Integrated manufacturing,employee and business performance: Australian and New Zealand evidence

This article reports on a large field study of Integrated Manufacturing (IM), consisting of Total Quality Management (TQM), Just in Time (JIT) and Advanced Manufacturing Technology (AMT). The relationship between these facets of Integrated Manufacturing and performance was examined empirically, including measures of employee performance and manufacturing performance. There was a significant correlation between employee performance and manufacturing performance. While all three facets of Integrated Manufacturing were relatively strongly connected with employee performance, manufacturing performance was significantly but less strongly connected to the intensity of TQM and JIT than employee performance, and not connected with the intensity of Advanced Manufacturing Technology. Some second-order interactive effects between the facets of Integrated Manufacturing and performance were found. Further, by partitioning the large data set into three using the AMT factor as a cutting variable, it was found that the stronger the AMT environment, the stronger the explanatory power of TQM and JIT on both employee and manufacturing performance. The interactive impact of AMT and TQM was significantly positively related to both employee and manufacturing performance.     

Outlier Respecting Points Approximation

In this paper, we consider a generalized problem formulation of computing a functional curve to approximate a point set in the plane with outliers. The goal is to seek a solution that not only optimizes its original objectives, but also somehow accommodates the impact of the outliers. Based on a new model of accommodating outliers, we present efficient geometric algorithms for various versions of this problem (e.g., the approximating functions are step functions or piecewise linear functions, the points are unweighted or weighted, etc). All our results are first known. Our new model and techniques for handling outliers may be useful to other applications as well.     

Market penetration analysis of electric vehicles in the German passenger car market towards 2030

There is currently intensive public discussion of fuel cell electric vehicles (FCEV) and other electric powertrains, such as battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV) and hybridized combustion engine vehicles (HEV). In this context, the German government has set the target of one million electric vehicles on the road by 2020, and six million by 2030 [1]. The goal of this paper is to identify the possible market share of electric vehicles in the German new car fleet in three scenarios in the timeframe from 2010 to 2030. The VECTOR21 vehicle technology scenario model is used to model the fleet in three scenarios. In the reference scenario with business-as-usual parameters, 189,000 electric vehicles will be sold in Germany by 2020. Scenario two with purchase price incentives from 5000 EUR, high oil prices, and low prices for hydrogen and electricity will result in 727,000 vehicles. In the last scenario with substantial OEM mark-up reductions and external conditions as in the business-as-usual scenario, 3.28 million vehicles will be sold.     

Nano-Porous Composites of Activated Carbon–Metal Organic Frameworks (Fe-BDC@AC) for Rapid Removal of Cr (VI): Synthesis,Adsorption, Mechanism,and Kinetics Studies

Journal of Inorganic and Organometallic Polymers and Materials - Metal–organic frameworks (MOFs) are a group of porous materials that display potential in the elimination of toxic industrial...     

Co‐optimization of SnS absorber and Zn(O,S) buffer materials for improved solar cells

Thin‐film solar cells consisting of earth‐abundant and non‐toxic materials were made from pulsed chemical vapor deposition (pulsed‐CVD) of SnS as the p‐type absorber layer and atomic layer deposition (ALD) of Zn(O,S) as the n‐type buffer layer. The effects of deposition temperature and annealing conditions of the SnS absorber layer were studied for solar cells with a structure of Mo/SnS/Zn(O,S)/ZnO/ITO. Solar cells were further optimized by varying the stoichiometry of Zn(O,S) and the annealing conditions of SnS. Post‐deposition annealing in pure hydrogen sulfide improved crystallinity and increased the carrier mobility by one order of magnitude, and a power conversion efficiency up to 2.9% was achieved. Copyright © 2014 John Wiley & Sons, Ltd.     

Effect of silane structure on the properties of silanized multiwalled carbon nanotube-epoxy nanocomposites

Multiwalled carbon nanotubes (MWCNTs) were functionalized with aminosilanes via an aqueous deposition route. The size and morphology of siloxane oligomers grafted to the MWCNTs was tuned by varying the silane functionality and concentration and their effect on the properties of a filled epoxy system was investigated. The siloxane structure was found to profoundly affect the thermo-mechanical behavior of composites reinforced with the silanized MWCNTs. Well-defined siloxane brushes increased the epoxy T_g by up to 19 °C and significantly altered the network relaxation dynamics, while irregular, siloxane networks grafted to the MWCNTs had little effect. The addition of both types of silanized MWCNTs elicited improvements in the strength of the nanocomposites, but only the well-defined siloxane brushes engendered dramatic improvements in toughness. Because the silanization reaction is simple, rapid, and performed under aqueous conditions, it is also an industrially attractive functionalization route.     

On problems without polynomial kernels

Kernelization is a strong and widely-applied technique in parameterized complexity. A kernelization algorithm, or simply a kernel, is a polynomial-time transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parameter bounded by a function of the parameter in the input. A kernel is polynomial if the size and parameter of the output are polynomially-bounded by the parameter of the input.In this paper we develop a framework which allows showing that a wide range of FPT problems do not have polynomial kernels. Our evidence relies on hypothesis made in the classical world (i.e. non-parametric complexity), and revolves around a new type of algorithm for classical decision problems, called a distillation algorithm, which is of independent interest. Using the notion of distillation algorithms, we develop a generic lower-bound engine that allows us to show that a variety of FPT problems, fulfilling certain criteria, cannot have polynomial kernels unless the polynomial hierarchy collapses. These problems include k-Path, k-Cycle, k-Exact Cycle, k-Short Cheap Tour, k-Graph Minor Order Test, k-Cutwidth, k-Search Number, k-Pathwidth, k-Treewidth, k-Branchwidth, and several optimization problems parameterized by treewidth and other structural parameters.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.