Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.     

软件:有效数字化广告牌背后的"大脑"

日益精密且成本逐渐下降的的显示技术使人们对下一代户内、户外广告的"数字化"充满了期待。然而运行显示网络必不可少的"大脑"是不容忽略的。下面综述了成功的数字广告系统的软件需求,以及这些系统的工作方式。     

Effects of three crosslinkers on colored pH‐responsive core–shell latexes

Environmentally responsive hydrogels have become attractive research subjects for many applications. In this work, a series of pH‐sensitive and color‐changing nanoparticles (nanogels) with core–shell structures were synthesized. The cores were blue latex particles synthesized by a miniemulsion technique with styrene and methacrylic acid as monomers and Neozapon Blue 807 as a dye. The shell was a pH‐sensitive polymer that was precipitation‐polymerized onto the core from 2‐(diethylamino) ethyl methacrylate with N,N′‐methylenebisacrylamide, poly(propylene glycol) diacrylate, and divinylbenzene as crosslinking agents. In the resultant latexes, the color was observed to change from deep blue to pale blue as the pH of the system was changed from a high of 10 to a low of 3. The synthesized latexes and particles (gels) were then characterized with an ultraviolet–visible spectrometer, dynamic light scattering, Fourier transform infrared, and nuclear magnetic resonance. Atomic force microscopy was also used to investigate the different morphologies of the particles after the synthesized latexes were dried at different pH values. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

印度的开放

印度在对外国公司开放零售市场上已经迈出了第一步，但仍然有很长的路要走。     

Thai power: Net metering comes to Thailand

Thailand has recently introduced net metering legislation that provides streamlined procedures for small renewable energy generators to connect to the grid, and guarantees both a market and good prices. The new laws create income opportunities for rural communities based on locally produced, clean, renewable energy supplies and offer significant potential to reduce Thailand's dependence on imported oil and coal. Chris Greacen, Chuenchom Sangarasri Greacen and Rich Plevin report.     

Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.     

Distributed Sensing and Control Technology for Intelligent Civil Infrastructure Systems

Abstract: Sensing and control functions are the fundamental technologies on which many civil infrastructure control and monitoring systems are constructed. Early civil infrastructure control and monitoring systems employed a centralized computing model for sensor processing and actuator control. These systems required extensive wiring to route power, sensor, and control signals among the distributed input and output devices. Furthermore, the operation of the entire system depended on one or two computers. Abstractly, these civil infrastructure applications are a realtime data acquisition and control system. Due to the dramatic decrease in computing costs, it is now feasible to construct these real-time systems using dozens or even hundreds of distributed processors linked with a data communication network. This allows the controllers to be located adjacent to sensors and actuators, thereby reducing the need for long lengths of multiconductor wire that is subject to noise and breakage. Also, failure of a single processor does not necessarily cause the entire system to fail. This paper introduces the reader to a technology called LonWorks for constructing distributed sensing and control systems. The LonWorks technology is essentially a data communication network that links together extremely small computers called Neurons. Particular emphasis is placed on describing the communication architecture and emphasizing how the distributed Neuron communication processors are designed to ensure a truly modular and open architecture.     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.