Helping to distinguish primary from secondary transfer events for trace DNA.

DNA is routinely recovered in criminal investigations. The sensitivity of laboratory equipment and DNA profiling kits means that it is possible to generate DNA profiles from very small amounts of cellular material. As a consequence, it has been shown that DNA we detect may not have arisen from a direct contact with an item, but rather through one or more intermediaries. Naturally the questions arising in court, particularly when considering trace DNA, are of how DNA may have come to be on an item. While scientists cannot directly answer this question, forensic biological results can help in discriminating between alleged activities. Much experimental research has been published showing the transfer and persistence of DNA under varying conditions, but as of yet the results of these studies have not been combined to deal with broad questions about transfer mechanisms. In this work we use published data and Bayesian networks to develop a statistical logical framework by which questions of transfer mechanism can be approached probabilistically. We also identify a number of areas where further work could be carried out in order to improve our knowledge base when helping to address questions about transfer mechanisms. Finally, we apply the constructed Bayesian network to ground truth known data to determine if, with current knowledge, there is any power in DNA quantities to distinguish primary and secondary transfer events

Macroscopic observation of the morphological characteristics of the ammunition gunpowder.

At the present time, optical examinations are not used in routine to analyze the gunshot residues, except for the counting and the localization of particles. Indeed, the sequence of examinations often starts immediately with destructive techniques, contrary to the widely accepted law saying that we should progress from general to particular and from non-destructive to destructive and which is recommended for all the fields of forensic sciences. When a cartridge is shot, and before the projectile leaves the gun, all the powder grains should must be completely burnt; however, if this ideal case does not happen, it is possible to find unburnt and/or partially burnt gunpowder particles [H.H. Meng, R. Caddy, Gunshot residue analysis-a review, J. Forensic Sci. 42 (1997) 553-570]. The goal of this paper is to study the morphological characteristics of the powder before and after the shot, to establish if it is possible to determine which type of ammunition has been used on the basis of these characteristics. A set of 181 cartridges of different calibers was considered and various tests carried out to evaluate the technique. On the basis of the observation of the gunpowder particles found on the target, a list of potential cartridges can be established with the actually shot cartridge always found among them. It is important to underline that a maximum of eight cartridges were proposed for each experiment. Consequently, the method can be judged very discriminating. A database was then created, including information related to the morphological characteristics of the gunpowder before and after the shot as well as to the class characteristics of the analysed cartridges

A Smart Decision-Support System for dispersed manufacturing using neural object technology

In a dispersed manufacturing environment where the production value chain is being dissected and rationalised to take advantage of dissimilar core competencies of geographically isolated firms, manufacturing strategy is required to modify from time to time in accordance with the turbulent market. A 'smart' Decision-Support System (DSS) is proposed with the distinct feature of artificial intelligence capabilities to achieve progressive knowledge acquisition and creation to the entity it is associated with. In particular, this proposed system is characterised by the inclusion of a Neural Object Agent (NOA), which is built upon a newly developed technology coined as neural object technology.Department of Industrial and Systems Engineerin

A performance benchmarking system to support supplier selection

In today's competitive business environment, management of suppliers is essential for companies to monitor the value chain of the entire production network. Evidence suggests that undesirable occurrences in companies, such as extensive delays in the planned schedule, serious quality problems and cost overruns, are, to a certain extent, related to the unfulfilled promises of business partners. Subjective judgment and the lack of a systematic method for supplier selection hinder the analysis of the current and projected performance of the suppliers, which is necessary before making a final decision. This paper attempts to propose a generic model for supplier selection, focusing on the methodology to benchmark the potential suppliers and providing a comparison of performance measures based on a number of relevant criteria. To validate the feasibility of the proposed system, this paper makes use of existing AI tools that have been developed for selecting and benchmarking suppliers for manufacturing firms.Department of Industrial and Systems Engineerin

IDR: An intrusion detection router for defending against Distributed Denial-of-Service (DDoS) attacks

Distributed Denial-of-Service (DDoS) attack has turned into one of the major security threads in recent years. Usually the only solution is to stop the services or shut down the victim and then discard the attack traffic only after the DDoS attack characteristics (such as the destination ports of the attack packets) are known. In this paper, we introduce a generic DDoS attack detection mechanism as well as the design and setup of a testbed for performing experiments and analysis. Our results showed that the mechanism can detect DDoS attack. This enable us to proceed to the next steps of packet classification and traffic control

Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

International audienceReactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the Pu239 isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from Pu239 fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to U235 fission is changed or the predicted U235, U238, Pu239, and Pu241 spectra are changed in equal measure