11,412 research outputs found

    Case Notes

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    The philosophy, nature and practice of forensic sediment analysis

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    The rapidly expanding field of forensic geoscience derives its roots from nineteenth-and early twentieth-century scientists who both influence and are influenced by literature and fictional writing. Forensic geoscience borrows much, but not all, of its precepts from geological and geomorphological analytical techniques. Fundamental differences exist between forensic geoscience and its sister disciplines, fundamental enough to make the unwary geoscientist succumb to philosophical and practical pitfalls which will not only endanger the outline of their report, but may well indeed provide false-negative or false-positive results leading to contrary or inaccurate conclusions. In the law, such outcomes have devastating and untenable consequences. Forensic geoscience requires techniques of exclusion rather than inclusion and an acknowledgement that analytical techniques may be diagnostic only in very specific situations. Whether analysis of the ubiquitous or the exotic component is chosen, acknowledgement of the need for samples to be representative is required. The presentation of false-positive results or the lack of identification of sample 'mixing' is prerequisite to the application of statistical tests which must be applied in the most careful manner. The realization of the limitations of the technique requires, wherever possible, conjunctive analysis by other truly independent techniques. While personal opinion derives from experience, there is no place for assumption. Research papers in forensic geoscience are not submitted to be speculative or challenging as may be the case in many fields of geomorphology and geology. There is no place for conjecture in forensic geoscience. © 2007 SAGE Publications

    Field Interrogation: Administrative, Judicial and Legislative Approaches

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    Secure Harmonized Speed Under Byzantine Faults for Autonomous Vehicle Platoons Using Blockchain Technology

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    Autonomous Vehicle (AV) platooning holds the promise of safer and more efficient road transportation. By coordinating the movements of a group of vehicles, platooning offers benefits such as reduced energy consumption, lower emissions, and improved traffic flow. However, the realization of these advantages hinges on the ability of platooning vehicles to reach a consensus and maintain secure, cooperative behavior. Byzantine behavior [1,2], characterized by vehicles transmitting incorrect or conflicting information, threatens the integrity of platoon coordination. Vehicles within the platoon share vital data such as position, speed, and other relevant information to optimize their operation, ensuring safe and efficient driving. However, Byzantine behavior in AV platoons presents a critical challenge by disrupting coordinated operations. Consequently, the malicious transmission of conflicting information can lead to safety compromises, traffic disruptions, energy inefficiency, loss of trust, chain reactions of faults, and legal complexities [3,4]. In this light, this thesis delves into the challenges posed by Byzantine behavior within platoons and presents a robust solution using ConsenCar; a blockchain-based protocol for AV platoons which aims to address Byzantine faults in order to maintain reliable and secure platoon operations. Recognizing the complex obstacles presented by Byzantine faults in these critical real-time systems, this research exploits the potential of blockchain technology to establish Byzantine Fault Tolerance (BFT) through Vehicle-to-Vehicle (V2V) communications over a Vehicular Ad hoc NETwork (VANET). The operational procedure of ConsenCar involves several stages, including proposal validation, decision-making, and eliminating faulty vehicles. In instances such as speed harmonization, the decentralized network framework enables vehicles to exchange messages to ultimately agree on a harmonized speed that maximizes safety and efficiency. Notably, ConsenCar is designed to detect and isolate vehicles displaying Byzantine behavior, ensuring that their actions do not compromise the integrity of decision-making. Consequently, ConsenCar results in a robust assurance that all non-faulty vehicles converge on unanimous decisions. By testing ConsenCar on the speed harmonization operation, simulation results indicate that under the presence of Byzantine behavior, the protocol successfully detects and eliminates faulty vehicles, provided that more than two-thirds of the vehicles are non-faulty. This allows non-faulty vehicles to achieve secure harmonized speed and maintain safe platoon operations. As such, the protocol generalizes to secure other platooning operations, including splitting and merging, intersection negotiation, lane-changing, and others. The implications of this research are significant for the future of AV platooning, as it establishes BFT to enhance the safety, efficiency, and reliability of AV transportation, therefore paving the way for improved security and cooperative road ecosystems

    Innovative methods in European road freight transport statistics: A pilot study

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    By using innovative methods, such as the automated transfer of corporate electronic data to National Statistical Institutions, official transport data can be significantly improved in terms of reliability, costs and the burden on respondents. In this paper, we show that the automated compilation of statistical reports is possible and feasible. Based on previous findings, a new method and tool were developed in cooperation with two business partners from the logistics sector in Austria. The results show that the prototype could successfully be implemented at the partner companies. Improved data quality can lead to more reliable analyses in various fields. Compared to actual volumes of investments into transport, the costs of transport statistics are limited. By using the new and innovative data collection techniques, these costs can even be reduced in the long run; at the same time, the risk of bad investments and wrong decisions caused by analyses relying on poor data quality can be reduced. This results in a substantial value for business, research, the economy and the society

    Machine Learning, Automated Suspicion Algorithms, and the Fourth Amendment

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    Machine Learning, Automated Suspicion Algorithms, and the Fourth Amendment

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