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This chapter explores the concept of market infrastructure, which is tentatively defined as a materially heterogeneous arrangement that silently supports and structures the consummation of market exchanges. Specifically, the authors investigate the enactment of market infrastructure in the US grocery retail sector by exploring how barcodes and related devices contributed to modify its market infrastructure during the period 1967–2010. Combining this empirical case with insights from previous research, the authors propose that market infrastructures are relational, available for use, modular, actively maintained, interdependent, commercial, emergent and political. The authors argue that this conception of market infrastructure provides a powerful tool for unveiling the complex agencements and engineering efforts that underpin seemingly superficial, individual and isolated market exchanges.

Aims to check the validity of measurements of dynamic postures recorded by a body scanner.

Measurements between various anatomical landmarks have been taken both manually and using a 3D body scanner so that the validity of the measurements might be assessed when dynamic postures are adopted. Mechanical measurements of changes in the body surface dimensions have been compared with figures produced by a body scanner for both the standard natural position and for five dynamic postures, which must be accommodated when designing high‐performance garments.

Although the 3D body scanner collects data almost instantaneously and without physical contact with the target surface, the readings taken in respect of dynamic poses showed significant variations from manually‐taken measurements, with discrepancies as large as 6.8 cm over a 16 cm distance.

The research has only been carried out on a very limited number of subjects. However, significant differences between manual and automatic body measurements are clearly demonstrated.

The research showed that as there are as yet no universally‐accepted conventions for 3D scanner measurements, the results appear to be optimised for the natural anatomical position. Body‐scanners are not well‐suited to taking measurements of dynamic postures expected in sporting activities.

Measurements of anthropometric landmarks for high‐performance activities have not previously been assessed, and these results usefully indicate the limitations of current 3D scanning technology.

Examines the advantages of hand‐held scanners in conjunction with DTP. Discusses the capabilities of various scanners, the problem of aspect ratios, the Optics program, scanner installation, scanning photos, and dots per inch options. Concludes that a period of experimentation is needed to explore the capabilities of hand scanning.

Additive manufactured (AM) skull models are increasingly used to plan complex surgical cases and design custom implants. The accuracy of such constructs depends on the standard tessellation language (STL) model, which is commonly obtained from computed tomography (CT) data. The aims of this study were to assess the image quality and the accuracy of STL models acquired using different CT scanners and acquisition parameters.

Images of three dry human skulls were acquired using two multi-detector row computed tomography (MDCT) scanners, a dual energy computed tomography (DECT) scanner and one cone beam computed tomography (CBCT) scanner. Different scanning protocols were used on each scanner. All images were ranked according to their image quality and converted into STL models. The STL models were compared to gold standard models.

Image quality differed between the MDCT, DECT and CBCT scanners. Images acquired using low-dose MDCT protocols were preferred over images acquired using routine protocols. All CT-based STL models demonstrated non-uniform geometrical deviations of up to +0.9 mm. The largest deviations were observed in CBCT-derived STL models.

While patient-specific AM constructs can be fabricated with great accuracy using AM technologies, their design is more challenging because it is dictated by the correctness of the STL model. Inaccurate STL models can lead to ill-fitting implants that can cause complications after surgery.

This paper suggests that CT imaging technologies and their acquisition parameters affect the accuracy of medical AM constructs.

The topic of human skeletal analysis is a sensitive subject in North America. Laws and regulations surrounding research of human skeletal material make it difficult to use these remains to characterize various populations. Recent technology has the potential to solve this dilemma. Three-dimensional (3D) scanning creates virtual models of this material, and stores the information, allowing future studies on the material. The paper aims to discuss these issues.

To assess the potential of this methodology, the authors compared processing time, accuracy and costs of computer tomography (CT) scanner to the Artec Eva portable 3D surface scanner. Using both methodologies the authors scanned and 3D printed one adult individual. The authors hypothesize that the Artec Eva will create digital replicas of <5 percent error based on Buikstra and Ubelaker standard osteometric measurements. Error was tested by comparing the measurements of the skeletal material to the Artec data, CT data and 3D printed data.

Results show that larger bones recorded by the Artec Eva have <5 percent error of the original specimen while smaller more detailed images have >5 percent error. The CT images are closer to <5 percent accuracy, with few bones still >5 percent error. The Artec Eva scanner is inexpensive in comparison to a CT machine, but takes twice as long to process the Eva’s data. The Artec Eva is sufficient in replication of larger elements, but the CT machine is still a preferable means of skeletal replication, particularly for small elements.

This research paper is unique because it compares two common forms of digitization, which has not been done. The authors believe this paper would be of value to natural history curators and various researchers.

Laser scanning technique is used to measure and model objects using point cloud data generated laser pulses. Conventional techniques to construct 3D models are time consuming, costly and need more manpower. The purpose of this paper is to assess the 3D model of the Sultan Salahuddin Abdul Aziz Shah Mosque’s main dome using a terrestrial laser scanner.

A laser scanner works through line of sight, which indicates that multiple scans need to be taken from a different view to ensure a complete data set. Targets must spread in all directions, and targets should be placed on fixed structures and flat surfaces for the normal scan and fine scan. After the scanning operation, point cloud data from the laser scanner were cleaned and registered before a 3D model could be developed.

As a result, the reconstruction of the 3D model was successfully developed. The samples are based on the triangle dimension, curve line, horizontal dimension and vertical dimension at the dome. The standard deviation and accuracy are calculated based on the comparison of the 21 samples taken between the high-resolution and low-resolution scanning data.

There are many ways to develop the 3D model and based on this study, the less complex ways also produce the best result. The authors implement the different types of dimensions for the 3D model assessment, which have not yet been considered in the past.

A midwest hospital purchases new CT Scanners which are much faster than the existing technology. Processes in the radiology department are optimized to the older, existing scanners and technicians are unable to take full advantage of the new scanner speed. The hospital finds itself working to change the processes to suit the new scanners capabilities and take full advantage of their speed.

This case allows students to analyze process capacity and time performance in different settings and understand how process structure impacts both operational and financial performance.

Scanning devices make it possible to transfer images from paper onto disc, where they can be processed, displayed and printed.

In recent years there have been many inquiries and studies by government agencies, media reporters, and academic institutions about pricing accuracy at retail stores that use scanners. Some of these reports have even accused retailers of using scanners to intentionally overcharge customers. This study is based on checking 146,518 items in over 2,000 stores in nine states. Analysis of these data found a price accuracy rate of 96.13 per cent with undercharges occurring more frequently than overcharges. In addition, there was a statistically significant difference between pricing accuracy at grocery stores and other retail stores. Grocery stores recorded a higher accuracy level. There is still room for improvement. Only 58 per cent of the stores inspected reached a 98 per cent pricing accuracy standard recommended by the National Conference of Weights and Measures.

This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for prediction of silver nanoparticles (AgNPs) and dye concentrations on AgNP-treated silk fabrics.

For estimation of the dye and AgNPs concentration using image processing, the silk fabrics were scanned under the condition of 200 pixels per inch. The red green blue (RGB) values of scanned images were obtained after applying the median filter. Then, the relationship between scanner RGB values and dye and AgNPs concentrations were obtained by using artificial intelligence methods such as ANFIS and ANNs.

The best result was achieved by the ANFIS system for calculation concentration of dye with 0.07% error and concentration of AgNPs with 0.008 (gr/l) error. The obtained results indicate that the performance of the ANFIS system method is better than the other methods.

Using a scanner-based artificial intelligence technique for prediction of nanosilver and dye content on silk fabric.