Size and habit of mineral particles in bone and mineralized callus during bone healing in sheep

Bone healing is known to occur through the successive formation and resorption of various tissues with different structural and mechanical properties. To get a better insight into this sequence of events, we used environmental scanning electron microscopy (ESEM) together with scanning small-angle X-ray scattering (sSAXS) to reveal the size and orientation of bone mineral particles within the regenerating callus tissues at different healing stages (2, 3, 6, and 9 weeks). Sections of 200 µm were cut from embedded blocks of midshaft tibial samples in a sheep osteotomy model with an external fixator. Regions of interest on the medial side of the proximal fragment were chosen to be the periosteal callus, middle callus, intercortical callus, and cortex. Mean thickness (T parameter), degree of alignment (ρ parameter), and predominant orientation (ψ parameter) of mineral particles were deduced from resulting sSAXS patterns with a spatial resolution of 200 µm. 2D maps of T and ρ overlapping with ESEM images revealed that the callus formation occurred in two waves of bone formation, whereby a highly disordered mineralized tissue was deposited first, followed by a bony tissue with more lamellar appearance in the ESEM and where the mineral particles were more aligned, as revealed by sSAXS. As a consequence, degree of alignment and mineral particle size within the callus increased with healing time, whereas at any given moment there were structural gradients, for example, from periosteal toward the middle callus.

Thermal performance optimization of building aspect ratio and south window size in five cities having different climatic characteristics of Turkey

The aim of the study is to establish optimum building aspect ratios and south window sizes of residential buildings from thermal performance point of view. The effects of 6 different building aspect ratios and eight different south window sizes for each building aspect ratio are analyzed for apartments located at intermediate floors of buildings, by the aid of the computer based thermal analysis program SUNCODE-PC in five cities of Turkey: Erzurum, Ankara, Diyarbakir, Izmir, and Antalya. The results are evaluated in terms of annual energy consumption and the optimum values are driven. Comparison of optimum values and the total energy consumption rates is made among the analyzed cities.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

An empirical study of applying ISO 9001 elements in large size Indonesian contractors

The effective implementation of such an ISO 9001 Quality Management System (QMS) in construction companies requires a proper and full implementation of the system to allow companies to improve the way they operate, by this means increasing profitability and market share, producing innovative and sustainable construction products, or improving employee and customer satisfaction. In light of this, this paper discusses the current status of QMS implementation, particularly related to the twenty elements of ISO 9001 within the grade 7 (G-7) category of Indonesian construction companies. A survey was conducted involving 403 respondents from 77 companies, to solicit an evaluation of the current implementation levels of the ISO 9001 elements. The survey findings indicated that for a large percentage of the sector surveyed they had ‘not so fully implemented’ the elements. Scrutiny of the data had also indicated elements that are ‘minimally implemented’, whilst none of the elements fell in the category of ‘fully implemented’. Based on these findings, it is suggested that the G-7 contractors may need to fully commit to practicing control of customer-supplied product and statistical techniques in order to enhance an effective implementation of ISO 9001 elements for ensuring better quality performance. These two elements are recognized as the least implemented of the quality elements.