Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Cybersecurity Mindfulness in the Age of Mindless AIs: Investigating AI Assistants Impact in High-Reliability Organizations

The Focus: The focus of this Master Thesis is to investigate how AI tools, such as Large Learning Models (LLMs), impact cybersecurity operations in organizations that are regarded as highly reliable. To understand the impacts of AI tools on such operations, we also need to understand the nature of AI tools, their context of use and the experience of users that rely on them. Research Approach: This thesis is structured around two different methods of investigation. First a systematic literature review was conducted, where related articles was found in different databases, i.e. Google Scholar, Web of Science and the Basket of Eight publications. After this a Qualitative study was conducted where a multiple case study with interviews and random sampling was utilized. A total of 8 informants were interviewed for this study, each lasting ~30 minutes where the questions were based on the findings from the literature. Findings: From the literature it became clear that AIs, while better than humans in many things such as analyzing Big Data, intrusion detection and other pattern recognition activities, does bring with it many difficulties to the individual and the organization. AIs and LLMs are prone to making you develop an overreliance on them where you accept their answers because of your own biases, while the information itself might be fundamentally wrong or even deceitful. This phenomenon is called AI Hallucination and is vital to understanding an AIs effect on individuals. The literature highlighted that when using any tool, it was important to realize that the AI tool is simply a machine and might be wrong, question everything and do not accept any information at face value. Quite simply, think things through. LLMs have a problem with transparency, it is impossible to know its ‘reasoning’ behind the information it provides. This fact is supported by both the literature and the interviews themselves. Overreliance, hallucination, cultivating the wrong kind of trust and lack of transparency all lead to an individual acting mindless who takes the information as true. While they have been deceived by trusting something that essentially is untrustworthy or at the very least should have been looked more into. Implication: The practical implications for this study is that an organization, especially if it is of high reliability should carefully identify measures to avoid the negative impact of AI Assistants when used in day-to-day work in cybersecurity operations

Cybersecurity Mindfulness in the Age of Mindless AIs: Investigating AI Assistants Impact in High-Reliability Organizations

The Focus: The focus of this Master Thesis is to investigate how AI tools, such as Large Learning Models (LLMs), impact cybersecurity operations in organizations that are regarded as highly reliable. To understand the impacts of AI tools on such operations, we also need to understand the nature of AI tools, their context of use and the experience of users that rely on them. Research Approach: This thesis is structured around two different methods of investigation. First a systematic literature review was conducted, where related articles was found in different databases, i.e. Google Scholar, Web of Science and the Basket of Eight publications. After this a Qualitative study was conducted where a multiple case study with interviews and random sampling was utilized. A total of 8 informants were interviewed for this study, each lasting ~30 minutes where the questions were based on the findings from the literature. Findings: From the literature it became clear that AIs, while better than humans in many things such as analyzing Big Data, intrusion detection and other pattern recognition activities, does bring with it many difficulties to the individual and the organization. AIs and LLMs are prone to making you develop an overreliance on them where you accept their answers because of your own biases, while the information itself might be fundamentally wrong or even deceitful. This phenomenon is called AI Hallucination and is vital to understanding an AIs effect on individuals. The literature highlighted that when using any tool, it was important to realize that the AI tool is simply a machine and might be wrong, question everything and do not accept any information at face value. Quite simply, think things through. LLMs have a problem with transparency, it is impossible to know its ‘reasoning’ behind the information it provides. This fact is supported by both the literature and the interviews themselves. Overreliance, hallucination, cultivating the wrong kind of trust and lack of transparency all lead to an individual acting mindless who takes the information as true. While they have been deceived by trusting something that essentially is untrustworthy or at the very least should have been looked more into. Implication: The practical implications for this study is that an organization, especially if it is of high reliability should carefully identify measures to avoid the negative impact of AI Assistants when used in day-to-day work in cybersecurity operations

Economic and Policy Challenges of the Energy Transition in CEE Countries

With the announcement of the European Green Deal, which defines a set of policy initiatives aimed at achieving a 50–55% reduction in carbon emissions by 2030 and making Europe climate neutral in 2050, the challenge of energy transition becomes even more critical. The transformation of national energy systems towards sustainability is progressing throughout all Central and Eastern European (CEE) countries, yet the goals and results are different. Most EU Member States have made substantial progress towards meeting their long-term commitments of emissions reductions. However, some bloc members have struggled to meet their obligations. An effective energy transition requires the introduction of appropriately designed policy instruments and of robust economic analyses that ensure the best possible outcomes at the lowest costs for society. In this context, this Special Issue aims to bring into the discussion the challenges that CEE countries have to face and overcome while undergoing energy transition

A CAN network based intelligent monitoring system for automotive vehicles

Road vehicles are the major pollution sources which seriously harm human health and the environment worldwide. A series of directives for limiting vehicle pollution have been introduced pillticularly in the EU and USA. Presently many investigations are being carried out in order to find out more effectively ways to reduce the vehicle pollution hy precise measurement of the vehicle pollution. Tllis thesis reports the details of the design illId construction of a CAN network based intelligent monitoring system for the distributed monitoring of vellicle pollution. The essential theory to SUppOlt the development of the CAN networks is given in this thesis. The system descrihed in this thesis monitors the vehicle engine vibration, temperature of the vehicle exhaust emissions, and the vehicle exhaust gases. The CAN networks have been developed for each suh-system so that the system can be incorporated into automotive vehicles. The system has been tested on a diesel engine and showed that the vihration and the temperature were accurately measured. The tests of the priI11ill'y set-ups of the gas monitoring suh-system showed that the exhaust gases could he detected, and the pollution levels of each exhaust gases could he precisely measured. This project has heen undertaken as pillt of an EC Framework Programme 6 STREP project (OPTO-EMI-SENSE) and the results are being reviewed by Centro Ricerche Fiat for installation on a new diesel engine car. Chulian

From Smart to Green Cities: a KPI-based model for the built environment regeneration. A study of application in Bologna

Smart City (SC) emerged during the end of last century as a reference concept for shaping the city of the future. The literature review shows how SC originates from a debate questioning about the future of cities in a world continuously object of pressures: resource scarcity, economic crisis, lack of social identity, besides continuous input from technologies. The progressive permeating of innovative devices, simplifying people life or enabling them in networking and knowledge, led to relevant modification of the built environment. The word “smart” refers therefore not only to the ICT component of city but it also refers to the need of facing an increasing complexity involving all sectors of cities. The extend of approaches, applications, testing and theories coming along with the SC topic oblige the research to critically and extensively study those elements, broadening the analysis to additional experiences, and going toward the definition of SC for coming to a wider definition of Green City as an integrated, sustainable, resilient and smart urban regeneration approach. The research studies these approaches deepening the relation between Architecture Technology and Urban Planning, with a specific insight into a step-by-step project approach and a KPIs performance assessment. The main original output of the research is the proposition of the Green City Circle: a model for addressing the regeneration of districts into existing urban contexts. The thesis is Climate KIC labelled (European Institute of Technology)

NASA Tech Briefs, July 1993

Topics include: Data Acquisition and Analysis: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences