Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Nutrient requirements of dairy cattle , Nutrient requirements of dairy cattle , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Nutrient requirements of dairy cattle

Micromachining technology was used to prepare chemical analysis systems on glass chips (1 centimeter by 2 centimeters or larger) that utilize electroosmotic pumping to drive fluid flow and electrophoretic separation to distinguish sample components. Capillaries 1 to 10 centimeters long etched in the glass (cross section, 10 micrometers by 30 micrometers) allow for capillary electrophoresis-based separations of amino acids with up to 75,000 theoretical plates in about 15 seconds, and separations of about 600 plates can be effected within 4 seconds. Sample treatment steps within a manifold of intersecting capillaries were demonstrated for a simple sample dilution process. Manipulation of the applied voltages controlled the directions of fluid flow within the manifold. The principles demonstrated in this study can be used to develop a miniaturized system for sample handling and separation with no moving parts.

Micromachining a miniaturized capillary electrophoresis-based chemical analysis system on a chip

2.2. New Approaches 707 2.2.1. Optical Sensor Systems 707 2.2.2. Mass Spectrometry 708 2.2.3. Ion Mobility Spectrometry 708 2.2.4. Gas Chromatography 709 2.2.5. Infrared Spectroscopy 709 2.2.6. Use of Substance-Class-Specific Sensors 709 2.3. Combined Technologies 710 3. Companies 710 4. Application Areas 710 4.1. Food and Beverage 712 4.2. Environmental Monitoring 715 4.3. Disease Diagnosis 716 5. Research and Development Trends 718 5.1. Sample Handling 719 5.2. Filters and Analyte Gas Separation 719 5.3. Data Evaluation 720 6. Conclusion 721 7. References 722

/pdf/electronic-nose-current-status-and-future-trends-475b22mugv.pdf

Electronic nose: current status and future trends.

Metal oxide semiconductor gas sensors are utilised in a variety of different roles and industries. They are relatively inexpensive compared to other sensing technologies, robust, lightweight, long lasting and benefit from high material sensitivity and quick response times. They have been used extensively to measure and monitor trace amounts of environmentally important gases such as carbon monoxide and nitrogen dioxide. In this review the nature of the gas response and how it is fundamentally linked to surface structure is explored. Synthetic routes to metal oxide semiconductor gas sensors are also discussed and related to their affect on surface structure. An overview of important contributions and recent advances are discussed for the use of metal oxide semiconductor sensors for the detection of a variety of gases—CO, NOx, NH3 and the particularly challenging case of CO2. Finally a description of recent advances in work completed at University College London is presented including the use of selective zeolites layers, new perovskite type materials and an innovative chemical vapour deposition approach to film deposition.

https://www.mdpi.com/1424-8220/10/6/5469/pdf

Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring

Electronic noses (e-noses) employ an array of chemical gas sensors and have been widely used for the analysis of volatile organic compounds. Pattern recognition provides a higher degree of selectivity and reversibility to the systems leading to an extensive range of applications. These range from the food and medical industry to environmental monitoring and process control. Many types of data analysis techniques have been used on the data produced. This review covers aspects of analysis from data normalisation methods to pattern recognition and classification techniques. An overview of data visualisation such as non-linear mapping and multivariate statistical techniques is given. Focus is then on the use of artificial intelligence techniques such as neural networks and fuzzy logic for classification and genetic algorithms for feature (sensor) selection. Application areas are covered with examples of the types of systems and analysis methods currently in use. Future trends in the analysis of sensor array data are discussed.

Data analysis for electronic nose systems

Chemical sensors have been widely used for the analysis of volatile organic compounds. Employing chemical sensors in an array format with pattern recognition provides a higher degree of selectivity and reversibility leading to an extensive range of applications. When such systems are used for odour analysis they are termed ‘electronic noses’. Application of electronic noses ranges from the food industry, medical industry to environmental monitoring and process control. Many types of different gas sensors have been employed in the array. These include conducting polymers, metal oxide semiconductors, piezoelectric, optical fluorescence and amperometric gas sensors The transducer principle of these sensors is varied and is discussed in detail within this review. Examples of the current trends in sensor array technology as well as the applications to which the sensor-based noses have been applied are also discussed.

Chemical Sensors for Electronic Nose Systems

First human experiments with a novel non-invasive, non-optical continuous glucose monitoring system.

Analysis of waste hierarchy in the European waste directive 2008/98/EC

A low-cost microBR (microbioreactor) made from PTFE [poly(tetrafluoroethylene)] was used to cultivate a model organism, Pseudomonas aeruginosa DS10-129. The progress of bioprocessing was monitored by comparing the growth of the organism in a microBR, a conventional bench scale bioreactor and a shake flask. Under the microBR conditions, the organism produced 23 mg/ml of pyocyanin that had antimicrobial effects against Bacillus subtilis, Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas teessidea and Pseudomonas clemancea. Furthermore, it produced a total of 106 microg/ml of effective biosurfactants consisting of dirhamnolipids (RL2) and monorhamnolipids (RL1). The biosurfactants reduced the surface tension of distilled water from 72 to 27.9 mN/m and emulsified kerosene by 71.30%. The pyocyanin and rhamnolipids were produced during the exponential and stationary phases of growth respectively. The results of the microBR were comparable to those obtained using conventional scale methods.

Zulfiqur Ali

Papers

Data analysis for electronic nose systems

Chemical Sensors for Electronic Nose Systems

First human experiments with a novel non-invasive, non-optical continuous glucose monitoring system.

Analysis of waste hierarchy in the European waste directive 2008/98/EC

Production of rhamnolipid biosurfactants by Pseudomonas aeruginosa DS10-129 in a microfluidic bioreactor.