Leveraging technology for value creation in the context of smart sustainable cities: five potential approaches | biophilic design

This master thesis report investigates the potential of five different innovations for value creation in the context of Smart Sustainable Cities by 2050,applyingthe research question “How to create value by entrepreneurially using innovations in Smart Sustainable Cities?”. Primary(interviews with experts)and secondary research was conducted. In-depth analyses and assessments of value creation and sustainability; critical examinations of the five innovations' challenges, interconnections, and potential are performed, concluding that by applying disruptive technology that surpass the requirements of the Smart City Canvas, leverage environmental sustainability without sacrificing price, quality or other advantages, value creation is ensured

Assessing household vulnerability to employment shocks: a simulation methodology applied to Bosnia and Herzegovina

Household vulnerability relates to the incapacity of a household to preserve its welfare because of negative events. In Bosnia and Herzegovina, vulnerability is a central issue in the policy debate: many households are at risk of poverty due to fragile livelihood systems or high exposure to shocks. Since no panel data are available in Bosnia and Herzegovina, a micro-simulation methodology is adopted on the basis of a household consumption model based on quintile regression. Shocks are simulated in the labour market and the methodology consents the identification of the typologies of households which suffer from severe welfare losses or that are plunged into poverty after the shocks. The results show that the vulnerable households identified change when different shocks or a diverse definition of vulnerability are taken into consideration. At the same time, the characteristics of households vulnerable to poverty are shown to be different with respect to those of the poor

Does Acupressure Help to Reduce Symptoms in Individuals Receiving Chemotherapy?

OBJECTIVE: The objective for this selective EBM review is to determine whether or not acupressure can help to reduce symptoms in individuals receiving chemotherapy. STUDY DESIGN: A systematic review of three peer-reviewed primary studies published between the years of 2007-2014. DATA SOURCES: Three randomized control trials evaluating if acupressure can reduce nausea and vomiting symptoms in cancer patients undergoing chemotherapy. Sources were chosen from Google Scholar and PubMed based on their relevance to the clinical topic. OUTCOMES MEASURED: The outcomes that are measured in the articles are chemotherapy-related nausea and vomiting. Two of the articles measured these patient oriented outcomes using Rhodes Index of Nausea, Vomiting, and Retching and the third article measured them using daily patient logs with elements from Rhodes Index of Nausea and Rhodes Index of Nausea, Vomiting, and Retching. RESULTS: The first study conducted by Molassiotis et al3 and the study by Dibble et al5 showed significant improvement of nausea and vomiting in the acupressure group compared to the control group. However, the other study conducted by Molassiotis et al4 did not have significant findings between the control and acupressure groups. CONCLUSION: The data presented in this review suggests that there is mixed evidence regarding whether or not there is a true association between acupressure utilization and reduction of nausea and vomiting. The one study that had significant results did not incorporate a placebo group to evaluate whether or not acupressure caused a true physiologic effect to reduce symptoms in individuals undergoing chemotherapy.3 In the other two studies,4,5 there was strong significance between the control groups and placebo groups. Therefore, further research should be conducted to determine if acupressure serves more of a placebo effect rather than causing true physiologic changes leading to reduction of nausea and vomiting in chemotherapy patients

How Punxsutawney Phil’s Predictions Affect the Stock Market: A Groundhog Day Analysis

With its roots dating back to 1887, Groundhog Day has cemented itself as a beloved American holiday where people gather to see if Punxsutawney Phil will predict 6 more weeks of winter, or an early arrival of spring. Utilizing the data and methodology framework from Shanaev, Savva, and Fedorova (2021) to test if Groundhog Day predictions have any effect on S&P 500 returns, this paper revisits and revises the analysis in attempt to replicate and improve the original findings with a dummy-variable regression while controlling for other calendar anomalies. Additionally, this study expands the original analysis by including two new tests: a weather effect analysis and a steel industry specific analysis. It is found that Groundhog Day predictions do not create statistically significant returns under either prediction, despite the findings of the base paper that find significant returns for early spring predictions

Investigating the Use and Implementation of Responsible AI in Organizations: A Survey Study

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis study delves into the analysis of the use and implementation of responsible Artificial Intelligence (RAI) within organizations on a global scale, with the objective of filling the research gap in understanding the extent to which the principles are essential for the implementation of RAI worldwide. The gap lies in the lack of comprehensive investigation into the RAI principles across various organizations on a global scale. Employing a quantitative approach, a survey was used for data collection. Administered across companies in diverse sectors, the survey aimed to obtain comprehensive insights into their approaches concerning RAI. Statistical tests, including the Chi-square test for exploring relationships between categorical variables and Cramer’s V for evaluating the strength of these relationships, were conducted. The survey results show that most companies have developed ethical guidelines for AI, which underlines the increasing recognition of ethical considerations. Nevertheless, 32.1% of respondents stated that there are no clearly defined ethical standards in their organization yet. Principles like privacy, reliability, robustness and security, and interpretability are recognized as the most relevant by the respondents. When it comes to engaging stakeholders in AI development, AI/ML teams as well as product teams take on a central role. While most organizations assess the impact of AI on privacy, integrity, and data rights, there is still a prevalent concern about the transparency of AI decision-making processes. This research emphasizes the growing importance of ethics in the development of AI and underlines the urgency of actively putting ethical guidelines into practice to ensure sustainable progress in the society of AI

An Electric-Circuit Model on the Inter-Tape Contact Resistance and Current Sharing for REBCO Cable and Magnet Applications

REBCO coated conductor has demonstrated high current capacity that can enable high-field magnets for high energy physics and fusion applications. However, quench protection is still one of the main challenges to be addressed for these applications. In addition, Ic and n value variations along the length of REBCO tapes exist in commercial production. The inter-tape contact resistance plays a key role to develop the self protection capability in cables and magnets by enabling current sharing and suppressing excessive eddy currents. Here we propose an electric-circuit model to describe the inter-tape contact resistance and its impact on the current sharing between REBCO tapes. We report the experiments on a 2-stacked tape REBCO cable with local Ic drop to validate the model. With the developed model, we study the upper limit of the contact resistance which allows current sharing between tapes. We also study the impact of variation in Ic and n values in tapes on the cable performance. Our model is expected to provide useful insight into the current sharing and target values for inter-tape contact resistance in REBCO cables and magnets for various applications

Graph neural network for track reconstruction in space experiments

Development of tracking algorithm with deep learning techniques A range of models inspired by computer vision applications were investigated, which operated on data from tracking detectors in a format resembling images [A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission, Andrii Tykhonov et al,Astroparticle Physics 146, April 2023, 102795 102795]. Although these approaches demonstrated potential, image-based methods encountered difficulties in adapting to the scale of realistic data, primarily due to the high dimensionality and sparsity of the data. Tracking data are naturally represented as graph by identifying hits as nodes and tracks segments as (in general) directed edges. So that, we have explored the use of geometric deep learning techniques. Specifically, we have developed an algorithm that leverages the Graph Neural Network approach, which is a subset of geometric deep learning. This approach has been applied to the task of track reconstruction in a simplified model of space experiments. The details of our toy model simulations, the algorithm's development process, and the preliminary results are described in the accompanying slides