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This work emphasises the synergy between anthropologi-cal research on human skeletal remains and suitable doc-umentation strategies. Highlighting the significance of data recording and the use of digital databases in various aspects of anthropological work on bones, including scien-tific standards, skeletal collections, analysis of research re-sults, ethical considerations, and curation, it provides a comprehensive examination of these topics to demonstrate the value of investing time and resources in this field, countering the existing lack of funding that has led to sig-nificant deficiencies. Additionally, the paper outlines the requirements and challenges associated with standard data protocoling and suggests that digital data manage-ment frameworks and technologies such as ontologies and semantic web technologies for anthropological information should be a central focus in developing solutions.
With globalization and the increasing diversity of the workforce, organizations are faced with the challenge of effectively managing multicultural teams. Understanding how employee engagement and job satisfaction are influenced by multicultural factors is crucial for organizations to create inclusive work environments that foster productivity and wellbeing. This literature review aims to explore the relationship between employee engagement, job satisfaction, and multi-cultural workplaces. It examines relevant studies and provides insights into the key factors, challenges, and strategies for enhancing employee engagement and job satisfaction in multicultural workplaces. The findings will shed light upon the author's research area on the factors influencing employee engagement and job satisfaction in multicultural work environments and contribute to a deeper understanding of cross-cultural dynamics in the workplace.
As new sensors are added to VR headsets, more data can be collected. This introduces a new potential threat to user privacy. We focused on the feasibility of extracting personal information from eye-tracking. To achieve this, we designed a preliminary user study focusing on the pupil response to audio stimuli. We used a variation of machine learning models to test the collected data to determine the feasibility of obtaining information such as the age or gender of the participant. Several of the experiments show promise for obtaining this information. We were able to extract with reasonable certainty whether caffeine was consumed and the gender of the participant. This demonstrates the unknown threat that embedded sensors pose to users. A further studies are planned to verify the results.
Die biologische Ammoniumoxidation ist ein zentraler Bestandteil des globalen Stickstoffkreislaufs. Angesichts der extremen Massen Stickstoff anthropogenen Ursprungs in der Umwelt, liegt die Entfernung reaktiven Stickstoffs im Interesse der Umwelt und der öffentlichen Gesundheit. In der folgenden Arbeit werden Bedingungen zur anaeroben Ammoniumoxidation mit Nitrat in einem Anammox-Reaktor untersucht. Dabei wurden 2 Laborreaktoren für eine Zeit von insgesamt 116 Tagen betrieben und beobachtet, die ausschließlich als Elektronendonatoren und Akzeptoren Ammonium und Nitrat enthielten. Zusätzlich wurden Batchkulturen mit Zellen eines Reaktors angezüchtet und auf ihre Gaszusammensetzung abhängig unterschiedlicher Eigenschaften untersucht. Hierbei wurde eine Reihe unterschiedlicher analytischer Quantifizierungsmethoden genutzt und es konnte gezeigt werden, dass ein Abbau unter den Bedingungen stattfindet.
Die aktuelle Forschung zu dieser Reaktion ist spärlich und verleiht der Bachelorarbeit dadurch Relevanz.
This thesis investigates the efficacy of four machine learning algorithms, namely linear regression, decision tree, random forest and neural network in the task of lead scoring. Specifically, the study evaluates the performance of these algorithms using datasets without sampling and with random under-sampling and over-sampling using SMOTE. The performance of each algorithm is measure using various performance metrics, including accuracy, AUC-ROC, specificity, sensitivity, precision, recall, F1 score, and G-mean. The results indicate that models trained on the dataset without sampling achieved higher accuracy than those trained on the dataset with either random under-sampling or random over-sampling using SMOTE. However, the neural network demonstrated remarkable results on each dataset compared to the other algorithms. These findings provide valuable insights into the effectiveness of machine learning algorithms for lead scoring tasks, particularly when using different sampling techniques. The findings of this study can aid lead management practices in selecting the most suitable algorithm and sampling technique for their needs. Furthermore, the study contributes to the literature by providing a comprehensive evaluation of the performance of machine learning algorithms for lead scoring tasks. This thesis has practical implications for businesses looking to improve their lead management practices, and future research could extend the analysis to other machine learning algorithms or more extensive datasets.
