Obesity and occupational injury:a prospective cohort study of 69,515 public sector employees

Obesity and overweight are suggested to increase the risk of occupational injury but longitudinal evidence to confirm this is rare. We sought to evaluate obesity and overweight as risk factors for occupational injuries.

Synergistic effects on gene delivery - co-formulation of small disulfide-linked dendritic polycations with Lipofectamine 2000 (TM)

This paper describes the application of gene delivery vectors based on connecting together two well-defined low-generation poly(L-lysine) (PLL) dendrons using a disulfide-containing linker unit. We report that the transfection ability of these vectors in their own right is relatively low, because the low-generation number limits the endosomal buffering capacity. Importantly, however, we demonstrate that when applied in combination with Lipofectamine 2000 (TM), a vector from the cationic lipid family, these small cationic additives significantly enhance the levels of gene delivery (up to four-fold). Notably, the cationic additives have no effect on the levels of transfection observed with a cationic polymer, such as DEAE dextran. We therefore argue that the synergistic effects observed with Lipofectamine 2000 (TM) arise as a result of combining the delivery advantages of two different classes of vector within a single formulation, with our dendritic additives providing a degree of pH buffering within the endosome. As such, the data we present indicate that small dendritic structures, although previously largely overlooked for gene delivery owing to their inability to transfect in their own right, may actually be useful well-defined additives to well-established vector systems in order to enhance the gene delivery payload.

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a well-designed full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.