A mathematical model for the optimization of the non-metallic mining supply chain in the mining district of Calamarí-Sucre (Colombia)

This article presents a mathematical model of the Supply chain of non-metallic mining. The model considers uncertainty scenarios in materials, elements for capacity planning in a multilevel chain and with multiple products. The mathematical model is collaborative and maximizes the profits of the actors in the supply chain. The model is implemented in Calamarí-Sucre mining district (Colombia). The scenario is applied to the extraction, processing, storage, and distribution of limestone. To solve the model, the GAMS software was used through libraries of relaxed mixed nonlinear programming - RMINLP and the DICOPT solver. The results indicate that the greatest benefits occur in a scenario of the high provision of raw materials. The equity in the economic benefits show a dynamics of vertical integration in the sector. The model applied to non-metallic mining complexes helps determine optimal strategies and decisions in different echelons

Niches and Interspecific Competitive Relationships of the Parasitoids, Microplitis prodeniae and Campoletis chlorldeae, of the Oriental Leafworm Moth, Spodoptera litura, in Tobacco

Both Microplitis prodeniae Rao and Chandry (Hymenoptera: Bracondidae) and Campoletis chlorideae Uchida (Hymenoptera: Ichnumonidae) are major parasitoids of Spodoptera litura (Fabricious) (Lepidoptera: Noctuidae) in tobacco, Nicotiana tabacum L. (Solanales: Solanaceae) at Nanxiong, Guangdong Province, South China. The niches and interspecific competition relationships of the two species were studied. The results show that the competition between the two species for spatial and food resources was very intense, and C. chlorideae was always dominant when the two species compete for spatial and food resources in different periods. Thus C. chlorideae may drive M. prodeniae away when they occupy the same spatial or food resource. The adaptability of C. chlorideae to the environment in the tobacco fields may be greater than that of M. prodeniae, so C. chlorideae can maintain a higher population compared to that of M. prodeniae

Biodiversity Loss and the Taxonomic Bottleneck: Emerging Biodiversity Science

Human domination of the Earth has resulted in dramatic changes to global and local patterns of biodiversity. Biodiversity is critical to human sustainability because it drives the ecosystem services that provide the core of our life-support system. As we, the human species, are the primary factor leading to the decline in biodiversity, we need detailed information about the biodiversity and species composition of specific locations in order to understand how different species contribute to ecosystem services and how humans can sustainably conserve and manage biodiversity. Taxonomy and ecology, two fundamental sciences that generate the knowledge about biodiversity, are associated with a number of limitations that prevent them from providing the information needed to fully understand the relevance of biodiversity in its entirety for human sustainability: (1) biodiversity conservation strategies that tend to be overly focused on research and policy on a global scale with little impact on local biodiversity; (2) the small knowledge base of extant global biodiversity; (3) a lack of much-needed site-specific data on the species composition of communities in human-dominated landscapes, which hinders ecosystem management and biodiversity conservation; (4) biodiversity studies with a lack of taxonomic precision; (5) a lack of taxonomic expertise and trained taxonomists; (6) a taxonomic bottleneck in biodiversity inventory and assessment; and (7) neglect of taxonomic resources and a lack of taxonomic service infrastructure for biodiversity science. These limitations are directly related to contemporary trends in research, conservation strategies, environmental stewardship, environmental education, sustainable development, and local site-specific conservation. Today’s biological knowledge is built on the known global biodiversity, which represents barely 20% of what is currently extant (commonly accepted estimate of 10 million species) on planet Earth. Much remains unexplored and unknown, particularly in hotspots regions of Africa, South Eastern Asia, and South and Central America, including many developing or underdeveloped countries, where localized biodiversity is scarcely studied or described. ‘‘Backyard biodiversity’’, defined as local biodiversity near human habitation, refers to the natural resources and capital for ecosystem services at the grassroots level, which urgently needs to be explored, documented, and conserved as it is the backbone of sustainable economic development in these countries. Beginning with early identification and documentation of local flora and fauna, taxonomy has documented global biodiversity and natural history based on the collection of ‘‘backyard biodiversity’’ specimens worldwide. However, this branch of science suffered a continuous decline in the latter half of the twentieth century, and has now reached a point of potential demise. At present there are very few professional taxonomists and trained local parataxonomists worldwide, while the need for, and demands on, taxonomic services by conservation and resource management communities are rapidly increasing. Systematic collections, the material basis of biodiversity information, have been neglected and abandoned, particularly at institutions of higher learning. Considering the rapid increase in the human population and urbanization, human sustainability requires new conceptual and practical approaches to refocusing and energizing the study of the biodiversity that is the core of natural resources for sustainable development and biotic capital for sustaining our life-support system. In this paper we aim to document and extrapolate the essence of biodiversity, discuss the state and nature of taxonomic demise, the trends of recent biodiversity studies, and suggest reasonable approaches to a biodiversity science to facilitate the expansion of global biodiversity knowledge and to create useful data on backyard biodiversity worldwide towards human sustainability

In Vitro Antibacterial Activity of Cysteine Protease Inhibitor from Kiwifruit (Actinidia deliciosa)

The need for replacing traditional pesticides with alternative agents for the management of agricultural pathogens is rising worldwide. In this study, a cysteine proteinase inhibitor (CPI), 11 kDa in size, was purified from green kiwifruit to homogeneity. We examined the growth inhibition of three plant pathogenic Gram-negative bacterial strains by kiwi CPI and attempted to elucidate the potential mechanism of the growth inhibition. CPI influenced the growth of phytopathogenic bacteria Agrobacterium tumefaciens (76.2 % growth inhibition using 15 mu M CPI), Burkholderia cepacia (75.6 % growth inhibition) and, to a lesser extent, Erwinia carotovora (44.4 % growth inhibition) by inhibiting proteinases that are excreted by these bacteria. Identification and characterization of natural plant defense molecules is the first step toward creation of improved methods for pest control based on naturally occurring molecules

AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost