen besonders relevant für aktuelle gesellschaftliche und politische Fragestellungen sind. Prof. Wenzels zweiter öffentlicher Vortrag an der Bauhaus-Universität Weimar skizzierte die Potentiale von Kultureinrichtungen in Zeiten gesellschaftspolitischer Veränderungen im Allgemeinen und die Bedeutung Jüdischer Museen angesichts verbaler und tätlicher Gewalt gegen Jüdinnen und Juden im Besonderen.
In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk.
Marine macroalgae such as Ulva intestinalis have promising properties as feedstock for cosmetics and pharmaceuticals. However, since the quantity and quality of naturally grown algae vary widely, their exploitability is reduced – especially for producers in high-priced markets. Moreover, the expansion of marine or shore-based cultivation systems is unlikely in Europe, since promising sites either lie in fishing zones, recreational areas, or natural reserves. The aim was therefore to develop a closed photobioreactor system enabling full control of abiotic environmental parameters and an effective reconditioning of the cultivation medium in order to produce marine macroalgae at sites distant from the shore. To assess the feasibility and functionality of the chosen technological concept, a prototypal plant has been implemented in central Germany – a site distant from the sea. Using a newly developed, submersible LED light source, cultivation experiments with Ulva intestinalis led to growth rates of 7.72 ± 0.04 % day−1 in a cultivation cycle of 28 days. Based on the space demand of the production system, this results in fresh mass productivity of 3.0 kg m−2, respectively, of 1.1 kg m−2 per year. Also considering the ratio of biomass to energy input amounting to 2.76 g kWh−1, significant future improvements of the developed photobioreactor system should include the optimization of growth parameters, and the reduction of the system’s overall energy demand.
Scaling of concrete due to salt frost attack is an important durability issue in moderate and cold climates. The actual damage mechanism is still not completely understood. Two recent damage theories—the glue spall theory and the cryogenic suction theory—offer plausible, but conflicting explanations for the salt frost scaling mechanism. The present study deals with the cryogenic suction theory, which assumes that freezing concrete can take up unfrozen brine from a partly frozen deicing solution during salt frost attack. According to the model hypothesis, the resulting saturation of the concrete surface layer intensifies the ice formation in this layer and causes salt frost scaling. In this study an experimental technique was developed that makes it possible to quantify to which extent brine uptake can increase ice formation in hardened cement paste (used as a model material for concrete). The experiments were carried out with low temperature differential scanning calorimetry, where specimens were subjected to freeze–thaw cycles while being in contact with NaCl brine. Results showed that the ice content in the specimens increased with subsequent freeze–thaw cycles due to the brine uptake at temperatures below 0 °C. The ability of the hardened cement paste to bind chlorides from the absorbed brine at the same time affected the freezing/melting behavior of the pore solution and the magnitude of the ice content.
The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase-field fracture method. The nonlocal governing equations are expressed as an integral form on support and dual-support. The first example shows that the nonlocal elasticity has the same form as dual-horizon non-ordinary state-based peridynamics. The derivation is simple and general and it can convert efficiently many local physical models into their corresponding nonlocal forms. In addition, a criterion based on the instability of the nonlocal gradient is proposed for the fracture modelling in linear elasticity. Several numerical examples are presented to validate nonlocal elasticity and the nonlocal thin plate.
Within the scope of literature, the influence of openings within the infill walls that are bounded by a reinforced concrete frame and excited by seismic drift forces in both in- and out-of-plane direction is still uncharted. Therefore, a 3D micromodel was developed and calibrated thereafter, to gain more insight in the topic. The micromodels were calibrated against their equivalent physical test specimens of in-plane, out-of-plane drift driven tests on frames with and without infill walls and openings, as well as out-of-plane bend test of masonry walls. Micromodels were rectified based on their behavior and damage states. As a result of the calibration process, it was found that micromodels were sensitive and insensitive to various parameters, regarding the model’s behavior and computational stability. It was found that, even within the same material model, some parameters had more effects when attributed to concrete rather than on masonry. Generally, the in-plane behavior of infilled frames was found to be largely governed by the interface material model. The out-of-plane masonry wall simulations were governed by the tensile strength of both the interface and masonry material model. Yet, the out-of-plane drift driven test was governed by the concrete material properties.
Antimicrobial resistance (AMR) is identified by the World Health Organization (WHO) as one of the top ten threats to public health worldwide. In addition to public health, AMR also poses a major threat to food security and economic development. Current sanitation systems contribute to the emergence and spread of AMR and lack effective AMR mitigation measures. This study assesses source separation of blackwater as a mitigation measure against AMR. A source-separation-modified combined sanitation system with separate collection of blackwater and graywater is conceptually described. Measures taken at the source, such as the separate collection and discharge of material flows, were not considered so far on a load balance basis, i.e., they have not yet been evaluated for their effectiveness. The sanitation system described is compared with a combined system and a separate system regarding AMR emissions by means of simulation. AMR is represented in the simulation model by one proxy parameter each for antibiotics (sulfamethoxa-zole), antibiotic-resistant bacteria (extended-spectrum beta-lactamase E. Coli), and antibiotic re-sistance genes (blaTEM). The simulation results suggest that the source-separation-based sanitation system reduces emissions of antibiotic-resistant bacteria and antibiotic resistance genes into the aquatic environment by more than six logarithm steps compared to combined systems. Sulfa-methoxazole emissions can be reduced by 75.5% by keeping blackwater separate from graywater and treating it sufficiently. In summary, sanitation systems incorporating source separation are, to date, among the most effective means of preventing the emission of AMR into the aquatic envi-ronment.
Electric trains are considered one of the most eco-friendly and safest means of transportation. Catenary poles are used worldwide to support overhead power lines for electric trains. The performance of the catenary poles has an extensive influence on the integrity of the train systems and, consequently, the connected human services. It became a must nowadays to develop SHM systems that provide the instantaneous status of catenary poles in- service, making the decision-making processes to keep or repair the damaged poles more feasible. This study develops a data-driven, model-free approach for status monitoring of cantilever structures, focusing on pre-stressed, spun-cast ultrahigh-strength concrete catenary poles installed along high-speed train tracks. The pro-posed approach evaluates multiple damage features in an unfied damage index, which leads to straightforward interpretation and comparison of the output. Besides, it distinguishes between multiple damage scenarios of the poles, either the ones caused by material degradation of the concrete or by the cracks that can be propagated during the life span of the given structure. Moreover, using a logistic function to classify the integrity of structure avoids the expensive learning step in the existing damage detection approaches, namely, using the modern machine and deep learning methods. The findings of this study look very promising when applied to other types of cantilever structures, such as the poles that support the power transmission lines, antenna masts, chimneys, and wind turbines.
Personalized ventilation (PV) is a mean of delivering conditioned outdoor air into the breathing zone of the occupants. This study aims to qualitatively investigate the personalized flows using two methods of visualization: (1) schlieren imaging using a large schlieren mirror and (2) thermography using an infrared camera. While the schlieren imaging was used to render the velocity and mass transport of the supplied flow, thermography was implemented to visualize the air temperature distribution induced by the PV. Both studies were conducted using a thermal manikin to simulate an occupant facing a PV outlet. As a reference, the flow supplied by an axial fan and a cased axial fan was visualized with the schlieren system as well and compared to the flow supplied by PV. Schlieren visualization results indicate that the steady, low-turbulence flow supplied by PV was able to penetrate the thermal convective boundary layer encasing the manikin's body, providing clean air for inhalation. Contrarily, the axial fan diffused the supplied air over a large target area with high turbulence intensity; it only disturbed the convective boundary layer rather than destroying it. The cased fan supplied a flow with a reduced target area which allowed supplying more air into the breathing zone compared to the fan. The results of thermography visualization showed that the supplied cool air from PV penetrated the corona-shaped thermal boundary layer. Furthermore, the supplied air cooled the surface temperature of the face, which indicates the large impact of PV on local thermal sensation and comfort.
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.
