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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.
According to Eurocode, the computation of bending strength for steel cantilever beams is a straightforward process. The approach is based on an Ayrton-Perry formula adaptation of buckling curves for steel members in compression, which involves the computation of an elastic critical buckling load for considering the instability. NCCI documents offer a simplified formula to determine the critical bending moment for cantilevers beams with symmetric cross-section. Besides the NCCI recommendations, other approaches, e.g. research literature or Finite-Element-Analysis, may be employed to determine critical buckling loads. However, in certain cases they render different results. Present paper summarizes and compares the abovementioned analytical and numerical approaches for determining critical loads and it exemplarily analyses corresponding cantilever beam capacities using numerical approaches based on plastic zones theory (GMNIA).
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.
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.
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.
Global structural analyses in civil engineering are usually performed considering linear-elastic material behavior. However, for steel structures, a certain degree of plasticization depending on the member classification may be considered. Corresponding plastic analyses taking material nonlinearities into account are effectively realized using numerical methods. Frequently applied finite elements of two and three-dimensional models evaluate the plasticity at defined nodes using a yield surface, i.e. by a yield condition, hardening rule, and flow rule. Corresponding calculations are connected to a large numerical as well as time-consuming effort and they do not rely on the theoretical background of beam theory, to which the regulations of standards mainly correspond. For that reason, methods using beam elements (one-dimensional) combined with cross-sectional analyses are commonly applied for steel members in terms of plastic zones theories. In these approaches, plasticization is in general assessed by means of axial stress only. In this paper, more precise numerical representation of the combined stress states, i.e. axial and shear stresses, is presented and results of the proposed approach are validated and discussed.
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.
Modern cryptography has become an often ubiquitous but essential part of our daily lives. Protocols for secure authentication and encryption protect our communication with various digital services, from private messaging, online shopping, to bank transactions or exchanging sensitive information. Those high-level protocols can naturally be only as secure as the authentication or encryption schemes underneath. Moreover, on a more detailed level, those schemes can also at best inherit the security of their underlying primitives. While widespread standards in modern symmetric-key cryptography, such as the Advanced Encryption Standard (AES), have shown to resist analysis until now, closer analysis and design of related primitives can deepen our understanding.
The present thesis consists of two parts that portray six contributions: The first part considers block-cipher cryptanalysis of the round-reduced AES, the AES-based tweakable block cipher Kiasu-BC, and TNT. The second part studies the design, analysis, and implementation of provably secure authenticated encryption schemes.
In general, cryptanalysis aims at finding distinguishable properties in the output distribution. Block ciphers are a core primitive of symmetric-key cryptography which are useful for the construction of various higher-level schemes, ranging from authentication, encryption, authenticated encryption up to integrity protection. Therefore, their analysis is crucial to secure cryptographic schemes at their lowest level. With rare exceptions, block-cipher cryptanalysis employs a systematic strategy of investigating known attack techniques. Modern proposals are expected to be evaluated against these techniques. The considerable effort for evaluation, however, demands efforts not only from the designers but also from external sources.
The Advanced Encryption Standard (AES) is one of the most widespread block ciphers nowadays. Therefore, it is naturally an interesting target for further analysis. Tweakable block ciphers augment the usual inputs of a secret key and a public plaintext by an additional public input called tweak. Among various proposals through the previous decade, this thesis identifies Kiasu-BC as a noteworthy attempt to construct a tweakable block cipher that is very close to the AES. Hence, its analysis intertwines closely with that of the AES and illustrates the impact of the tweak on its security best. Moreover, it revisits a generic tweakable block cipher Tweak-and-Tweak (TNT) and its instantiation based on the round-reduced AES.
The first part investigates the security of the AES against several forms of differential cryptanalysis, developing distinguishers on four to six (out of ten) rounds of AES. For Kiasu-BC, it exploits the additional freedom in the tweak to develop two forms of differential-based attacks: rectangles and impossible differentials. The results on Kiasu-BC consider an additional round compared to attacks on the (untweaked) AES. The authors of TNT had provided an initial security analysis that still left a gap between provable guarantees and attacks. Our analysis conducts a considerable step towards closing this gap. For TNT-AES - an instantiation of TNT built upon the AES round function - this thesis further shows how to transform our distinguisher into a key-recovery attack.
Many applications require the simultaneous authentication and encryption of transmitted data. Authenticated encryption (AE) schemes provide both properties. Modern AE schemes usually demand a unique public input called nonce that must not repeat. Though, this requirement cannot always be guaranteed in practice. As part of a remedy, misuse-resistant and robust AE tries to reduce the impact of occasional misuses. However, robust AE considers not only the potential reuse of nonces. Common authenticated encryption also demanded that the entire ciphertext would have to be buffered until the authentication tag has been successfully verified. In practice, this approach is difficult to ensure since the setting may lack the resources for buffering the messages. Moreover, robustness guarantees in the case of misuse are valuable features.
The second part of this thesis proposes three authenticated encryption schemes: RIV, SIV-x, and DCT. RIV is robust against nonce misuse and the release of unverified plaintexts. Both SIV-x and DCT provide high security independent from nonce repetitions. As the core under SIV-x, this thesis revisits the proof of a highly secure parallel MAC, PMAC-x, revises its details, and proposes SIV-x as a highly secure authenticated encryption scheme. Finally, DCT is a generic approach to have n-bit secure deterministic AE but without the need of expanding the ciphertext-tag string by more than n bits more than the plaintext.
From its first part, this thesis aims to extend the understanding of the (1) cryptanalysis of round-reduced AES, as well as the understanding of (2) AES-like tweakable block ciphers. From its second part, it demonstrates how to simply extend known approaches for (3) robust nonce-based as well as (4) highly secure deterministic authenticated encryption.
Utilizing Modern FIB/SEM Technology and EDS for 3D Imaging of Hydrated Alite and its Pore Space
(2021)
The exploration of cementitious materials using scanning electron microscopes (SEM) is mainly done using fractured or polished surfaces. This leads to high-resolution 2D-images that can be combined using EDX and EBSD to unveil details of the microstructure and composition of materials. Nevertheless, this does not provide a quantitative insight into the three-dimensional fine structure of for example C-S-H phases.
The focused ion beam (FIB) technology can cut a block of material in thin layers of less than 10 nm. This gives us a volume of 1000 μm³ with a voxel resolution of down to 4 x 4 x 10 nm³. The results can be combined with simultaneously acquired EDX data to improve image segmentation. Results of the investigation demonstrate that it is possible to obtain close-to-native 3D-visualisation of the spatial distribution of unreacted C3S, C-S-H and CH. Additionally, an optimized preparation method allows us to quantify the fine structure of C-S-H phases (length, aspect ratio, …) and the pore space.
Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of
the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected
Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPSIBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models,
empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.