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The "Stapelhaus": Experimental building project on the campus of the Bauhaus-Universität Weimar
(2020)
The project is a cooperation between the bauhaus.ifex and MFPA Weimar and is intended to develop step by step as an experimental student village. Special focus is given to sustainability and construction using different building materials. For the construction of the first room module, CemCel was chosen as a new, lightweight and fibre-based building material.
Diese Dissertation beschäftigt sich mit Kunstwerken, die das alltägliche Ding in den Blick nehmen. Nährboden dieser Kunstform sind die soziokulturellen Entwicklungen des 20. Jahrhunderts, mit denen wesentliche Veränderungen hinsichtlich des Verhältnisses von Mensch und Ding einhergingen.
Daraus resultierte eine allgemeine künstlerische Zuwendung zu den Dingen und eine einzigartige Kulmination aus verschiedenartigen Auseinandersetzungen mit ihnen als kunstfähige Gegenstände, über die sich die neue Dingwelt erschlossen wurde und deren Kunstwerke einen Spiegel dieser Entwicklungen darstellen.
Die Dissertation stellt ebenfalls die Dinge selbst in den Fokus. Vier Aspekte von Dingen (Materialität, Funktionalität, Repräsentationalität und Relationalität) werden gesondert ins Auge gefasst und in den theoretischen Diskurs des 20. Jahrhunderts eingeordnet, um sie als Teil der gelebten Realität besser zu verstehen, von der sich der ästhetische Blick nicht trennen lässt. Anhand der künstlerischen Positionen von Robert Rauschenberg, Christo und Jeanne-Claude, Daniel Spoerri und Arman sowie Claes Oldenburg werden die verschiedenen Aspekte der Dinge näher betrachtet und analysiert, wie diese speziell in den Kunstwerken thematisiert werden und welche Relevanz sie für deren Rezeptionserfahrung haben.
Die Korrelation dieser beiden Ebenen - die Dinge als konstitutiver Bestandteil im sozialen Raum und die Dinge als Elemente in Kunstwerken -, die im Fokus der vorliegenden Untersuchung steht, ermöglicht es, die künstlerische Zuwendung zu den Dingen in den 1960er-Jahren neu einzuordnen. Darüber hinaus wird dadurch ein differenziertes Bild von der Kunst dieser Zeit sowie den Dingen in der Kunst im Allgemeinen gezeichnet.
Synergistic Framework for Analysis and Model Assessment in Bridge Aerodynamics and Aeroelasticity
(2020)
Wind-induced vibrations often represent a major design criterion for long-span bridges. This work deals with the assessment and development of models for aerodynamic and aeroelastic analyses of long-span bridges.
Computational Fluid Dynamics (CFD) and semi-analytical aerodynamic models are employed to compute the bridge response due to both turbulent and laminar free-stream. For the assessment of these models, a comparative methodology is developed that consists of two steps, a qualitative and a quantitative one. The first, qualitative, step involves an extension
of an existing approach based on Category Theory and its application to the field of bridge aerodynamics. Initially, the approach is extended to consider model comparability and completeness. Then, the complexity of the CFD and twelve semi-analytical models are evaluated based on their mathematical constructions, yielding a diagrammatic representation of model quality.
In the second, quantitative, step of the comparative methodology, the discrepancy of a system response quantity for time-dependent aerodynamic models is quantified using comparison metrics for time-histories. Nine metrics are established on a uniform basis to quantify the discrepancies in local and global signal features that are of interest in bridge aerodynamics. These signal features involve quantities such as phase, time-varying frequency and magnitude content, probability density, non-stationarity, and nonlinearity.
The two-dimensional (2D) Vortex Particle Method is used for the discretization of the Navier-Stokes equations including a Pseudo-three dimensional (Pseudo-3D) extension within an existing CFD solver. The Pseudo-3D Vortex Method considers the 3D structural behavior for aeroelastic analyses by positioning 2D fluid strips along a line-like structure. A novel turbulent Pseudo-3D Vortex Method is developed by combining the laminar Pseudo-3D VPM and a previously developed 2D method for the generation of free-stream turbulence. Using analytical derivations, it is shown that the fluid velocity correlation is maintained between the CFD strips.
Furthermore, a new method is presented for the determination of the complex aerodynamic admittance under deterministic sinusoidal gusts using the Vortex Particle Method. The sinusoidal gusts are simulated by modeling the wakes of flapping airfoils in the CFD domain with inflow vortex particles. Positioning a section downstream yields sinusoidal forces that are used for determining all six components of the complex aerodynamic admittance. A closed-form analytical relation is derived, based on an existing analytical model. With this relation, the inflow particles’ strength can be related with the target gust amplitudes a priori.
The developed methodologies are combined in a synergistic framework, which is applied to both fundamental examples and practical case studies. Where possible, the results are verified and validated. The outcome of this work is intended to shed some light on the complex wind–bridge interaction and suggest appropriate modeling strategies for an enhanced design.
Städte ohne Wachstum - eine bislang kaum vorstellbare Vision. Doch Klimawandel, Ressourcenverschwendung, wachsende soziale Ungleichheiten und viele andere Zukunftsgefahren stellen das bisherige Allheilmittel Wachstum grundsätzlich infrage. Wie wollen wir heute und morgen zusammenleben? Wie gestalten wir ein gutes Leben für alle in der Stadt? Während in einzelnen Nischen diese Fragen bereits ansatzweise beantwortet werden, fehlt es noch immer an umfassenden Entwürfen und Transformationsansätzen, die eine fundamental andere, solidarische Stadt konturieren. Diesen Versuch wagt das Projekt Postwachstumsstadt.
In diesem Buch werden konzeptionelle und pragmatische Aspekte aus verschiedenen Bereichen der Stadtpolitik zusammengebracht, die neue Pfade aufzeigen und verknüpfen. Die Beiträge diskutieren städtische Wachstumskrisen, transformative Planung und Konflikte um Gestaltungsmacht. Nicht zuletzt wird dabei auch die Frage nach der Rolle von Stadtutopien neu gestellt. Dadurch soll eine längst fällige Debatte darüber angestoßen werden, wie sich notwendige städtische Wenden durch eine sozialökologische Neuorientierung vor Ort verwirklichen lassen.
In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
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.
The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.
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.
Rechargeable lithium ion batteries (LIBs) play a very significant role in power supply and storage. In recent decades, LIBs have caught tremendous attention in mobile communication, portable electronics, and electric vehicles. Furthermore, global warming has become a worldwide issue due to the ongoing production of greenhouse gases. It motivates solutions such as renewable sources of energy. Solar and wind energies are the most important ones in renewable energy sources. By technology progress, they will definitely require batteries to store the produced power to make a balance between power generation and consumption. Nowadays,rechargeable batteries such as LIBs are considered as one of the best solutions. They provide high specific energy and high rate performance while their rate of self-discharge is low.
Performance of LIBs can be improved through the modification of battery characteristics. The size of solid particles in electrodes can impact the specific energy and the cyclability of batteries. It can improve the amount of lithium content in the electrode which is a vital parameter in capacity and capability of a battery. There exist diferent sources of heat generation in LIBs such as heat produced during electrochemical reactions, internal resistance in battery. The size of electrode's electroactive particles can directly affect the produced heat in battery. It will be shown that the smaller size of solid particle enhance the thermal characteristics of LIBs.