The growing complexity of modern practical problems puts high demand on mathematical modelling. Given that various models can be used for modelling one physical phenomenon, the role of model comparison and model choice is becoming particularly important. Methods for model comparison and model choice typically used in practical applications nowadays are computationbased, and thus time consuming and computationally costly. Therefore, it is necessary to develop other approaches to working abstractly, i.e., without computations, with mathematical models. An abstract description of mathematical models can be achieved by the help of abstract mathematics, implying formalisation of models and relations between them. In this paper, a category theory-based approach to mathematical modelling is proposed. In this way, mathematical models are formalised in the language of categories, relations between the models are formally defined and several practically relevant properties are introduced on the level of categories. Finally, an illustrative example is presented, underlying how the category-theory based approach can be used in practice. Further, all constructions presented in this paper are also discussed from a modelling point of view by making explicit the link to concrete modelling scenarios.
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.
Durch internationale Fluchtbewegungen über die sogenannte Balkanroute bildete sich in Serbiens Hauptstadt Belgrad in den letzten Jahren ein sogenannter Refugee District heraus. Im Kontext von Migration und Flucht werden dabei zahlreiche Spannungsfelder auf unterschiedlichen räumlichen und politischen Ebenen sichtbar. Für Flüchtende kreieren diese eine Situation, die von Stillstand, Ausweglosigkeit, Kontrolle, Gefahr und Verdrängung geprägt ist. Allerdings führen die Vielschichtigkeit und die Diversität unterschiedlicher Akteur*innen, die bezüglich der Situation von Flüchtenden auf der Balkanroute wirkmächtig sind, auch zu Nischen, Widerständigkeiten und der Möglichkeit (neuer) Allianzen. Auf diese Weise entsteht eine kollektive Praktik der Nicht-Bewegung im Widerstand gegen die Unterdrückung und für globale Bewegungsfreiheit.
In this paper we present a theoretical background for a coupled analytical–numerical approach to model a crack propagation process in two-dimensional bounded domains. The goal of the coupled analytical–numerical approach is to obtain the correct solution behaviour near the crack tip by help of the analytical solution constructed by using tools of complex function theory and couple it continuously with the finite element solution in the region far from the singularity. In this way, crack propagation could be modelled without using remeshing. Possible directions of crack growth can be calculated through the minimization of the total energy composed of the potential energy and the dissipated energy based on the energy release rate. Within this setting, an analytical solution of a mixed boundary value problem based on complex analysis and conformal mapping techniques is presented in a circular region containing an arbitrary crack path. More precisely, the linear elastic problem is transformed into a Riemann–Hilbert problem in the unit disk for holomorphic functions. Utilising advantages of the analytical solution in the region near the crack tip, the total energy could be evaluated within short computation times for various crack kink angles and lengths leading to a potentially efficient way of computing the minimization procedure. To this end, the paper presents a general strategy of the new coupled approach for crack propagation modelling. Additionally, we also discuss obstacles in the way of practical realisation of this strategy.
In this work, extensive reactive molecular dynamics simulations are conducted to analyze the nanopore creation by nano-particles impact over single-layer molybdenum disulfide (MoS2) with 1T and 2H phases. We also compare the results with graphene monolayer. In our simulations, nanosheets are exposed to a spherical rigid carbon projectile with high initial velocities ranging from 2 to 23 km/s. Results for three different structures are compared to examine the most critical factors in the perforation and resistance force during the impact. To analyze the perforation and impact resistance, kinetic energy and displacement time history of the projectile as well as perforation resistance force of the projectile are investigated.
Interestingly, although the elasticity module and tensile strength of the graphene are by almost five times higher than those of MoS2, the results demonstrate that 1T and 2H-MoS2 phases are more resistive to the impact loading and perforation than graphene. For the MoS2nanosheets, we realize that the 2H phase is more resistant to impact loading than the 1T counterpart.
Our reactive molecular dynamics results highlight that in addition to the strength and toughness, atomic structure is another crucial factor that can contribute substantially to impact resistance of 2D materials. The obtained results can be useful to guide the experimental setups for the nanopore creation in MoS2or other 2D lattices.
