Proof of Concept for the Development of a Ground Vibration Sensor System for Future Research in Blasting

Ground vibrations from blasting are one of the leading limitations to mining (underground and surface). There is a need for a low-cost scalable vibration monitoring system to conduct large scale ground vibration projects in the mining industry. Studies conducted on ground vibrations use any number of different sensors to obtain their data, the different sensor capabilities and methods for data processing lead to uncertainties in the research and regulations set for ground vibrations. Commercial Systems do not allow researchers to obtain raw output data, and the data processing procedures are not provided or disclosed for these systems. In order to study ground vibrations and their impact on structures, the University of Kentucky Explosives Research Team is developing a system to obtain raw ground vibration data for their research projects going forward. This study investigates the feasibility of the initial vibration system assembled in conjunction with a significant ground vibrations study happening at a surface coal mine. The assembled system, along with two other systems, were used to study three blast events at structures near the surface coal mine. The two acquired systems were used for data comparison and validation against the assembled system in this document. Additionally, a comparative analysis was performed on the vibration frequency content obtained from the three sensors and a recommendation was made for the continued use of the assembled sensor system in ground vibrations research

Inhibitors of cell cycle checkpoints and DNA replication cause different responses in normal versus malignant urothelial cells

S-phase checkpoints are triggered in tumor cells in response to DNA replication stress caused by the tumor microenvironment or oncogenes. A recent report from our laboratory showed that tumor cells and more normal epithelial cells have a very different response to replication stress. In this Author's View, the implications of this finding are discussed

A Progressive Addiction Recovery Approach

Abstract Addiction is a complex chronic disease that plagues our society today. Understanding addiction requires a deep understanding of the relationship between the brain, mind, body, soul and others; in turn, its solution requires the same. Current research has made much progress and replaced the previous biomedical model for health and well-being with a biopsychosocial model. This same multi-dimensional approach is beginning to be applied to addiction recovery; however, there is much opportunity for progressive treatments to incorporate new components into current intervention models. Although there have been some studies to research the effects of the psychological component to recovery, other studies to research the effects of physical activity on recovery, those that research the effects of social support, and still others to study the effects of nutrition on recovery, there are little to none that have incorporated them in one treatment plan for the study of its efficaciousness. Psychology, physical activity, nutrition and social support systems are topics of review in this paper for the purpose and support of developing progressive approaches to addiction recovery. The research is promising for combining these four components in order to enhance the current social models that have already shown some success. Keywords: biopsychosocial, holistic, addiction, recovery, substance use, psychology, physical activity, exercise, nutrition, health, well-being, social support system

Chk1 suppressed cell death

The role of Chk1 in the cellular response to DNA replication stress is well established. However recent work indicates a novel role for Chk1 in the suppression of apoptosis following the disruption of DNA replication or DNA damage. This review will consider these findings in the context of known pathways of Chk1 signalling and potential applications of therapies that target Chk1

Hamster leukemia virus: lack of endogenous DNA synthesis and unique structure of its DNA polymerase

Infectious hamster leukemia virus (HaLV) contains a DNA polymerase different from those of murine and avian viruses. No endogenous reaction directed by the 60 to 70S RNA of HaLV could be demonstrated in detergenttreated HaLV virions, nor could the purified DNA polymerase copy added viral RNA. The virion RNA could, however, act as template for added avian myeloblastosis virus DNA polymerase and the HaLV DNA polymerase could efficiently utilize homopolymers as templates. The HaLV enzyme was like other reverse transcriptases in that certain ribohomopolymers were much better templates than the homologous deoxyribohomopolymers. No ribonuclease H activity could be shown in the HaLV enzyme, but neither could activity be found in the murine leukemia virus DNA polymerase. The hamster enzyme was unique in that poly(A) ·oligo(dT) was a poor template, and globin mRNA primed with oligo(dT) was totally inactive as a template. Its uniqueness was also indicated by its subunit composition; electrophoresis of the HaLV DNA polymerase in sodium dodecyl sulfate-containing polyacrylamide gels revealed equimolar amounts of two polypeptides of molecular weight 68,000 and 53,000. The sedimentation rate of the enzyme in glycerol gradients was consistent with a structure containing one each of the two polypeptides. The enzyme thus appears to be structurally distinct from other known virion DNA polymerases. Its inability to carry out an endogenous reaction in vitro might result from an inability to utilize certain primers

Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii