An exposure prevention rating method for intervention needs assessment and effectiveness evaluation

This article describes a new method for (1) systematically prioritizing needs for intervention on hazardous substance exposures in manufacturing work sites, and (2) evaluating intervention effectiveness. We developed a checklist containing six unique sets of yes/no variables organized in a 2 × 3 matrix of exposure potential versus protection (two columns) at the levels of materials, processes, and human interface (three rows). The three levels correspond to a simplified hierarchy of controls. Each of the six sets of indicator variables was reduced to a high/moderate/low rating. Ratings from the matrix were then combined to generate a single overall exposure prevention rating for each area. Reflecting the hierarchy of controls, material factors were weighted highest, followed by process, and then human interface. The checklist was filled out by an industrial hygienist while conducting a walk-through inspection (N = 131 manufacturing processes/areas in 17 large work sites). One area or process per manufacturing department was assessed and rated. Based on the resulting Exposure Prevention ratings, we concluded that exposures were well controlled in the majority of areas assessed (64% with rating of 1 or 2 on a 6-point scale), that there is some room for improvement in 26 percent of areas (rating of 3 or 4), and that roughly 10 percent of the areas assessed are urgently in need of intervention (rated as 5 or 6). A second hygienist independently assessed a subset of areas to evaluate inter-rater reliability. The reliability of the overall exposure prevention ratings was excellent (weighted kappa = 0.84). The rating scheme has good discriminatory power and reliability and shows promise as a broadly applicable and inexpensive tool for intervention needs assessment and effectiveness evaluation. Validation studies are needed as a next step. This assessment method complements quantitative exposure assessment with an upstream prevention focus.

A simple and effective method to ameliorate the interfacial properties of cellulosic fibre based bio-composites using poly (ethylene glycol) based amphiphiles

In order to overcome interfacial incompatibility issues in natural fibre reinforced polymer bio-composites, surface modifications of the natural fibres using complex and environmentally unfriendly chemical methods is necessary. In this paper, we demonstrate that the interfacial properties of cellulose-based bio-composites can be tailored through surface adsorption of polyethylene glycol (PEG) based amphiphilic block copolymers using a greener alternative methodology. Mixtures of water or water/acetone were used to form amphiphilic emulsions or micro-crystal suspensions of PEG based amphiphilic block copolymers, and their deposition from solution onto the cellulosic substrate was carried out by simple dip-coating. The findings of this study evidence that, by tuning the amphiphilicity and the type of building blocks attached to the PEG unit, the flexural and dynamic thermo-mechanical properties of cellulose-based bio-composites comprised of either polylactide (PLA) or high density polyethylene (HDPE) as a matrix, can be remarkably enhanced. The trends, largely driven by interfacial effects, can be ascribed to the combined action of the hydrophilic and hydrophobic components of these amphiphiles. The nature of the interactions formed across the fibre-matrix interface is discussed. The collective outcome from this study provides a technological template to significantly improve the performance of cellulose-based bio-composite materials.

The Integrating Theory, Evidence and Action (ITEA) method: a procedure for helping practitioners translate theory and research into action

Introduction: The aim of this article is to describe and explain a new method for integrating theory and evidence that enables practitioners to translate evidence into action applied in their practice. Method: A new multiple methods procedure called the Integrating Theory, Evidence and Action method is described. It is a mixed method that progresses through seven distinct steps: clinical question, framework, identification, deconstruction, analysis, reconstruction, and transfer/utilization. An example of using this method to review evidence around occupational therapy with people recovering from alcohol misuse and/or abuse is provided. Findings: This method highlights the importance of theory, tests the empirical strength of theories, includes diverse forms of evidence, and encourages the integration of knowledge within clinical practice. Conclusion: The Integrating Theory, Evidence and Action method is accessible and useful to practitioners and will support their efforts to make their practice evidence based. Current methods of evidence-based practice focus mostly on research evidence (particularly quantitative evidence); however, research is only one of the ways of knowing that practitioners draw upon to guide their practice. This method enables occupational therapists to integrate theory, evidence, and practice in a coherent and translatable way.

Ti-SrO metal matrix composites for bone implant materials

Titanium-strontia (Ti-SrO) metal matrix composites (MMCs) with 0, 1, 3 and 5% (weight ratio) of SrO have been fabricated through the powder metallurgy method. Increasing the weight ratio of SrO from 0 to 5%, the compressive strength of Ti-SrO MMCs increased from 982 MPa to 1753 MPa, while the ultimate strain decreased from 0.28 to 0.05. The elastic moduli of Ti-3SrO and Ti-5SrO MMCs were higher than those of Ti and Ti-1SrO MMC samples. Additionally, the micro hardness of Ti-SrO MMCs was enhanced from 59% to 190% with the addition of SrO. The enhanced compression strength and micro hardness of Ti-SrO MMCs were attributed to the Hall-Petch effect and the SrO dispersion strengthening in the Ti matrix. MTS assay results demonstrated that Ti-SrO MMCs with 3% SrO exhibited enhanced proliferation of osteoblast-like cells. Alkaline phosphatase activity of cells was not influenced significantly on the surface of Ti-SrO MMCs compared with pure Ti in a term longer than 10 days. The cell morphology on the Ti-SrO MMCs was observed using confocal microscopy and scanning electron microscopy, which confirmed that the Ti-3%SrO MMCs showed optimal in vitro biocompatibility. This journal is © the Partner Organisations 2014.

Collagen type-I leads to in vivo matrix mineralization and secondary stabilization of Mg-Zr-Ca alloy implants

Biodegradable magnesium-zirconia-calcium (Mg-Zr-Ca) alloy implants were coated with Collagen type-I (Coll-I) and assessed for their rate and efficacy of bone mineralization and implant stabilization. The phases, microstructure and mechanical properties of these alloys were analyzed using X-ray diffraction (XRD), optical microscopy and compression test, respectively, and the corrosion behavior was established by their hydrogen production rate in simulated body fluid (SBF). Coll-I extracted from rat tail, and characterized using fourier transform infrared (FT-IR) spectroscopy, was used for dip-coating the Mg-based alloys. The coated alloys were implanted into the femur bones of male New Zealand white rabbits. In vivo bone formation around the implants was quantified by measuring the bone mineral content/density (BMC/BMD) using dual-energy X-ray absorptiometry (DXA). Osseointegration of the implant and new bone mineralization was visualized by histological and immunohistochemical analysis. Upon surface coating with Coll-I, these alloys demonstrated high surface energy showing enhanced performance as an implant material that is suitable for rapid and efficient new bone tissue induction with optimal mineral content and cellular properties. The results demonstrate that Coll-I coated Mg-Zr-Ca alloys have a tendency to form superior trabecular bone structure with better osteoinduction around the implants and higher implant secondary stabilization, through the phenomenon of contact osteogenesis, compared to the control and uncoated ones in shorter periods of implantation. Hence, Coll-I surface coating of Mg-Zr-Ca alloys is a promising method for expediting new bone formation in vivo and enhancing osseointegration in load bearing implant applications.

Characterization of structure and energy of TixCy cluster at early formation stage in iron matrix by molecular dynamics

The structure, energy and bonding property of TixCy clusters formed in iron matrix were studied through molecular dynamics (MD) simulation method. The initial clusters with 1D-linear, 2D-ring, and 3D-tetrahedral structures were determined and their stability was calculated. The effect of temperature on the stability of the clusters was also discussed. In addition, the dissociation path of TiC clusters in iron matrix and the corresponding energy variation were analyzed. © 2014 Elsevier B.V. All rights reserved.