Crowd-Powered Medical Diagnosis : The Potential of Crowdsourcing for Patients with Rare Diseases
(2023)
With the recent rise in medical crowdsourcing platforms,
patients with chronic illnesses increasingly broadcast their
medical records to obtain an explanation for their complex
health conditions. By providing access to a vast pool of
diverse medical knowledge, crowdsourcing platforms have
the potential to change the way patients receive a medical
diagnosis. We developed a conceptual model that details
a set of variables. To further the understanding of
crowdsourcing as an emerging phenomenon in health care,
we provide a contextualization of the various factors that
drive participants to exert effort. For this purpose, we used
CrowdMed.com as a platform from which we gathered and
examined a unique dataset that involves tasks of diagnosing
rare medical conditions. By promoting crowdsourcing
as a robust and non-discriminatory alternative to seeking
help from traditional physicians, we contribute to the acceptance
and adoption of crowdsourcing services in health
economics.
Derived from the Ancient Greek word τραῦμα (engl. wound,
damage), the word trauma refers to either physical or emotional wounds. Nowadays, it is mostly used in the context of psychological wounds, inflicted by an identity-shattering event – an event that causes the traumatised to not be able to reconcile their lived reality with the expectation of a human universal experience anymore. The last decade, the last two years in particular, and the last two weeks ad absurdum, have scarred the global landscape of human existence beyond recognition. From Putin’s unexpected reimposition of mutually assured destruction doctrines via the global SARS-Cov-2 pandemic to the lingering threat of climate doom, people all over the globe have been faced with persistent threats to their most basic perceptions of ontological safety. This article seeks to examine the impact of the SARS-Cov-2 pandemic and to which degree it is justified to speak of a pandemic trauma. In addition, it engages with the liminality of pandemic trauma as a shared, collective and an isolated, individual experience, and potential mitigation strategies for building community resilience.
The digital transformation of higher education demands effective and efficient methods for learning support and assessment of learning processes. This paper relates learning support and assessment to each other in the context of learning management systems. It refers to previous studies carried out in multiple introductory economic courses of the University of Applied Sciences Mittweida which examine possible connections between the use of digital tests and learning success, investigate student’s acceptance and self-perceived learning success with respect to the webbased portion of a blended course and a purely online based course. Based on a survey (n = 71) and a quantitative analysis (n = 214) with logging and exam assessment data, the previous work shows that students approached the web-based course portion with rather reserved attitudes. Still, they perceived the individual course elements, namely videos, podcasts, interactive worksheets, online tests, and a comprehensive PDF file to be beneficial to their learning experience. Especially we could indicate a positive correlation between the points students achieved in the online tests and the exam results.
Aspects of Mindful Leadership Upon the Psychological Health of Employees in an Intercultural Context
(2023)
Across the globe, organizations are in the midst of rapid transformation. Immigration, digitalization and the push for sustainability are just to name a few. Organizational structures are being pushed for more agility, co-opetition, integration, tenable and resilient workplaces. Social structures of companies are being reformed and the weight of cooperation and integration lays upon the leaders and employees. But from this weight of integration what psychological effects does it play upon the migrant and domestic employees to be engaged at work? What role does the leadership style impact the mental health and engagement in the cross-cultural workplace? Previous work has shown the importance of workplace integration, however, the impact of the mental health of domestic employees needs more attention from the scholars in this new context. The object of the research is to define the connection of mindful leadership and the psychological health of employees within a cross-cultural workplace and to develop strategies to improve workplace engagement.
As the cryptocurrency ecosystem rapidly grows, interoperability has become increasingly crucial, enabling assets and data to interact seamlessly across multiple chains. This work describes the concept and implementation of a trustless connection between the Bitcoin Lightning Network and EVM-compatible blockchains, allowing the transfer of assets between the two ecosystems. Establishing such a connection can significantly contribute to the growth of both ecosystems as they can benefit from each other’s advantages and emerge new pos- sibilities.
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.