Discrete function theory in higher-dimensional setting has been in active development since many years. However, available results focus on studying discrete setting for such canonical domains as half-space, while the case of bounded domains generally remained unconsidered. Therefore, this paper presents the extension of the higher-dimensional function theory to the case of arbitrary bounded domains in Rn. On this way, discrete Stokes’ formula, discrete Borel–Pompeiu formula, as well as discrete Hardy spaces for general bounded domains are constructed. Finally, several discrete Hilbert problems are considered.
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.
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.
When it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures’ models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure’s behavior and model parameters without the need of preliminary measurements for model updating.
Few studies have investigated how search behavior affects complex writing tasks. We analyze a dataset of 150 long essays whose authors searched the ClueWeb09 corpus for source material, while all querying, clicking, and writing activity was meticulously recorded. We model the effect of search and writing behavior on essay quality using path analysis. Since the boil-down and build-up writing strategies identified in previous research have been found to affect search behavior, we model each writing strategy separately. Our analysis shows that the search process contributes significantly to essay quality through both direct and mediated effects, while the author's writing strategy moderates this relationship. Our models explain 25–35% of the variation in essay quality through rather simple search and writing process characteristics alone, a fact that has implications on how search engines could personalize result pages for writing tasks. Authors' writing strategies and associated searching patterns differ, producing differences in essay quality. In a nutshell: essay quality improves if search and writing strategies harmonize—build-up writers benefit from focused, in-depth querying, while boil-down writers fare better with a broader and shallower querying strategy.
This article is focused on the research and development of new cellulose ether derivatives as innovative superplasticizers for mortar systems. Several synthetic strategies have been pursued to obtain new compounds to study their properties on cementitious systems as new bio-based additives. The new water-soluble admixtures were synthesized using a complex carboxymethylcellulose-based backbone that was first hydrolyzed and then sulfo-ethylated in the presence of sodium vinyl sulphonate. Starting with a complex biopolymer that is widely known as a thickening agent was very challenging. Only by varying the hydrolysis times and temperatures of the reactions was achieved the aimed goal. The obtained derivatives showed different molecular weight (Mw) and anionic charges on their backbones. An improvement in shear stress and dynamic viscosity values of CEM II 42.5R cement was observed with the samples obtained with a longer time of higher temperature hydrolysis and sulfo-ethylation. Investigations into the chemical nature of the pore solution, calorimetric studies and adsorption experiments clearly showed the ability of carboxymethyl cellulose superplasticizer (CMC SP) to interact with cement grains and influence hydration processes within a 48-h time window, causing a delay in hydration reactions in the samples. The fluidity of the cementitious matrices was ascertained through slump test and preliminary studies of mechanical and flexural strength of the hardened mortar formulated with the new ecological additives yielded values in terms of mechanical properties. Finally, the computed tomography (CT) images completed the investigation of the pore network structure of hardened specimens, highlighting their promising structure porosity.
Compiling and disseminating information about incidents and disasters are key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: (1) impact assessment and verification of model predictions, (2) narrative generation, (3) recruiting citizen volunteers, (4) supporting weakly institutionalized areas, (5) narrowing surveillance areas, and (6) reporting triggers for periodical surveillance. Furthermore, we discuss the benefits and shortcomings of using social media data for closing information gaps related to incidents and disasters.
Entrepreneurship and start-up activities are seen as a key response to recent upheavals in the media industry: Newly founded ventures can act as important drivers for industry transformation and renewal, pioneering new products, business models, and organizational designs (e.g. Achtenhagen, 2017; Buschow & Laugemann, 2020).
In principle, media students represent a crucial population of nascent entrepreneurs: individuals who will likely become founders of start-ups (Casero-Ripollés et al., 2016). However, their willingness to start a new business is generally considered to be rather low (Goyanes, 2015), and for journalism students, the idea of innovation tends to be conservative, following traditional norms and professional standards (Singer & Broersma, 2020). In a sample of Spanish journalism students, López-Meri et al. (2020) found that one of the main barriers to entrepreneurial intentions is that students feel they lack knowledge and training in entrepreneurship.
In the last 10 years, a wide variety of entrepreneurship education courses have been set up in media departments of colleges and universities worldwide.
