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This report examines exporters’ challenges and possible solutions for public intervention to promote foreign trade. Based on fieldwork conducted in Georgia, we explore which policy approaches can help to stimulate Georgian exports further. Our outcomes show that exporters face substantial barriers such as navigating complex trade regulations, lack of knowledge about target markets, trade finance gaps, as well as new export promotion programs (EPPs) in competitor countries. Other upper-middle-income countries can learn from our results that exporters can significantly benefit from a comprehensive export promotion strategy combined with an ecosystem-based “team” approach. EPPs related to awareness and capacity building in Georgia should be part of this strategy, focusing on challenges such as a lack of knowledge about trade practices and international business skills. Other EPPs must help to mitigate related market failures, as information gathering is costly, and firms have no incentive to share this information with competitors. Furthermore, targeted marketing support and customer matchmaking can answer Georgian exporters’ challenges, such as lack of market access and low sector visibility. Our results also show that public intervention through financial support and risk mitigation is essential for firms with an international orientation. The high-quality, rich outcomes provide significant value for other upper-middle-income countries by exploring the example of Georgia’s contemporary circumstances in an in-depth manner based on extensive interviews and document analysis. Limitations include that our work primarily relies on qualitative data and further research could involve a quantitative study with a diverse range of sectors.
"Ad fontes!"
Francesco Petrarca (1301–1374)
In the beginning, there was an idea: the reconstruction of the first "Iron Hand" of the Franconian imperial knight Götz von Berlichingen (1480–1562). We found that with this historical prosthesis, simple actions for daily use, such as holding a wine glass, a mobile phone, a bicycle handlebar grip, a horse’s reins, or some grapes, are possible without effort. Controlling this passive artificial hand, however, is based on the help of a healthy second hand.
The growing threat posed by multidrug-resistant (MDR) pathogens, such as Klebsiella pneumoniae (Kp), represents a significant challenge in modern medicine. Traditional antibiotic therapies are often ineffective against these pathogens, leading to high mortality rates. MDR Kp infections pose a novel challenge in military medical contexts, particularly in Medical Biodefense, as they can be deliberately spread, leading to resource-intensive care in military centres. Recognizing this issue, the European Defence Agency initiated a prioritised research project in 2023 (EDF Resilience PHAGE- SGA 2023). To address this challenge, the Bundeswehr Institute of Microbiology (IMB) leads BMBF- (Federal Ministry of Education and Research) and EU-funded projects on the use of bacteriophages as adjuvant therapy alongside antibiotics. Since 2017, the IMB has isolated and characterised Kp phages, collecting over 600 isolates and optimizing their production for therapy, in compliance with the EMA (European Medicine Agency) guidelines. This involves in vitro phage genome packaging to minimize endotoxin load, reduce manufacturing costs, and shorten production times. The goal of this work was to establish MinION sequencing (Oxford Nanopore Technology) as a quick and reliable way for initial identification and characterisation of phage genomes. Especially as a quick screening method for isolated on Kp, prior to more precise but also more expensive and time consuming sequencing methods like Illumina. This characterisation is crucial for developing a personalized pipeline aimed at producing magistral or Good Manufacturing Practice (GMP) quality medicinal phage solutions tailored individually for each patient. DNA extraction methods were compared to identify suitable input DNA for sequencing purposes. Additionally, the quality of this DNA was as- sessed to determine its suitability for in vitro phage packaging, which was successfully done achieving a phage titer of 103, confirming that the DNA used for MinION sequencing could indeed be used for acellular packaging. The created genomes were annotated and compared with Illumina sequencing, revealing high similarity in all five individually tested cases. Between the generated sequences only a 4% maximal percentual difference in genome size was observed, while simultaneously showing high similarity in the actual sequence. Throughout the course of this study, a total of 645.15 GB of sequencing data were generated. In total, 38 phages were successfully characterised, with 21 phage genomes assembled and annotated, and saved in the IMB database.
In 2015, Google engineer Alexander Mordvintsev presented DeepDream as technique to visualise the feature analysis capabilities of deep neural networks that have been trained on image classification tasks. For a brief moment, this technique enjoyed some popularity among scientists, artists, and the general public because of its capability to create seemingly hallucinatory synthetic images. But soon after, research moved on to generative models capable of producing more diverse and more realistic synthetic images. At the same time, the means of interaction with these models have shifted away from a direct manipulation of algorithmic properties towards a predominance of high level controls that obscure the model's internal working. In this paper, we present research that returns to DeepDream to assess its suit-ability as method for sound synthesis. We consider this research to be necessary for two reasons: it tackles a perceived lack of research on musical applications of DeepDream, and it addresses DeepDream's potential to combine data driven and algorithmic approaches. Our research includes a study of how the model architecture, choice of audio data-sets, and method of audio processing influence the acoustic characteristics of the synthesised sounds. We also look into the potential application of DeepDream in a live-performance setting. For this reason, the study limits itself to models consisting of small neural networks that process time-domain representations of audio. These models are resource-friendly enough to operate in real time. We hope that the results obtained so far highlight the attractiveness of Deep-Dream for musical approaches that combine algorithmic investigation with curiosity driven and open ended exploration.