Thermal issues such as overheating, temperature maldistribution in the battery, and thermal runaway have confined applications of LIBs. Such thermal challenges reduce the Life cycle of LIBs. As well, they may lead to dangerous conditions such as fire or even explosion in batteries. However, recent advances in fabrication of advanced materials such as graphene and carbon nanotubes with extraordinary thermal conductivity and electrical properties propose new opportunities to enhance their performance. Since experimental works are expensive, our objective is to use computational methods to investigate the thermal issues in LIBS. Dissipation of the heat produced in the battery can improve the cyclability and specific capacity of LIBs. In real applications, packs of LIB consist several battery cells that are used as the power source. Therefore, it is worth to investigate thermal characteristic of battery packs under their cycles of charging/discharging operations at different applied current rates. To remove the produced heat in batteries, they can be surrounded by materials with high thermal conductivity. Parafin wax absorbs high energy since it has a high latent heat. Absorption high amounts of energy occurs at constant temperature without phase change. As well, thermal conductivity of parafin can be magnified with nano-materials such as graphene, CNT, and fullerene to form a nano-composite medium. Improving the thermal conductivity of LIBs increase the heat dissipation from batteries which is a vital issue in systems of battery thermal management. The application of two-dimensional (2D) materials has been on the rise since exfoliation the graphene from bulk graphite. 2D materials are single-layered in an order of nanosizes which show superior thermal, mechanical, and optoelectronic properties. They are potential candidates for energy storage and supply, particularly in lithium ion batteries as electrode material. The high thermal conductivity of graphene and graphene-like materials can play a significant role in thermal management of batteries. However, defects always exist in nano-materials since there is no ideal fabrication process. One of the most important defects in materials are nano-crack which can dramatically weaken the mechanical properties of the materials. Newly synthesized crystalline carbon nitride with the stoichiometry of C3N have attracted many attentions due to its extraordinary mechanical and thermal properties. The other nano-material is phagraphene which shows anisotropic mechanical characteristics which is ideal in production of nanocomposite.
It shows ductile fracture behavior when subjected under uniaxial loadings. It is worth to investigate their thermo-mechanical properties in its pristine and defective states. We hope that the findings of our work not only be useful for both experimental and theoretical researches but also help to design advanced electrodes for LIBs.
Vor dem Hintergrund einer stetig wachsenden Nachfrage an Beton wie auch ambitionierter Reduktionsziele beim in der Zementproduktion anfallenden CO2 gelten calcinierte Tone als derzeit aussichtsreichste technische Neuerung im Bereich nachhaltiger Bindemittelkonzepte. Unter Ausnutzung ihrer Puzzolanität soll ein erheblicher Teil der Klinkerkomponente im Zement ersetzt werden, wobei der zu ihrer Aktivierung notwendige Energiebedarf vergleichsweise niedrig ist. Wesentliche Vorteile der Tone sind ihre weltweit nahezu unbegrenzte Verfügbarkeit sowie der äußerst geringe rohstoffbedingte CO2-Ausstoß während der Calcinierung. Schwierigkeiten auf dem Weg der Umsetzung bestehen allerdings in der Vielseitigkeit des Systems, welches durch eine hohe Varietät der Rohtone und des daraus folgenden thermischen Verhaltens gekennzeichnet ist. Entsprechend schwierig ist die Übertragbarkeit von Erfahrungen mit bereits etablierten calcinierten Tonen wie dem Metakaolin, der sich durch hohe Reinheit, einen aufwendigen Aufbereitungsprozess und eine entsprechend hohe Reaktivität auszeichnet. Ziel der Arbeit ist es daher, den bereits erlangten Kenntnisstand auf andere, wirtschaftlich relevante Tone zu erweitern und deren Eignung für die Anwendung im Beton herauszuarbeiten.
In einem mehrstufigen Arbeitsprogramm wurde untersucht, inwieweit großtechnisch nutzbare Tone aktivierbar sind und welche Eigenschaften sich daraus für Zement und Beton ergeben. Die dabei festgestellte Reihenfolge Kaolinit > Montmorillonit > Illit beschreibt sowohl die Reaktivität der Brennprodukte als auch umgekehrt die Höhe der optimalen Calciniertemperatur. Auch wandelt sich der Charakter der entstandenen Metaphasen in dieser Abfolge von röntgenamorph und hochreaktiv zu glasig und reaktionsträge. Trotz dieser Einordnung konnte selbst mit dem Illit eine mit Steinkohlenflugasche vergleichbare Puzzolanität festgestellt werden. Dies bestätigte sich anschließend in Parameterversuchen, bei denen die Einflüsse von Rohstoffqualität, Calcinierung, Aufbereitung und Zement hinsichtlich der Reaktivitätsausbeute bewertet wurden. Die Bandbreite der erzielbaren Qualitäten ist dabei immens und gipfelt nicht zuletzt in stark unterschiedlichen Wirkungen auf die Festbetoneigenschaften. Hier machte sich vor allem die für Puzzolane typische Porenverfeinerung bemerkbar, sodass viele von Transportvorgängen abhängige Schadmechanismen unterdrückt wurden. Andere Schadex-positionen wie der Frostangriff ließen sich durch Zusatzmaßnahmen wie dem Eintrag von Luftporen beherrschen. Zu bemängeln sind vor allem die schlechte Verarbeitbarkeit kaolinitischer Metatone wie auch die für Puzzolane stark ausgeprägte Carbonatisierungsneigung.