These programs have been designed to sensitize and prepare communications, media and journalism students to think and act entrepreneurially (e.g. Caplan et al., 2020; Ferrier, 2013; Ferrier & Mays, 2017; Hunter & Nel, 2011). Entrepreneurial competencies
and practices not only play a crucial role for start-ups, but, in imes of digital transformation, are increasingly sought after by legacy media companies as well (Küng, 2015).
At the Department of Journalism and Communication Research, Hanover University of Music, Drama and Media, Germany, we have been addressing these developments with the “Media Entrepreneurship” program. The course, established in 2013, aims to provide fundamental knowledge of entrepreneurship, as well as promoting students‘ entrepreneurial thinking and behavior. This article presents the pedagogical approach of the program and investigates learning outcomes. By outlining and evaluating the Media Entrepreneurship program, this article aims to promote good practices of entrepreneurship education in communications, media and journalism, and to reflect on the limitations of such programs.
Chemical glass frosting processes are widely used to create visual attractive glass surfaces. A commonly used frosting bath mainly contains ammonium bifluoride (NH4HF2) mixed with hydrochloric acid (HCl). The frosting process consists of several baths. Firstly, the preliminary bath to clean the object. Secondly, the frosting bath which etches the rough light scattering structure into the glass surface. Finally, the washing baths to clean the frosted object. This is where the constituents of the preceding steps accumulate and have to be filtered from the sewage. In the present contribution, phosphoric acid (H3PO4) was used as a substitute for HCl to reduce the amount of ammonium (NH4+) and chloride (Cl−) dissolved in the waste water. In combination with magnesium carbonate (MgCO3), it allows the precipitation of ammonium within the sewage as ammonium magnesium phosphate (MgNH4PO4). However, a trivial replacement of HCl by H3PO4 within the frosting process causes extensive frosting errors, such as inhomogeneous size distributions of the structures or domains that are not fully covered by these structures. By modifying the preliminary bath composition, it was possible to improve the frosting result considerably. To determine the optimal composition of the preliminary bath, a semi-automatic evaluation method has been developed. This method renders the objective comparison of the resulting surface quality possible.
Biofeedback constitutes a well-established, non-invasive method to voluntary interfere in emotional processing by means of cognitive strategies. However, treatment durations exhibit strong inter-individual variations and first successes can often be achieved only after a large number of sessions. Sham feedback constitutes a rather untapped approach by providing feedback that does not correspond to the participant’s actual state. The current study aims to gain insights into mechanisms of sham feedback processing in order to support new techniques in biofeedback therapy. We carried out two experiments and applied different types of sham feedback on skin conductance responses and pupil size changes during affective processing. Results indicate that standardized but context-sensitive sham signals based on skin conductance responses exert a stronger influence on emotional regulation compared to individual sham feedback from ongoing pupil dynamics. Also, sham feedback should forego unnatural signal behavior to avoid irritation and skepticism among participants. Altogether, a reasonable combination of stimulus features and sham feedback characteristics enables to considerably reduce the actual bodily responsiveness already within a single session.
This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.
One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel.
To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.
While Public-Private Partnership (PPP) is widely adopted across various sectors, it raises a question on its meagre utilisation in the housing sector. This paper, therefore, gauges the perspective of the stakeholders in the building industry towards the application of PPP in various building sectors together with housing. It assesses the performance reliability of PPP for housing by learning possible take-aways from other sectors. The role of key stakeholders in the industry becomes highly responsible for an informed understanding and decision-making. To this end, a two-tier investigation was conducted including surveys and expert interviews, with several stakeholders in the PPP industry in Europe, involving the public sector, private sector, consultants, as well as other community/user representatives.
The survey results demonstrated the success rate with PPPs, major factors important for PPPs such as profitability or end-user acceptability, the prevalent practices and trends in the PPP world, and the majority of support expressed in favour of the suitability of PPP for housing. The interviews added more detailed dimensions to the understanding of the PPP industry, its functioning and enabling the formation of a comprehensive outlook. The results present the perspective, approaches, and experiences of stakeholders over PPP practices, current trends and scenarios and their take on PPP in housing. It shall aid in understanding the challenges prevalent in the PPP approach for implementation in housing and enable the policymakers and industry stakeholders to make provisions for higher uptake to accelerate housing provision.
