Kontrastive Syntax im Deutschen und Chinesischen

Die vorliegende Arbeit ist das Ergebnis einer ausführlichen Auseinandersetzung mit den Unterschieden der deutsch-chinesischen Satzgliedstellungen. Durch die Analyse der vergleichenden Syntax wird das Grammatikphänomen auch reflektiert. Die Arbeit sollte sich nicht nur auf die Theorie der Grammatik und des Satzbaus beschränken, sondern auch Differenzen der chinesisch-deutschen Denkweise berücksichtigen, die auch durch den Vergleich der Syntax ersichtlich werden. Die Arbeit ist so aufgebaut, dass das deutsche Grammatikphänomen immer am Anfang der jeweiligen Kapitel erläutert wird, danach folgen die deutschen Beispiele und chinesischen Übersetzungen, von denen die vom deutschen Original abweichenden Übersetzungen besonderes erklärt werden. Damit wird klargestellt, warum sich manches nicht direkt übersetzen lässt und auf welche Art und Weise bestimmte grammatische Formulierungen ins Chinesische übertragen werden können. Der erste Teil der Arbeit geht auf den Einfachsatz ein. Die einzelnen Satzglieder beider Sprachen, wie Subjekt, Prädikat und Objekt, werden durch einen Pfeil markiert, wodurch die Satzstellung einfach verglichen werden kann. Die speziellen grammatischen Phänomene vom Chinesischen werden entweder durch Zeichenschattierung oder durch Unterstreichen gekennzeichnet. Damit soll einerseits eine klare Wahrnehmung der Unterschiede erzielt werden, andererseits auch eine leichtere Nachvollziehbarkeit für diejenigen sicherstellen, die der chinesischen Schriftzeichen nicht mächtig sind. In diesem Teil werden die speziellen chinesischen Satztypen und ihre deutschen Übersetzungen dargestellt. Die Besonderheiten können für den Sprachvergleich einen anderen Blickwinkel bringen. In den einzelnen Kapiteln werden die Verwendung des Grammatikphänomens und seine Entsprechung in der gegenübergestellten Sprache deutlich beschrieben. Der zweite Teil geht vom zusammengesetzten Satz aus. Die Klassifikation des zusammengesetzten Satzes in zwei große Kategorien, die koordinative und die subordinative Verbindung, wird schematisch erläutert. Die Konjunktionen im Deutschen wie im Chinesischen werden markiert. Beim zusammengesetzten Satz werden nicht nur die Stellungen der einzelnen Glieder des Satzes, sondern auch die Verwendungen und die Besonderheiten der Konjunktion zum Ausdruck gebracht. Am Ende des jeweiligen Kapitels wird eine kurze Zusammenfassung erstellt, die noch einmal die wichtigsten Punkte anführt und den Inhalt besser zu verstehen hilft. Durch die Analyse der Syntax der beiden Sprachen wird erklärt, warum die Reihenfolge des Satzes im Deutschen flexibel ist und warum die chinesische Reihenfolge so streng ist. Dies kann als Grundlage für die Erforschung der chinesisch-deutschen kontrastiven Syntax betrachtet werden. China hat sich in den Bereichen Wirtschaft, Technik und Wissenschaft schnell entwickelt, sodass der chinesischen Sprache immer mehr Beachtung geschenkt wird. Durch die Arbeit soll sich für Sprachwissenschaftler ein neuer Blickwinkel zur Erforschung des Sprachvergleichs zweier ganz unterschiedlicher Sprachfamilien eröffnen. Es wird auch erwünscht, dass die Arbeit die Kenntnisse der chinesischen Deutschlernenden und deutschen Chinesischlernenden vertieft und die Verständigung in der jeweils anderen Sprache erleichtert

Cenosphere formation and combustion characteristics of single droplets of vacuum residual oils

The ignition, combustion characteristics, and cenosphere formation of single droplets combustion of four vacuum residues (VRs) from different refineries with various asphaltene contents were studied experimentally. The single droplets of VRs were suspended at the tip of a silicon carbide fiber and heated in air at temperatures of 973 and 1023 K, respectively, in an electrically heated tube furnace. The ignition and combustion behavior of the VRs were recorded using a CCD camera, which enabled the determination of droplet size, ignition delay time, flame duration, and cenosphere size. The effect of initial droplet size, gas temperature, and asphaltene content on the ignition delay time, flame duration, cenosphere morphology, and particle size were investigated. The whole ignition and combustion process of single droplets of the VRs consisted of five stages in succession: (1) pre-ignition, mainly involving the evaporation of highly volatile components from the droplet surface; (2) steady combustion of fuel vapors evaporated from the droplet surface; (3) splashing combustion of fuel vapors evaporated from droplet interior; (4) disruptive combustion due to thermal decomposition of asphaltene; and (5) solid residue ignition and combustion. A visible and sooty flame was formed upon ignition and lasted during stages 2–4. The droplet size increased sharply in the stage 4 due to the thermal decomposition of asphaltene, which was more profound for VRs with higher asphaltene content and at higher gas temperatures. The ignition delay time increased with increasing initial droplet size and gas temperature but varied little as the asphaltene content in the VRs increased, suggesting that the ignition process of VRs was controlled by the vaporization of high volatile components on the droplet surface. The thermal decomposition of asphaltene produced solid residue, which was in the form of a cenosphere with the shell thickness being ca. 20 μm and a number of blowholes presented in the shell. The VRs with higher asphaltene content had more and bigger blowholes. The ratio of cenosphere particle size to initial droplet size is independent of the initial droplet size but almost increased linearly with the asphaltene content in the VRs

