A statistical model which combines features of factor analytic and analysis of variance techniques

This paper describes a method of matrix decomposition which retains the ability of factor analytic techniques to summarize data in terms of a relatively low number of coordinates; but at the same time, does not sacrifice the useful analysis of variance heuristic of partitioning data matrices into independent sources of variation which are relatively simple to interpret. The basic model is essentially a two-way analysis of variance model which requires that the matrix of interaction parameters be decomposed by using factor analytic techniques. Problems of judging statistical significance are discussed; and an illustrative example is presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45727/1/11336_2005_Article_BF02289676.pd

Stem-Level Bucking Pattern Optimization in Chainsaw Bucking Based on Terrestrial Laser Scanning Data

Cross-cutting of a tree into a set of assortments (»bucking pattern«) presents a large potential for optimizing the volume and value recovery; therefore, bucking pattern optimization has been studied extensively in the past. However, it has not seen widespread adoption in chainsaw bucking, where time consuming and costly manual measurement of input parameters is required for taper curve estimation. The present study investigated an alternative approach, where taper curves are fit based on terrestrial laser scanning data (TLS), and how deviations from observed taper curves (REF) affect the result of bucking pattern optimization. In addition, performance of TLS was compared to a traditional, segmental taper curve estimation approach (APP) and an experienced chainsaw operator’s solution (CHA). A mature Norway Spruce stand was surveyed by stationary terrestrial laser scanning. In TLS, taper curves were fit by a mixed-effects B-spline regression approach to stem diameters extracted from 3D point cloud data. A network analysis technique algorithm was used for bucking pattern optimization during harvesting. Stem diameter profiles and the chainsaw operator’s bucking pattern were obtained by manual measurement. The former was used for post-operation fit of REF taper curves by the same approach as in TLS. APP taper curves were fit based on part of the data. For 35 trees, TLS and APP taper curves were compared to REF on tree, trunk and crown section level. REF and APP bucking patterns were optimized with the same algorithm as in TLS. For 30 trees, TLS, APP and CHA bucking patterns were compared to REF on operation and tree level. Taper curves were estimated with high accuracy and precision (underestimated by 0.2 cm on average (SD=1.5 cm); RMSE=1.5 cm) in TLS and the fit outperformed APP. Volume and value recovery were marginally higher in TLS (0.6%; 0.9%) than in REF on operation level, while substantial differences were observed for APP (–6.1%; –4.1%). Except for cumulated nominal length, no significant differences were observed between TLS and REF on tree level, while APP result was inferior throughout. Volume and value recovery in CHA was significantly higher (2.1%; 2.4%), but mainly due to a small disadvantage of the optimization algorithm. The investigated approach based on terrestrial laser scanning data proved to provide highly accurate and precise estimations of the taper curves. Therefore, it can be considered a further step towards increased accuracy, precision and efficiency of bucking pattern optimization in chainsaw bucking

Toughening of thermosetting polyimides

Work directed toward increasing the resistance to crack propagation of thermoset polyimides is described. Rubber modification and Teflon microfiber impregnation techniques for increasing fracture toughness are investigated. Unmodified Kerimid 601 has a fracture surface work value of 0.20 in-lbs/sq in. Dispersed particles of amine terminated butadiene acrylonitrile liquid rubber or of silicone rubber do not raise this value much. By contrast, 5 percent of well fibrillated Teflon produces an eight-fold increase in fracture toughness. Further process improvements should increase this factor to 20-30

Visualising interactions in bi- and triadditive models for three-way tables

This paper concerns the visualisation of interaction in three-way arrays. It extends some standard ways of visualising biadditive modelling for two-way data to the case of three-way data. Three-way interaction is modelled by the Parafac method as applied to interaction arrays that have main effects and biadditive terms removed. These interactions are visualised in three and two dimensions. We introduce some ideas to reduce visual overload that can occur when the data array has many entries. Details are given on the interpretation of a novel way of representing rank-three interactions accurately in two dimensions. The discussion has implications regarding interpreting the concept of interaction in three-way arrays

