Significance of and Challenges in Regulating Endocrine Disruptors – How Regulators and Industry Can Conquer?

Endocrine disruptors (EDs) disrupt the standard operation of the endocrine systems, resulting in untoward effects. EDs have gained extensive consideration due to their severe adverse impacts on public and wildlife health. A variety of compounds from both natural and synthetic origin may cause endocrine disruptions. These may be found in industrial chemicals, persistent organic pollutants, and products of regular use including pharmaceuticals, medical equipment, implants, medical/surgical and dental devices, cosmetics, food products, other consumer goods, their packaging and processing materials. Apart from direct consumption or use, these chemicals may impact by entering our food chain or ecosystem. These chemicals act by mimicking the hormones or blocking their receptors or interfering in their normal production, absorption, distribution, metabolism and excretion. The implementation of a regulatory framework on the complex multidisciplinary field of EDs brings enormous challenges, which pose barriers to the regulatory process. This study aims to focus on the key public and ecological health concerns presented by EDs, challenges faced by regulators to achieve successful regulatory proposition and the importance of collaboration endeavours to potentially conquer such challenges. Endocrine-disrupting chemicals (EDCs) or EDs can impact at low exposure levels, bringing about a broad range of health issues including disorders related to reproductive, fetal development, neurological, immunological, metabolic and cancer, etc. They may cause health effects across generations. The regulatory frameworks available across major regulators are tackling the identification of EDs and their mechanisms to provide necessary guidance on the safety and disposal of such substances. However, the challenges faced outweigh the regulatory mechanisms in place. The major challenges are related to structural ranges at times leading to no representative structures, active metabolites, substantiate quantum, delayed effects, epigenetic changes, widespread existence, concentration correlation for different biological species, availability of appropriate methods, exposure to a mixture of chemicals, complex endocrinology principles, unknown sources, routes and mechanisms, impacts at early stages of life, geographical movement of EDs, hazard-based vs. risk-based approaches. Regulators of healthcare and environmentalists needs to collaborate amongst them and with wider stakeholders including industry sponsors to find ways of dealing with such challenges and capitalize on the research-based knowledge grid available across institutions. Existence of EDs, their impact on living beings and mechanism of influence are like a tangled web, which induces difficulties in regulating them with conventional mindset. Conquering these challenges necessitates that regulators should join forces amongst themselves, with other institutions operating for environment, with industry sponsors and researchers to achieve success in public health safety.

Distributed Data Mining: Implementing Data Mining Jobs on Grid Environments

Data mining technology is not only composed by efficient and effective algorithms, executed as standalone kernels. Rather, it is constituted by complex applications articulated in the non-trivial interaction among hardware and software components, running on large scale distributed environments. This last feature turns out to be both the cause and the effect of the inherently distributed nature of data, on one side, and, on the other side, of the spatiotemporal complexity that characterizes many DM applications. For a growing number of application fields, Distributed Data Mining (DDM) is therefore a critical technology. In this research paper, after reviewing the open problems in DDM, we describe the DM jobs on Grid environments. We will introduce the design of Knowledge Grid System.

Analysis of the Knowledge Loss and Knowledge Grid Based on BP Neural Network

Knowledge resources are the key elements of the enterprise core competencies, and knowledge loss is easy to overlook the problems of enterprises, many enterprises have to pay this end expensive price, knowledge loss problem has become an important issue of the impact the survival and development of enterprises (especially in knowledge-intensive enterprises). Knowledge Grid research focus on intelligent information processing, its goal is to eliminate isolated island of information and knowledge, to realize the intelligent sharing of information resources and knowledge resources, avoid knowledge loss. From the research direction of knowledge loss and knowledge grid, this paper analysis knowledge loss based on BP neural network, discusses the open grid services architecture, knowledge grid services architecture, system architecture, in order to provide a reference for related research.