A Constrained Fuzzy Knowledge-Based System for the Management of Container Yard Operations

The management of container yard operations is considered by yard operators to be a very challenging task due to the many uncertainties inherent in such operations. The storage of the containers is one of those operations that require proper management for the efficient utilisation of the yard, requiring rapid retrieval time and a minimum number of re-handlings. The main challenge is when containers of a different size, type, or weight need to be stored in a yard that holds a number of pre-existing containers. This challenge becomes even more complex when the date and time for the departure of the containers are unknown, as is the case when the container is collected by a third-party logistics company without any prior notice being given. The aim of this study is to develop a new system for the management of container yard operations that takes into consideration a number of factors and constraints that occur in a real-life situation. One of these factors is the duration of stay for the topmost containers of each stack, when the containers are stored. Because the duration of stay for containers in a yard varies dynamically over time, an ‘ON/OFF’ strategy is proposed to activate/deactivate the duration of stay factor constraint if the length of stay for these containers varies significantly over time. A number of tools and techniques are utilised for developing the proposed system including: discrete event simulation for the modelling of container storage and retrieval operations, a fuzzy know ledge-based model for the stack allocation of containers, and a heuristic algorithm called ‘neighbourhood’ for the container retrieval operation. Results show that by adopting the proposed ‘ON/OFF’ strategy, 5% of the number of re-handlings, 2.5% of the total retrieval time, 6.6% of the total re-handling time and 42% of the average waiting time per truck are reduced

Design of a Two-level Adaptive Multi-Agent System for Malaria Vectors driven by an ontology

<p>Abstract</p> <p>Background</p> <p>The understanding of heterogeneities in disease transmission dynamics as far as malaria vectors are concerned is a big challenge. Many studies while tackling this problem don't find exact models to explain the malaria vectors propagation.</p> <p>Methods</p> <p>To solve the problem we define an Adaptive Multi-Agent System (AMAS) which has the property to be elastic and is a two-level system as well. This AMAS is a dynamic system where the two levels are linked by an Ontology which allows it to function as a reduced system and as an extended system. In a primary level, the AMAS comprises organization agents and in a secondary level, it is constituted of analysis agents. Its entry point, a User Interface Agent, can reproduce itself because it is given a minimum of background knowledge and it learns appropriate "behavior" from the user in the presence of ambiguous queries and from other agents of the AMAS in other situations.</p> <p>Results</p> <p>Some of the outputs of our system present a series of tables, diagrams showing some factors like Entomological parameters of malaria transmission, Percentages of malaria transmission per malaria vectors, Entomological inoculation rate. Many others parameters can be produced by the system depending on the inputted data.</p> <p>Conclusion</p> <p>Our approach is an intelligent one which differs from statistical approaches that are sometimes used in the field. This intelligent approach aligns itself with the distributed artificial intelligence. In terms of fight against malaria disease our system offers opportunities of reducing efforts of human resources who are not obliged to cover the entire territory while conducting surveys. Secondly the AMAS can determine the presence or the absence of malaria vectors even when specific data have not been collected in the geographical area. In the difference of a statistical technique, in our case the projection of the results in the field can sometimes appeared to be more general.</p

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model.To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods

Southeast Asian diversity: first insights into the complex mtDNA structure of Laos

