Highly selective amperometric glucose sensors based on rhodium-dispersed carbon paste/glucose oxidase electrodes are described. The dispersed rhodium particles exhibit efficient and preferential electrocatalytic activity toward the liberated peroxide species and allow cathodic detection of the glucose substrate at -0.10 V, with no interference from easily oxidizable constituents. Such operation thus eliminates the need for interferant-eliminating enzyme layers, permselective membrane barriers, or artificial electron mediators, hence greatly simplifying the sensor design and fabrication. The sensor selectivity is illustrated in the presence of physiological levels of ascorbic acid, uric acid, acetaminophen, salicylic acid, tyrosine, urea, galactose, and glutathione. Attractive dynamic properties and high sensitivity are also achieved in the absence of membrane barriers. A stable response is observed over several months.Because of the high demand for blood glucose measurements (particularly for treatment and control of diabetes), significant research and development efforts have been devoted to producing reliable glucose sensors.1-1 2 Particular attention has been given to the development of single-use (disposable) strips for self-testing of blood glucose and to implantable devices for continuous in vivo monitoring of this sugar. Amperometric enzyme electrodes have received considerable attention in connection with these self-testing and in vivo applications.

Three typical methanol-gasoline blends M10, M20, and M85 containing 10%, 20%, and 85% of methanol by volume, respectively, were used to investigate the effects of different methanol/gasoline ratios on engine power, thermal efficiency, and emissions, especially the exhaust methanol emission. A three-cylinder, port fuel injection engine was applied. Experimental results show that the engine power/torque ratio under the wide open throttle condition mainly depends on the amount of heat delivered to the engine. The addition of methanol significantly improves the brake thermal efficiency, while the methanol/gasoline ratio has a slight effect on it. Engine out CO and NO x emissions decrease with the increase of the methanol/gasoline ratio. The use of M85 leads to a reduction of CO and NO x by about 25% and 80%, respectively. A gas chromatograph is calibrated and used to measure the methanol emission. Measurement indicates that the addition of methanol in gasoline results in an increase of the unburnt CH 3 OH emission. And its concentration is nearly logarithmically proportional to the cyclically injected quantity. Because the response of the flame ionization detector to methanol is 40% that of hydrocarbon, the total hydrocarbon emission of the engine is revised. The nonmethanol hydrocarbons that resulted from gasoline are less affected by methanol addition, while the methanol emission is controlled independently by the cyclic quantity of fuel methanol injection.

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The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

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Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes of the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients' comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features' importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, neutrophil, IL-6, and LDH). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient's severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.

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