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Due to its high versatility and scalability, manual grinding is an important and widely used technology in production for rework, repair, deburring, and finishing of large or unique parts. To make the process more interactive and reliable, manual grinding needs to incorporate "skill-based design," which models a person-based system and can go significantly beyond the considerations of traditional human factors and ergonomics to encompass both processing parameters (e.g., feed rate, tool path, applied forces, material removal rate (MRR)), and machined surface quality (e.g., surface roughness). This study quantitatively analyzes the characteristics of complex techniques involved in manual operations. A series of experiments have been conducted using subjects of different levels of skill, while analyzing their visual gaze, cutting force, tool path, and workpiece quality. Analysis of variance (ANOVA) and multivariate regression analysis were performed and showed that the unique behavior of the operator affects the process performance measures of specific energy consumption and MRR. In the future, these findings can be used to predict product quality and instruct new practitioners.

This paper presents new techniques to analyze and understand the sensorimotor characteristics of manual operations such as grinding, and links their influence on process performance. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of his or her skillset. Based on the joint gaze, force, and velocity data collected from a series of manual grinding experiments, we have compared operators with different levels of experience and quantitatively described characteristics of human manual skill and their effects on manufacturing process parameters such as cutting energy, surface finish, and material removal rate (MRR). For instance, we find that an experienced subject performs the task in a precise manner by moving the tool in complex paths, with lower applied forces and velocities, and short fixations compared to a novice. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and impact future efforts in workforce training as well as the digitalization of manual expertise, thereby facilitating the transformation of raw data into product-specific knowledge.

The evaluation of multi-scale surface roughness parameters (SRPs) is important to solve many engineering problems (e.g. contact stress, sealing, friction) and is closely related to further fundamental problems (e.g. microbial contamination). Traditionally, surface roughness has been used as a standard for indicating process performance, such as tool wear, tool vibration etc. This paper also aims to find appropriate surface roughness parameters (SRPs) that can be used as process monitoring indices. Grade 304 stainless steel surfaces, generated by extrusion and grinding processes, were used in this study. The evaluation of different SRPs and their topography properties (such as fractal dimension) is discussed for extruded and ground surfaces. One problem with existing surface metrology is the availability of a multitude of disconnected roughness parameters. A statistical approach is presented in this paper that allows the most appropriate roughness parameters to monitor whether the intended surface quality converges to be found.

This paper reports our progress in developing techniques for “parsing” raw gaze and force data from manual grinding tasks into a principled model. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of her skillset. Based on the joint, gaze, and force data collected from a series of experiments, and by extending existing scanpath methods, we develop a visualization method called Gaze-Motor Space-Time Cube (GMSTC), which can help us gain insight into the joint gaze-motor routine existing in complex manual tasks. For instance, there exists a strong correlation between the spectra of a subject's fixation and force distributions. Such insight might be hard to extract through an examination of either the gaze or the force data separately. Furthermore, by comparing data obtained from operators with different levels of skill, we are able to quantitatively describe characteristics of human manual skill. For instance, we find that an experienced subject exhibits longer fixation durations and smaller fixation variations than an intermediate one. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and have implications in the way in which knowledge and manual expertise is transferred from one generation of practitioners to the next.

BackgroundWhile Indian studies have assessed care providers' knowledge and practices, there is no systematic review on the quality of tuberculosis (TB) care.MethodsWe searched multiple sources to identify studies (2000-2014) on providers' knowledge and practices. We used the International Standards for TB Care to benchmark quality of care.ResultsOf the 47 studies included, 35 were questionnaire surveys and 12 used chart abstraction. None assessed actual practice using standardised patients. Heterogeneity in the findings precluded meta-analysis. Of 22 studies evaluating provider knowledge about using sputum smears for diagnosis, 10 found that less than half of providers had correct knowledge; 3 of 4 studies assessing self-reported practices by providers found that less than a quarter reported ordering smears for patients with chest symptoms. In 11 of 14 studies that assessed treatment, less than one third of providers knew the standard regimen for drug-susceptible TB. Adherence to standards in practice was generally lower than correct knowledge of those standards. Eleven studies with both public and private providers found higher levels of appropriate knowledge/practice in the public sector.ConclusionsAvailable evidence suggests suboptimal quality of TB care, particularly in the private sector. Improvement of quality of care should be a priority for India.

XMRV, or xenotropic murine leukemia virus (MLV)-related virus, is a novel gammaretrovirus originally identified in studies that analyzed tissue from prostate cancer patients in 2006 and blood from patients with chronic fatigue syndrome (CFS) in 2009. Howev