Wesentliches Ergebnis der Arbeit ist, dass auch Tone, die bisher als geringwertig bezüglich des Aktivierungspotentials galten, nutzbare puzzolanische Eigenschaften entwickeln können. So kann selbst ein stark verunreinigter Illit-Ton die Qualität von Flugasche erreichen. Mit stei-gendem Tonmineralgehalt sowie bei Präsens thermisch instabilerer Tonminerale wie Mont-morillonit und Kaolinit erweitert sich das Spektrum nutzbarer Puzzolanitäten bis hin zur hochreaktiven Metakaolin-Qualität. Damit lassen sich gute bis sehr gute Betoneigenschaften erzielen, sodass die Leistungsfähigkeit etablierter Kompositmaterialien erreicht wird. Somit sind die Voraussetzungen für eine umfangreiche Nutzung der erheblichen Tonmengen im Zement und Beton gegeben. Entsprechend können Tone einen effektiven Beitrag zu einer gesteigerten Nachhaltigkeit in der Baustoffproduktion weltweit leisten.
Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.
The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.
Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things
(2020)
The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model
(2020)
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
This thesis explores how cultural heritage plays a role in the development of urban identity by engaging both actively and passively with memory, i.e. remembering and forgetting. I argue that architectural heritage is a medium where specific cultural and social decisions form its way of presentation, and it reflects the values and interests of the period. By the process of remembering and forgetting, the meanings between inhabitant and object in urban environment are practiced, and the meanings are created.
To enable the research in narrative observation, cultural tourism management is chosen as the main research object, which reflects the alteration of interaction between the architectural heritage and urban identity. Identifying the role of heritage management, the definition of social resilience and the prospects of cultural heritage as a means of social resilience are addressed. Case region of the research is East Ger- many, thereby, the study examines the distinct approaches and objectives regarding heritage management under the different political systems along the German reunification process.
The framework is based on various theoretical paradigms to investigate the broad research questions: 1) What is the role of historic urban quarters in the revitalisation of East German towns? 2) How was the transition processed by cultural heritage management? 3) How did policy affect residents’ lives?
The case study is applied to macro level (city level: Gotha and Eisenach) and micro level study (object level: specific heritage sites), to analyse the performance of selective remembering and making tourist destination through giving significance to specific heritage. By means of site observations, archival research, qualitative inter- views, photographs, and discourse analysis on printed tourism materials, the study demonstrates that certain sites and characteristics of the city enable creating and focusing messages, which aids the social resilience.
Combining theory and empirical studies this thesis attempts to widen the academic discussion regarding the practice of remembering and forgetting driven by cultural heritage. The thesis argues for cultural heritage tourism as an element of social resilience and one that embraces the historic and cultural identity of the inhabitants.
Das Hauptziel der vorliegenden Arbeit war es, eine stetige Kopplung zwischen der ananlytischen und numerischen Lösung von Randwertaufgaben mit Singularitäten zu realisieren. Durch die inter-polationsbasierte gekoppelte Methode kann eine globale C0 Stetigkeit erzielt werden. Für diesen Zweck wird ein spezielle finite Element (Kopplungselement) verwendet, das die Stetigkeit der Lösung sowohl mit dem analytischen Element als auch mit den normalen CST Elementen gewährleistet.
