The Polonnaruwa meteorite: oxygen isotope, crystalline and biological composition

Results of X-Ray Diffraction (XRD) analysis, Triple Oxygen Isotope analysis and Scanning Electron Microscopic (SEM) studies are presented for stone fragments recovered from the North Central Province of Sri Lanka following a witnessed fireball event on 29 December 2012. The existence of numerous nitrogen depleted highly carbonaceous fossilized biological structures fused into the rock matrix is inconsistent with recent terrestrial contamination. Oxygen isotope results compare well with those of CI and CI-like chondrites but are inconsistent with the fulgurite hypothesis.Comment: 7 pages, 7 figures, 4 table

ENVIRONMENTAL DEGRADATION DUE TO INLAND CORAL MINING IN AKURALA, SOUTHWESTERN COASTAL ZONE

The Akurala coastal stretch (approx. 87-S<) krn from Colombo) provides an excellentexample of environmental degradation due to human exploitation of coastal resources.Holocene corals are found 2.5 - 3.0 In below the surface in a stretch extending about I kminland from the coast. The objectives of the present study were to assess the degree ofenvironmental degradation resulting from JI11andcoral mining in this stretch. 1\ was basedOll a field survey, air photo interpretation and GIS application.Although inland coral mining in Akurala records a history of over two hundred years, thisactivity has intensified over the past fifty years. The remaining pits vary in size (small: 10-465 rrr', medium: 465-1365 m2 and large: 1365-929Gm2). Mangrove plants and mangroveassociates invade these pits. The commonest are Acrosticum aureum, Bruguiera sexangulaand Lumnitzera racemosa. Among the inangrove associates and non-mangrove species are,Cerbera mangas, Hibiscus tiliaceus, Pandanus sp. Premna servattfolia and several sedgespecies (Cyperaceac). There are also such invasive plants as Annona glabra, Salviniamoles/a. and Panicus maximum growing in and around the pits.The buried coral beds are a valuable resource providing employment in mining andassociated activities to 45% of the area's population. Yet, coral mining has created manyproblems. The threats of deep pits to people, land subsidence and damages to houses arcthe main problems. Proliferation of invasive plants and mosquito breeding destroy theaesthetic value.Among the recommendations emanating from the study, the closing of abandoned pits ismost relevant. Also aquaculture in large pits can be viable. Planting mangroves in someparts too can help restore Sri Lanka's dwindling mangroves.

Monte-Carlo approximations for Dempster-Shafer belief theoretic algorithms

The Dempster-Shafer (DS) belief theory is often used within data fusion, particularly in applications rife with uncertainty that causes problems for probabilistic models. However, when a large number of variables is involved, DS theory (DST) based techniques can quickly become intractable. In this paper, we present a method for complexity reduction of DST methods based on statistical sampling, a tool commonly used in probabilistic-based signal processing (e.g., particle filters). In particular, we use sampling-based approximations to reduce the number of propositions with non-zero support, upon which the computational complexity of many DST-based algorithms are directly dependent on, thereby significantly reducing the computational overhead. We present some preliminary results that demonstrate the validity and accuracy of the proposed method, along with some insights into further developments. We also compare the proposed method to previously presented approximation methods

Focal elements generated by the Dempster-Shafer theoretic conditionals: A complete characterization

Incorporation of soft evidence into the fusion process poses considerable challenges, including issues related to the material implications of propositional logic statements, contradictory evidence, and non-identical scopes of sources providing soft evidence. The conditional approach to Dempster-Shafer (DS) theoretic evidence updating and fusion provides a promising avenue for overcoming these challenges. However, the computation of the Fagin-Halpern (FH) conditionals utilized in the conditional evidence updating strategies is non-trivial because of the lack of a method to identify the conditional focal elements directly. The work in this paper presents a complete characterization of the conditional focal elements via a necessary and sufficient condition that identifies the explicit structure of a proposition that will remain a focal element after conditioning. We illustrate the resulting computational advantage via several experiments

Belief theoretic methods for soft and hard data fusion

In many contexts, one is confronted with the problem of extract ing information from large amounts of different types soft data (e.g., text) and hard data (from e.g., physics-based sensing systems). In handling hard data, signal and data processing offers a wealth of methods related to modeling, estimation, tracking, and inference tasks. However, soft data present several challenges that necessitate the development of new data processing methods. For example, with suitable statistical natural language processing (NLP) methods, text can be converted into logic statements that are associated with various forms of associated uncertainty related to the credibility of the statement, the reliability of the text source, and so forth. In combining or fusing soft data with either soft or hard data, one must deploy methods that can suitably preserve and update the uncertainty associated with the data, thereby providing uncertainty bounds related to any inferences regarding semantics. Since standard Bayesian probabilistic approaches have problems with suitably handling uncertain logic statements, there is an emerging need for new methods for processing heterogeneous data. In this paper, we describe a framework for fusing soft and hard data based on the Dempster-Shafer (DS) belief theoretic approach which is well-suited to the task of capturing the types of models and uncertain rules that are more typical of soft data. Since the effectiveness of traditional DS methods has been hampered by high computational requirements, we base the processing framework on our new conditional approach to DS theoretic evidence updating and fusion. We address the issue of laying the foundation for a theoretically justifiable, and computationally efficient framework for fusing soft and hard data taking into account the inherent data uncertainty such as reliability and credibility. Moreover, we present an illustrative ex ample that highlights the potential for the DS conditional approach for fusing heterogeneous data

Belief theoretic methods for soft and hard data fusion

In many contexts, one is confronted with the problem of extract ing information from large amounts of different types soft data (e.g., text) and hard data (from e.g., physics-based sensing systems). In handling hard data, signal and data processing offers a wealth of methods related to modeling, estimation, tracking, and inference tasks. However, soft data present several challenges that necessitate the development of new data processing methods. For example, with suitable statistical natural language processing (NLP) methods, text can be converted into logic statements that are associated with various forms of associated uncertainty related to the credibility of the statement, the reliability of the text source, and so forth. In combining or fusing soft data with either soft or hard data, one must deploy methods that can suitably preserve and update the uncertainty associated with the data, thereby providing uncertainty bounds related to any inferences regarding semantics. Since standard Bayesian probabilistic approaches have problems with suitably handling uncertain logic statements, there is an emerging need for new methods for processing heterogeneous data. In this paper, we describe a framework for fusing soft and hard data based on the Dempster-Shafer (DS) belief theoretic approach which is well-suited to the task of capturing the types of models and uncertain rules that are more typical of soft data. Since the effectiveness of traditional DS methods has been hampered by high computational requirements, we base the processing framework on our new conditional approach to DS theoretic evidence updating and fusion. We address the issue of laying the foundation for a theoretically justifiable, and computationally efficient framework for fusing soft and hard data taking into account the inherent data uncertainty such as reliability and credibility. Moreover, we present an illustrative ex ample that highlights the potential for the DS conditional approach for fusing heterogeneous data

The Polonnaruwa meteorite: oxygen isotope, crystalline and biological composition

Results of X-Ray Diffraction (XRD) analysis, Triple Oxygen Isotope analysis and Scanning Electron Microscopic (SEM) studies are presented for stone fragments recovered from the North Central Province of Sri Lanka following a witnessed fireball event on 29 December 2012. The existence of numerous nitrogen depleted highly carbonaceous fossilized biological structures fused into the rock matrix is inconsistent with recent terrestrial contamination. Oxygen isotope results compare well with those of CI and CI-like chondrites but are inconsistent with the fulgurite hypothesis