Broadband dielectric measurement methods based on vector network analyzer coupled with coaxial transmission line cell (CC) and open-ended coaxial probe (OC) are simply reviewed, by which the dielectric behaviors in the frequency range of 1 MHz to 3 GHz of two practical geomaterials are investigated. Kaolin after modified compaction with different water contents is measured by using CC. The results are consistent with previous study on standardized compacted kaolin and suggest that the dielectric properties at frequencies below 100 MHz are not only a function of water content but also functions of other soil state parameters including dry density. The hydration process of a commercial grout is monitored in real time by using OC. It is found that the time dependent dielectric properties can accurately reveal the different stages of the hydration process. These measurement results demonstrate the practicability of the introduced methods in determining dielectric properties of soft geomaterials.
The development of a hydro-mechanically coupled Coupled-Eulerian–Lagrangian (CEL) method and its application to the back-analysisof vibratory pile driving model tests in water-saturated sand is presented. The predicted pile penetration using this approachis in good agreement with the results of the model tests as well as with fully Lagrangian simulations. In terms of pore water pressure, however, the results of the CEL simulation show a slightly worse accordance with the model tests compared to the Lagrangian simulation. Some shortcomings of the hydro-mechanically coupled CEL method in case of frictional contact problems and pore fluids with high bulk modulus are discussed. Lastly, the CEL method is applied to the simulation of vibratory driving of open-profile piles under partially drained conditions to study installation-induced changes in the soil state. It is concluded that the proposed method is capable of realistically reproducing the most important mechanisms in the soil during the driving process despite its addressed shortcomings.
Die derzeitige Wohnungskrise hat eine sozial-ökologische Kernproblematik. Dabei ist die sozial ungerechte und ökologisch problematische Verteilung von Wohnfläche meist unsichtbar und wird weder in wissenschaftlichen noch in aktivistischen Kontexten ausreichend als Frage der Flächengerechtigkeit problematisiert. Denn Wohnraum und Fläche in einer Stadt sind keine endlos verfügbaren Güter: Wenn einige Menschen auf viel Raum leben, bleibt für andere Menschen weniger Fläche übrig. Und die Menschen, die am wenigstens für eine Verknappung von Wohnraum verantwortlich sind, leiden am meisten darunter. Dieser Artikel arbeitet zunächst den Begriff der Wohnflächengerechtigkeit heraus, wobei auf die Ungleichverteilung von Wohnfläche und deren gesellschaftliche Implikationen unter derzeitigen Wohnungsverteilungsmechanismen Bezug genommen wird. Anschließend wird der Verbrauch von (Wohn-)Fläche aus ökologischer Perspektive problematisiert. Der Artikel diskutiert scheinbare und transformationsorientierte Lösungs- und Handlungsansätze. Abschließend fordert er in der kritischen Stadtforschung und in aktivistischen Kontexten eine stärkere Debatte um eine Wohnflächengerechtigkeit, deren Verwirklichung gleichermaßen eine soziale wie ökologische Dimension hat.
Matthias Bernt und Andrej Holm weisen zu Recht darauf hin, dass es einer Forschung zu ostdeutschen Städten als konzeptionell eigenständigem Feld bedarf, die die spezifische Verräumlichung des tiefgreifenden gesellschaftlichen Transformationsprozesses nach 1990 ins Zentrum stellt. Dabei betrachten sie insbesondere das Feld des Wohnens als produktiv, um Kenntnis über die Struktur und Wirkung dieses Prozesses zu erlangen. Allerdings bleiben sie vage dabei, wie eine solche spezifisch auf Ostdeutschland gerichtete Wohnungsforschung zu konzipieren wäre und in welcher Weise die Besonderheiten und Parallelitäten ostdeutscher Entwicklungen zu den Transformationen von Wohnungs- und Stadtentwicklungspolitik in Westdeutschland, aber auch international, in Bezug zu setzen wären.
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.
Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.
Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.
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.
For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated.
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.
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.