This paper describes the authors' first experiments in creating an artificial dancer whose movements are generated through a combination of algorithmic and interactive techniques with machine learning. This approach is inspired by the time honoured practice of puppeteering. In puppeteering, an articulated but inanimate object seemingly comes to live through the combined effects of a human controlling select limbs of a puppet while the rest of the puppet's body moves according to gravity and mechanics. In the approach described here, the puppet is a machine-learning-based artificial character that has been trained on motion capture recordings of a human dancer. A single limb of this character is controlled either manually or algorithmically while the machine-learning system takes over the role of physics in controlling the remainder of the character's body. But rather than imitating physics, the machine-learning system generates body movements that are reminiscent of the particular style and technique of the dancer who was originally recorded for acquiring training data. More specifically, the machine-learning system operates by searching for body movements that are not only similar to the training material but that it also considers compatible with the externally controlled limb. As a result, the character playing the role of a puppet is no longer passively responding to the puppeteer but makes movement decisions on its own. This form of puppeteering establishes a form of dialogue between puppeteer and puppet in which both improvise together, and in which the puppet exhibits some of the creative idiosyncrasies of the original human dancer.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.
Steroid hormones (SHs) are a rising concern due to their high bioactivity, ubiquitous nature, and prolonged existence as a micropollutants in water, they pose a potential risk to both human health and the environment, even at low concentrations. Estrogens, progesterone, and testosterone are the three important types of steroids essential for human development and maintaining multiorgan balance, are focus to this concern. These steroid hormones originate
from various sources, including human and livestock excretions, veterinary medications, agricultural runoff, and pharmaceuticals, contributing to their presence in the environment. According to the recommendation of WHO, the guidance value for estradiol (E2) is 1 ng/L. There are several methods been attempted to remove the SH micropollutant by conventional water and wastewater technologies which are still under research. Among the various methods, electrochemical membrane reactor (EMR) is one of the emerging technologies that can address the challenge of insufficient SHs removal from the aquatic environment by conventional treatment. The degradation of SHs can be significantly influenced by various factors when treated with EMR.
In this project, the removal of SH and the important mechanism for the removal using carbon nanotube CNT-EMR is studied and the efficiency of CNT-EMR in treating the SH micropollutant is identified. By varying different parameters this experiment is carried out with the (PES-CNTs) ultrafiltration membrane. The study is carried out depending upon the SH removal based on the limiting factor such as cell voltage, flux, temperature, concentration, and type of the SH.
Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).
This thesis focuses on the development and implementation of a Datagram Transport Layer Security (DTLS) communication framework within the ns-3 network simulator, specifically targeting the LoRaWAN model network. The primary aim is to analyse the behaviour and performance of DTLS protocols across different network conditions within a LoRaWAN context. The key aspects of this work include the following.
Utilization of ns-3: This thesis leverages ns-3’s capabilities as a powerful discrete event network simulator. This platform enables the emulation of diverse network environments, characterized by varying levels of latency, packet loss, and bandwidth constraints.
Emulation of Network Challenges: The framework specifically addresses unique challenges posed by certain network configurations, such as duty cycle limitations. These constraints, which limit the time allocated for data transmission by each device, are crucial in understanding the real-world performance of DTLS protocols.
Testing in Multi-client-server Scenarios: A significant feature of this framework is its ability to test DTLS performance in complex scenarios involving multiple clients and servers. This is vital for assessing the behaviour of a protocol under realistic network conditions.
Realistic Environment Simulation: By simulating challenging network conditions, such as congestion, limited bandwidth, and resource constraints, the framework provides a realistic environment for thorough evaluation. This allows for a comprehensive analysis of DTLS in terms of security, performance, and scalability.
Overall, this thesis contributes to a deeper understanding of DTLS protocols by providing a robust tool for their evaluation under various and challenging network conditions.
Strings P
(2021)
Strings is an audiovisual performance for an acoustic violin and two generative instruments, one for creating synthetic sounds and one for creating synthetic imagery. The three instruments are related to each other conceptually , technically, and aesthetically by sharing the same physical principle, that of a vibrating string. This submission continues the work the authors have previously published at xCoAx 2020. The current submission briefly summarizes the previous publication and then describes the changes that have been made to Strings. The P in the title emphasizes, that most of these changes have been informed by experiences collected during rehearsals (in German Proben). These changes have helped Strings to progress from a predominantly technical framework to a work that is ready for performance.