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To investigate the effects of climate change on interactions within ecosystems, a microcosm experiment was conducted. The effects of temperature increase and predator diversity on Collembola communities and their decomposition rate were investigated. The predators used were mites and Chilopods, whose predation effects on several response variables were analysed. This data included Collembola abundance, biomass and body mass as well as basal respiration and microbial biomass carbon. These response variables were tested against the predictors in several models. Temperature showed high significance in interaction with mite abundance in almost all models. Furthermore, the results of the basal respiration and microbial biomass carbon support the suggestion of a trophic cascade within the animal interaction.
A Systematic Literature Review on Blockchain Oracles: State of Research, Challenges, and Trends
(2023)
To enable data exchange between the Blockchain protocol (on-chain) and the real world (off-chain), e.g., non-Blockchain-based applications and systems, a software called Oracle is used [3]. Blockchain oracle is an important component in the use of off-chain data for on-chain smart contracts. However, there is limited scientific literature available on this important blockchain topic. Therefore, in this paper, a novel systematic literature review based on intelligent methods, e.g., information linking, topic clustering and focus identification through frequency calculations, is proposed. Thus, the current state of scientific research interest, content and challenges, and future research directions for blockchain oracles are identified. This paper shows that there is little unbiased literature that does not call oracles a problem. From the results of this new literature review framework, relevant areas of data handling and verification with blockchain oracles are identified for future research.
Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers.
Over recent years, Maximal Extractable Value (MEV) has gained significant importance within the decentralized finance (DeFi) ecosystem. Remarkably, within just two years of its emergence, MEV has seen an extraction of approximately 600 million USD - a phenomenon that has sparked concerns regarding potential threats to blockchain stability.
With growing interest in the Ethereum network and the growing DeFi sector, research surrounding MEV has substantially increased. This work aims to offer a comprehensive understanding of MEV. Additionally, this research quantifies the largest types of MEV (Arbitrage, Sandwich and Liquidations) from March 2022 to March 2023. The data are then compared to other sources, revealing a general upward trend, with a particularly noticeable increase in Sandwich Attacks.
The cryptocurrency ecosystem has seen significant growth with Ethereum and Bitcoin as foundational pillars. Ethereum introduced smart contracts revolutionizing decentralized applications (dApps) across various domains. Scalability challenges led to alternative ecosystems like Binance Smart Chain and Polygon, maintaining compatibility through the Ethereum Virtual Machine (EVM). Bitcoin also faces scalability issues, leading to the Lightning Network's development—an off-chain solution with payment channels for scalable instant transactions. Interoperability is increasingly crucial as the cryptocurrency ecosystem continues to grow, enabling seamless interactions between assets and data across multiple blockchain platforms. EVM-compatible blockchains and the Lightning Network offer unique advantages in their respective use cases. This paper utilizes atomic swaps to create a secure, fast, and user-friendly trustless bridge between the Lightning Network and EVM-compatible blockchains, fostering the growth of both ecosystems and unlocking novel opportunities.
Currently, the Internet of Things (IoT) is connected to the virtual world through the Web of Things (WoT), allowing efficient utilization of real-world objects with Internet technologies. The WoT facilitates abstract interaction between applications and connected IoT devices, allowing owners to switch between devices while using multiple ones. To achieve this, virtual assets in WoT devices can be tokenized through smart contracts and transferred using hashed proof as transactions within blockchain networks that support virtual currencies. The goal of Web of Things is to establish connectivity, interoperability, and integration among IoT devices using web standards and protocols, reducing reliance on device manufacturers. This enables easy integration of Web 3.0 cryptocurrency for device management. This study proposes a solution for WoT applications involving different cryptocurrency definitions. Finally, simulation results are presented to demonstrate the tokenization-based ownership transfer in the Web of Things.
Recently a deep neural network architecture designed to work on graph- structured data have been capturing notice as well as getting implemented in various domains and application. However, learning representation (feature embedding) from graphical data picking pace in research and constructing graph(s) from dataset remains a challenge. The ability to map the data to lower dimensions further makes the task easier while providing comfort in applying many operations. Graph neural network (GNN) is one of the novel neural network models that is catching attention as it is outperforming in various applications like recommender systems, social networks, chemical synthesis, and many more. This thesis discusses a unique approach for a fundamental task on graphs; node classification. The feature embedding for a node is aggregated by applying a Recurrent neural network (RNN), then a GNN model is trained to classify a node with the help of aggregated features and Q learning supports in optimizing the shape of neural networks. This thesis starts with the working principles of the Feedforward neural network, recurrent units like simple RNN, Long short-term memory (LSTM), and Gated recurrent unit (GRU), followed by concepts of Reinforcement learning (RL) and the Q learning algorithm. An overview of the fundamentals of graphs, followed by the GNN architecture and workflow, is discussed subsequently. Some basic GNN models are discussed in brief later before it approaches the technical implementation details, the output of the model, and a comparison with a few other models such as GraphSage and Graph attention network (GAN).
In the field of satellites it is common practice to combine multiple ground stations into one network, to increase communication times with satellites. This work focuses on TIM, which is an international academic colaborative project. Important criteria for this project are elaborated and used to evaluate existing ground station networks. It concludes that there is no appropriate solution availiable for this specific use case and establish a proposed solution. The proposed ground station network software will be elaborated and evaluated.
Assessment of COI and 16S for insect species identification ti determine the diet of city bats
(2023)
Despite the numerous benefits of urbanization to human living conditions, urbanization has also negatively affected humans, their environment, and other organisms that share urban habitats with humans. Undoubtedly adverse while some wild animals avoid living in urban areas, others are more tolerant or prefer life in urban habitats. There are more than 1,400 species of bats in the world.
Therefore, they have the potential to contribute significantly to the mammalian biodiversity in urban areas. Insectivorous bats species play a key role in agriculture by improving yields and reducing chemical pesticide costs. Using metabarcoding, it is possible to determine the prey consumed by these noctule mammals based on the DNA fragments in their fecal pellets. This study
aimed to evaluate COI and 16S metabarcodes for insect species identification to determine the diet of metropolitan bats. For this purpose, COI and 16S metabarcodes were extracted, amplified, and sequenced from 65 bat feces collected in the Berlin metropolitan areas. Following a taxonomic annotation, I found that 73% of all identified insects could only be detected using the COI method, while 15% could be recovered using the 16S approach. Just 12% of all detected insects were identified simultaneously by both markers. According to this result, COI is more suitable for the taxonomic identification of insects from bat feces. However, given the bias of COI primers, it is recommended to use both markers for a more precise estimation of species diversity. Additionally,based on the insect species identified, I noticed that urban bats fed mainly on Diptera, Coleoptera,and Lepidoptera. The bat species Nyctalus noctula was most abundant in the samples. His diet analysis revealed that 91% of the samples contained the insect species Chironomus plumosus. 14 pest insect species were also found in his diet.
This desk research will initiate an exploration of present and potential blockchain applications in the higher education sector of Europe. The aim of this research is to create a theoretical base for a further postgraduate research and analysis, so to create an effective model/framework to augment the integration of blockchain technology into existing organizational processes, initially in higher educational institutions, but which may be adaptable and generalizable to other specific uses. Due to the novelty of the topic, academic resources related to the research area are limited. Most studies seem to focus on blockchain-based applications in industries such as finance, healthcare, and supply chain management, and there is little evidence of the impact of blockchain technology on education. This paper discusses present and suggests some potential blockchain-based applications in education in Europe and beyond. This research provides a groundwork for education and academia stakeholders, policymakers and researchers to exploit the potential of blockchain in different functions of an education system.