Characterization of microstructures and tensile properties of TRIP-aided steels with different matrix microstructure

Three different heat treatment processes have been proposed as a fundamental method to produce three kinds of TRIP-aided steels with polygonal ferritic matrix (F-TRIP), bainitic matrix (B-TRIP) and martensitic matrix (M-TRIP) in a newly designed low alloy carbon steel. By means of dilatometry study and detailed characterization, the relationships among transformation, microstructure and the resulting mechanical behavior were compared and analyzed for the three cases. The work hardening of the samples was evaluated by calculating the instantaneous n value as a function of strain. The M-TRIP sample exhibits the highest strength with the highest work hardening rate at low strains and subsequent rapid descending at high strains. In contrast, the B-TRIP sample has relatively high continuously constant work hardening behavior over strain levels greater than 0.067. The difference in work hardening behavior corresponds directly to the rate of the retained austenite-martensitic transformation during straining, which can be attributed to the carbon content, the morphology of the retained austenite and the matrix microstructure in the respective TRIP-aided samples. © 2014 Elsevier B.V.

Expansion of human hematopoietic stem/progenitor cells on decellularized matrix scaffolds

Umbilical cord blood (UCB) is one of the richest sources for hematopoietic stem/progenitor cells (HSPCs), with more than 3000 transplantations performed each year for the treatment of leukemia and other bone marrow, immunological, and hereditary diseases. However, transplantation of single cord blood units is mostly restricted to children, due to the limited number of HSPC per unit. This unit develops a method to increase the number of HSPCs in laboratory conditions by using cell-free matrices from bone marrow cells that mimic 'human-body niche-like' conditions as biological scaffolds to support the ex vivo expansion of HSPCs. In this unit, we describe protocols for the isolation and characterization of HSPCs from UCB and their serum-free expansion on decellularized matrices. This method may also help to provide understanding of the biochemical organization of hematopoietic niches and lead to suggestions regarding the design of tissue engineering-based biomimetic scaffolds for HSPC expansion for clinical applications.

Keyboard control method for virtual reality micro-robotic cell injection training

The rapid development of virtual reality offers significant potential for skills training applications. Our ongoing work proposes virtual reality operator training for the micro-robotic cell injection procedure. The interface between the operator and the system can be achieved in many different ways. The computer keyboard is ubiquitous in its use for everyday computing applications and also commonly utilized in virtual reality systems. Based on the premise that most people have experience in using a computer keyboard, as opposed to more sophisticated input devices, this paper considers the feasibility of using a keyboard to control the micro-robot for cell injection. In this study, thirteen participants underwent the experimental evaluation. The participants were asked to perform three simulated trial sessions in a virtual micro-robotic cell injection environment. Each session consisted of ten cell injection trials and relevant data for each trial were recorded and analyzed. Results showed participants' performance improvement after the three sessions. It was also observed that participants intuitively controlled multiple axes of the micro-robot simultaneously despite the absence of instruction on how to do so. This continued throughout the experiments and suggests skills transfer from other keyboard based interactions. Based on the results provided, it is suggested that keyboard control is a feasible, simple and low-cost control method for the virtual micro-robot.

A convex geometry-based blind source separation method for separating nonnegative sources

This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Spectral unmixing based on nonnegative matrix factorization with local smoothness constraint

Spectral unmixing (SU) is an emerging problem in the remote sensing image processing. Since both the endmember signatures and their abundances have nonnegative values, it is a natural choice to employ the attractive nonnegative matrix factorization (NMF) methods to solve this problem. Motivated by that the abundances are sparse, the NMF with local smoothness constraint (NMF-LSC) is proposed in this paper. In the proposed method, the smoothness constraint is utilized to impose the sparseness, instead of the traditional L1-norm which is restricted by the underlying column-sum-to-one requirement of the to the abundance matrix. Simulations show the advantages of our algorithm over the compared methods.

Flexible transfer learning framework for bayesian optimisation

Bayesian optimisation is an efficient technique to optimise functions that are expensive to compute. In this paper, we propose a novel framework to transfer knowledge from a completed source optimisation task to a new target task in order to overcome the cold start problem. We model source data as noisy observations of the target function. The level of noise is computed from the data in a Bayesian setting. This enables flexible knowledge transfer across tasks with differing relatedness, addressing a limitation of the existing methods. We evaluate on the task of tuning hyperparameters of two machine learning algorithms. Treating a fraction of the whole training data as source and the whole as the target task, we show that our method finds the best hyperparameters in the least amount of time compared to both the state-of-art and no transfer method.

A learning method for Top-N recommendations with incomplete data

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.