Die interpolationsbasierte gekoppelte Methode ist zwar für beliebige Knotenanzahl auf dem Interface ΓAD anwendbar, aber es konnte durch die Untersuchung von der Interpolationsmatrix und numerische Simulationen festgestellt werden, dass sie schlecht konditioniert ist. Um das Problem mit den numerischen Instabilitäten zu bewältigen, wurde eine approximationsbasierte Kopplungsmethode entwickelt und untersucht. Die Stabilität dieser Methode wurde anschließend anhand der Untersuchung von der Gramschen Matrix des verwendeten Basissystems auf zwei Intervallen [−π,π] und [−2π,2π] beurteilt. Die Gramsche Matrix auf dem Intervall [−2π,2π] hat einen günstigeren Konditionszahl in der Abhängigkeit von der Anzahl der Kopplungsknoten auf dem Interface aufgewiesen. Um die dazu gehörigen numerischen Instabilitäten ausschließen zu können wird das Basissystem mit Hilfe vom Gram-Schmidtschen Orthogonalisierungsverfahren auf beiden Intervallen orthogonalisiert. Das orthogonale Basissystem lässt sich auf dem Intervall [−2π,2π] mit expliziten Formeln schreiben. Die Methode des konsistentes Sampling, die häufig in der Nachrichtentechnik verwendet wird, wurde zur Realisierung von der approximationsbasierten Kopplung herangezogen. Eine Beschränkung dieser Methode ist es, dass die Anzahl der Sampling-Basisfunktionen muss gleich der Anzahl der Wiederherstellungsbasisfunktionen sein. Das hat dazu geführt, dass das eingeführt Basissys-tem (mit 2 n Basisfunktionen) nur mit n Basisfunktion verwendet werden kann.
Zur Lösung diese Problems wurde ein alternatives Basissystems (Variante 2) vorgestellt. Für die Verwendung dieses Basissystems ist aber eine Transformationsmatrix M nötig und bei der Orthogonalisierung des Basissystems auf dem Intervall [−π,π] kann die Herleitung von dieser Matrix kompliziert und aufwendig sein. Die Formfunktionen wurden anschließend für die beiden Varianten hergeleitet und grafisch (für n = 5) dargestellt und wurde gezeigt, dass diese Funktionen die Anforderungen an den Formfunktionen erfüllen und können somit für die FE- Approximation verwendet werden.
Anhand numerischer Simulationen, die mit der Variante 1 (mit Orthogonalisierung auf dem Intervall [−2π,2π]) durchgeführt wurden, wurden die grundlegenden Fragen (Beispielsweise: Stetigkeit der Verformungen auf dem Interface ΓAD, Spannungen auf dem analytischen Gebiet) über-
prüft.
The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.
Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.
In einer systematischen Interpretation von Vilém Flussers Werk schlägt die Arbeit vor, Flussers Ansatz als einen medienphilosophischen zu verstehen, insofern er das „wie“ der medienphilosophischen Fragestellung in den Mittelpunkt rückt. Medien werden nicht erst dann zu einem wesentlichen Bestandteil von Flussers Philosophie, wenn er sie explizit zum Gegenstand seiner Untersuchungen der gegenwärtigen Kultur und Gesellschaft oder historischer Rückblicke macht; Denken vollzieht sich immer in Medien oder medialen Praktiken, es wird nicht nur von ihnen (mit) geprägt – ohne Medien gäbe es kein Denken und umgekehrt verändert sich Philosophie mit den (jeweils) neuen Medien. Ausgehend von Begriffen oder eher Denkfiguren, die neben dem „was“ des jeweils verhandelten Themas auch das „wie“ der Reflexion selbst adressieren, wird der „Umbruch in der Struktur des Denkens“ zugleich als Beschreibung von Medienumbrüchen verstanden – mit dem Fluchtpunkt des Sprungs in das Universum der Komputation – und als Vollzug der gegenwärtigen Veränderung der „Methode des Denkens“. Flussers (Ver)Suche einer Reflexion, die nicht mehr durch das Medium Schrift strukturiert ist, sondern sowohl alten Medien wie dem Bild – bzw. Praktiken des Abbildens, Darstellens, Einbildens usw. – als auch neuen Medien – dem Komputieren – Geltung verschafft, laufen auf eine widersprüchliche Diagnose des neuen Universums der Komputation (anders: der technischen Bilder) hinaus : eine kybermetisch inspirierte Vision der frei modellierbaren Wirklichkeit(en) einerseits und die Dystopie einer Welt, in der Apparaten Denken, Wahrnehmen und Handeln beherrschen andererseits. Die Arbeit zeigt auf, wie Flusser zu dieser Aporie der Medienreflexion – die weit über Flussers Werk hinaus virulent bleibt – gelangt und wie sie, ausgehend von seiner Figur der Geste, im Sinne einer performativen Medienreflexion gelöst werden könnte.
Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.
The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.
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.