A Prototype for Automated Delimitation of Work Cycles from Machine Sensor Data in Cable Yarding Operations

The demand for increased efficiency in timber harvesting has traditionally been met by continuous technical improvements in machines and an increase in mechanisation. The use of active and passive sensors on machines enables improvements in aspects such as operational efficiency, fuel consumption and worker safety. Timber harvesting machine manufacturers have used these technologies to improve the maintenance and control of their machines, to select and optimise harvesting techniques and fuel consumption. To a more limited extent, it has also been used to evaluate the time taken to complete tasks. The systematic use of machine sensor data, in a central database or cloud solution is a more recent trend. Machine data is recorded over long periods of time and at high resolution. This data therefore has considerable potential for scientific investigations. For mechanised timber harvesting operations, this could include a better understanding of the interaction between productivity and operational parameters, which first of all requires an efficient determination of cycle time. This study was the first to automatically delimitate tower yarder cycle times from machine sensor data. In addition to machine sensor data, cycle times were collected through a traditional manual time and motion study, and cycle times from both studies were compared to a reference cycle time determined from video footage of the yarder in operation. Based on three days of detailed time study, the total cycle time in the classic manual time (–1.3%) and in the machine sensor data (–1.2%) was only slightly shorter than in the reference study, and the average cycle time did not differ significantly (classic manual time study: –0.08±0.94 min, p=0.997; machine sensor data study: –0.08±0.26 min, p=0.997). However, the accuracy of the machine sensor approach (RMSE=0.92) was more than three times higher than that of the classic manual time study (RMSE=0.27). With the integration of sensors on forestry machines now being commonplace, this study shows that machine sensor data can be reliably interpreted for time study purposes such as machine or system optimisation. This eliminates the need for manual time study, which can be both cumbersome and dependent on the experience of the observer, and allows long term data sets to be obtained and analysed with comparatively little effort. However, a truly automated time study needs to be supplemented with automated determination of and linkage to other operational parameters, such as yarding and lateral yarding distance or load volume

Chronic AMPK activity dysregulation produces myocardial insulin resistance in the human Arg302Gln-PRKAG2 glycogen storage disease mouse model

BACKGROUND: The cardiac PRKAG2 mutation in the γ2-subunit of adenosine monophosphate activated kinase (AMPK) is characterized by excessive glycogen deposition, hypertrophy, frequent arrhythmias, and progressive conduction system disease. We investigated whether myocardial glucose uptake (MGU) was augmented following insulin stimulation in a mouse model of the PRKAG2 cardiac syndrome. METHODS: Myocardial and skeletal muscle glucose uptake was assessed with 2-[(18)F]fluoro-2-deoxyglucose positron emission tomography imaging in n = 3 transgenic wildtype (TGwt) vs n = 7 PRKAG2 mutant (TGmut) mice at baseline and 1 week later, 30 min following acute insulin. Systolic function, cardiac glycogen stores, phospho-AMPK α, and insulin-receptor expression levels were analyzed to corroborate to the in vivo findings. RESULTS: TGmut Patlak Ki was reduced 56% at baseline compared to TGwt (0.3 ± 0.2 vs 0.7 ± 0.1, t test p = 0.01). MGU was augmented 71% in TGwt mice following acute insulin from baseline (0.7 ± 0.1 to 1.2 ± 0.2, t test p < 0.05). No change was observed in TGmut mice. As expected for this cardiac specific transgene, skeletal muscle was unaffected at baseline with a 33% to 38% increase (standard uptake values) for both genotypes following insulin stimulation. TGmut mice had a 47% reduction in systolic function with a fourfold increase in cardiac glycogen stores correlated with a 29% reduction in phospho-AMPK α levels. There was no difference in cardiac insulin receptor expression between mouse genotypes. CONCLUSIONS: These results demonstrate a correlation between insulin resistance and AMPK activity and provide the basis for the use of this animal model for assessing metabolic therapy in the treatment of affected PRKAG2 patients