This paper reports the formation and structure of fast setting geopolymers activated by using three sodium silicate solutions with different modules (1.6, 2.0 and 2.4) and a berlinite-type aluminum orthophosphate. By varying the concentration of the aluminum orthophosphate, different Si/Al-ratios were established (6, 3 and 2). Reaction kinetics of binders were determined by isothermal calorimetric measurements at 20 °C. X-ray diffraction analysis as well as nuclear magnetic resonance (NMR) measurements were performed on binders to determine differences in structure by varying the alkalinity of the sodium silicate solutions and the Si/Al-ratio. The calorimetric results indicated that the higher the alkalinity of the sodium silicate solution, the higher the solubility and degree of conversion of the aluminum orthophosphate. The results of X-ray diffraction and Rietveldt analysis, as well as the NMR measurements, confirmed the assumption of the calorimetric experiments that first the aluminum orthophosphate was dissolved and then a polycondensation to an amorphous aluminosilicate network occurred. The different amounts of amorphous phases formed as a function of the alkalinity of the sodium silicate solution, indicate that tetrahydroxoaluminate species were formed during the dissolution of the aluminum orthophosphate, which reduce the pH value. This led to no further dissolution of the aluminum orthophosphate, which remained unreacted.
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.
The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2%, 10%, 50%, and 68% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province’s design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location.
A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
(2020)
Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.
Neuartige Sanitärsysteme zielen auf eine ressourcenorientierte Verwertung von Abwasser ab. Erreicht werden soll dies durch die separate Erfassung von Abwasserteilströmen. In den Fachöffentlichkeiten der Wasserwirtschaft und Raumplanung werden neuartige Sanitärsysteme als ein geeigneter Ansatz für die zukünftige
Sicherung der Abwasserentsorgung in ländlichen Räumen betrachtet. Die Praxistauglichkeit dieser Systeme wurde zwar in Forschungsprojekten nachgewiesen, bisher erschweren jedoch für Abwasserentsorger vielfältige Risiken die Einführung einer ressourcenorientierten Abwasserbewirtschaftung. Ausgehend von einer Untersuchung der Kontexte bei der Umsetzung eines neuartigen Sanitärsystems im ländlichen Raum Thüringens wird in diesem Beitrag der Frage nachgegangen, wie auf Landesebene mit dem abwasserwirtschaftlichen Instrumentarium die Einführung von ressourcenorientierten Systemansätzen unterstützt werden kann. Zentrale Elemente des Beitrags sind die Darstellung der wesentlichen Transformationsrisiken in Bezug auf die Einführung innovativer Lösungsansätze, eine Erläuterung der spezifischen abwasserwirtschaftlichen Instrumente sowie die Darlegung von Steuerungsansätzen,mit denen die Einführung von neuartigen Sanitärsystemen gefördert werden kann. Im Ergebnis wird die Realisierbarkeit von neuartigen Sanitärsystemen durch den strategischen Einsatz des Instrumentariums deutlich, gleichwohl die Wasserwirtschaft durch die Erweiterung der bisherigen Systemgrenzen auf die Kooperation mit anderen Bereichen der Daseinsvorsorge angewiesen ist.
Personalisierte Lüftung (PL) kann die thermische Behaglichkeit sowie die Qualität der eingeatmeten Atemluft verbessern, in dem jedem Arbeitsplatz Frischluft separat zugeführt wird. In diesem Beitrag wird die Wirkung der PL auf die thermische Behaglichkeit der Nutzer unter sommerlichen Randbedingungen untersucht. Hierfür wurden zwei Ansätze zur Bewertung des Kühlungseffekts der PL untersucht: basierend auf (1) der äquivalenten Temperatur und (2) dem thermischen Empfinden. Grundlage der Auswertung sind in einer Klimakammer gemessene sowie numerisch simulierte Daten. Vor der Durchführung der Simulationen wurde das numerische Modell zunächst anhand der gemessenen Daten validiert. Die Ergebnisse zeigen, dass der Ansatz basierend auf dem thermischen Empfinden zur Evaluierung des Kühlungseffekts der PL sinnvoller sein kann, da bei diesem die komplexen physiologischen Faktoren besser berücksichtigt werden.
Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.
This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.
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.
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.