<p>Abstract</p> <p>Background</p> <p>Vast migrations and subsequent assimilation processes have shaped the genetic composition of Southeast Asia, an area of close contact between several major ethnic groups. To better characterize the genetic variation of this region, we analyzed the entire mtDNA control region of 214 unrelated donors from Laos according to highest forensic quality standards. To detail the phylogeny, we inspected selected SNPs from the mtDNA coding region. For <it>a posteriori </it>data quality control, quasi-median network constructions and autosomal STR typing were performed. In order to describe the mtDNA setup of Laos more thoroughly, the data were subjected to population genetic comparisons with 16 East Asian groups.</p> <p>Results</p> <p>The Laos sample exhibited ample mtDNA diversity, reflecting the huge number of ethnic groups listed. We found several new, so far undescribed mtDNA lineages in this dataset and surrounding populations. The Laos population was characteristic in terms of haplotype composition and genetic structure, however, genetic comparisons with other Southeast Asian populations revealed limited, but significant genetic differentiation. Notable differences in the maternal relationship to the major indigenous Southeast Asian ethnolinguistic groups were detected.</p> <p>Conclusions</p> <p>In this study, we portray the great mtDNA variety of Laos for the first time. Our findings will contribute to clarify the migration history of the region. They encourage setting up regional and subpopulation databases, especially for forensic applications. The Laotian sequences will be incorporated into the collaborative EMPOP mtDNA database <url>http://www.empop.org</url> upon publication and will be available as the first mtDNA reference data for this country.</p

Cooperative coupling of ultracold atoms and surface plasmons

Cooperative coupling between optical emitters and light fields is one of the outstanding goals in quantum technology. It is both fundamentally interesting for the extraordinary radiation properties of the participating emitters and has many potential applications in photonics. While this goal has been achieved using high-finesse optical cavities, cavity-free approaches that are broadband and easy to build have attracted much attention recently. Here we demonstrate cooperative coupling of ultracold atoms with surface plasmons propagating on a plane gold surface. While the atoms are moving towards the surface they are excited by an external laser pulse. Excited surface plasmons are detected via leakage radiation into the substrate of the gold layer. A maximum Purcell factor of $\eta_\mathrm{P}=4.9$ is reached at an optimum distance of $z=250~\mathrm{nm}$ from the surface. The coupling leads to the observation of a Fano-like resonance in the spectrum.Comment: 9 pages, 4 figure

Neuroinflammation and structural injury of the fetal ovine brain following intra-amniotic Candida albicans exposure.

BackgroundIntra-amniotic Candida albicans (C. Albicans) infection is associated with preterm birth and high morbidity and mortality rates. Survivors are prone to adverse neurodevelopmental outcomes. The mechanisms leading to these adverse neonatal brain outcomes remain largely unknown. To better understand the mechanisms underlying C. albicans-induced fetal brain injury, we studied immunological responses and structural changes of the fetal brain in a well-established translational ovine model of intra-amniotic C. albicans infection. In addition, we tested whether these potential adverse outcomes of the fetal brain were improved in utero by antifungal treatment with fluconazole.MethodsPregnant ewes received an intra-amniotic injection of 10(7) colony-forming units C. albicans or saline (controls) at 3 or 5 days before preterm delivery at 0.8 of gestation (term ~ 150 days). Fetal intra-amniotic/intra-peritoneal injections of fluconazole or saline (controls) were administered 2 days after C. albicans exposure. Post mortem analyses for fungal burden, peripheral immune activation, neuroinflammation, and white matter/neuronal injury were performed to determine the effects of intra-amniotic C. albicans and fluconazole treatment.ResultsIntra-amniotic exposure to C. albicans caused a severe systemic inflammatory response, illustrated by a robust increase of plasma interleukin-6 concentrations. Cerebrospinal fluid cultures were positive for C. albicans in the majority of the 3-day C. albicans-exposed animals whereas no positive cultures were present in the 5-day C. albicans-exposed and fluconazole-treated animals. Although C. albicans was not detected in the brain parenchyma, a neuroinflammatory response in the hippocampus and white matter was seen which was characterized by increased microglial and astrocyte activation. These neuroinflammatory changes were accompanied by structural white matter injury. Intra-amniotic fluconazole reduced fetal mortality but did not attenuate neuroinflammation and white matter injury.ConclusionsIntra-amniotic C. albicans exposure provoked acute systemic and neuroinflammatory responses with concomitant white matter injury. Fluconazole treatment prevented systemic inflammation without attenuating cerebral inflammation and injury