Graph-PHPA: graph-based proactive horizontal pod autoscaling for microservices using LSTM-GNN

Microservice-based architecture has become prevalent for cloud-native applications. With an increasing number of applications being deployed on cloud platforms every day leveraging this architecture, more research efforts are required to understand how different strategies can be applied to effectively manage various cloud resources at scale. A large body of research has deployed automatic resource allocation algorithms using reactive and proactive autoscaling policies. However, there is still a gap in the efficiency of current algorithms in capturing the important features of microservices from their architecture and deployment environment, for example, lack of consideration of graphical dependency. To address this challenge, we propose Graph-PHPA, a graph-based proactive horizontal pod autoscaling strategy for allocating cloud resources to microservices leveraging long short-term memory (LSTM) and graph neural network (GNN) based prediction methods. We evaluate the performance of Graph-PHPA using the Bookinfo microservices deployed in a dedicated testing environment with real-time workloads generated based on realistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing it with the rule-based resource allocation scheme in Kubernetes as our baseline. Extensive experiments have been implemented and our results illustrate the superiority of our proposed approach in resource savings over the reactive rule-based baseline algorithm in different testing scenarios

A Survey on graph neural networks for microservice-based cloud applications

Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice architecture from prototype design to large-scale service deployment. To appreciate the big picture of this emerging trend, we provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications. To begin, we identify the key areas in which GNNs are applied, and then we review in detail how GNNs can be designed to address the challenges in specific areas found in the literature. Finally, we outline potential research directions where GNN-based solutions can be further applied. Our research shows the popularity of leveraging convolutional graph neural networks (ConGNNs) for microservice-based applications in the current design of cloud systems and the emerging area of adopting spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) for more advanced studie

The influence of adatom diffusion on the formation of skyrmion lattice in sub-monolayer Fe on Ir(111)

Room temperature grown Fe monolayer (ML) on the Ir(111) single crystal substrate has attracted great research interests as nano-skyrmion lattice can form under proper growth conditions. The formation of the nanoscale skyrmion, however, appears to be greatly affected by the diffusion length of the Fe adatoms on the Ir(111) surface. We made this observation by employing spin-polarized scanning tunneling microscopy to study skyrmion formation upon systematically changing the impurity density on the substrate surface prior to Fe deposition. Since the substrate surface impurities serve as pinning centers for Fe adatoms, the eventual size and shape of the Fe islands exhibit a direct correlation with the impurity density, which in turn determines whether skyrmion can be formed. Our observation indicates that skyrmion only forms when the impurity density is below 0.006/nm2, i.e., 12 nm averaged spacing between the neighboring defects. We verify the significance of Fe diffusion length by growing Fe on clean Ir(111) substrate at low temperature of 30 K, where no skyrmion was observed to form. Our findings signify the importance of diffusion of Fe atoms on the Ir(111) substrate, which affects the size, shape and lattice perfection of the Fe islands and thus the formation of skyrmion lattice

Creation of nano-skyrmion lattice in Fe/Ir(111) system using voltage pulse

Magnetic ultrathin films grown on heavy metal substrates often exhibit rich spin structures due to the competition between various magnetic interactions such as Heisenberg exchange, Dzyaloshinskii-Moriya interaction and higher-order spin interactions. Here we employ spin-polarized scanning tunneling microscopy to study magnetic nano-skyrmion phase in Fe monolayer grown on Ir(111) substrate. Our observations show that the formation of nano-skyrmion lattice in the Fe/Ir(111) system depends sensitively on the growth conditions and various non-skyrmion spin states can be formed. Remarkably, the application of voltage pulses between the tip and the sample can trigger a non-skyrmion to skyrmion phase transition. The fact that nano-skyrmions can be created using voltage pulse indicates that the balance between the competing magnetic interactions can be affected by an external electric field, which is highly useful to design skyrmion-based spintronic devices with low energy consumption

Learning World Models with Identifiable Factorization

Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines

Direct numerical simulation of packed and monolith syngas catalytic combustors for micro electrical mechanical systems

In this work, a catalytic combustor for micro electrical mechanical system for syngas was designed and analysed using Direct Numerical Simulation (DNS) in conjunction with finite rate chemistry. The effect of catalyst (platinum (Pt), palladium (Pd), palladium oxide (PdO), and rhodium (Rh)), bed type (packed with twelve catalyst shapes and four catalyst monolith), shapes (packed: cylinder, hollow cylinder, four cylinder, single cylinder, single cylinder, cross-webb, grooved, pall-ring, hexagonal, berl-saddle, cube, intalox-saddle, and sphere, monolith: triangular, rectangular, hexagonal, and circular), and operating conditions (inlet temperature and velocity, fuel/air ratio, different concentrations CH4-H2-CO) on combustion efficiency and pressure drop were studied using different parameters (combustion efficiency (η), pressure drop, effectiveness factor (Ψ), and fuel conversions (H2 and CH4 conversions)). Analysis under different operating conditions reveals that the designed combustor can operate effectively with syngas of varying compositions with a high combustion efficiency of over 85%. Combustion mainly takes place on the surface of the catalyst without gas phase reaction with pressure drops between 18 Pa and 155 Pa. The intalox saddle shape catalysts resulted in the bed effectiveness factor 0.93.1 The Damköhler for hydroxyl radicals (OH) over the entire length of the reactor is uniformly distributed and well below 3, suggesting uniform combustion