Biofiltration for Treatment of Gases Contaminanted by Beta-caryophyllene

Fixed-film biological treatment processes, commonly referred to as biofilters, have been applied to successfully treat a wide variety of volatile organic compounds (VOCs) present in air emitted by a wide variety of industrial operations. The ability of biofilters to treat some classes of VOCs, however, has not been well established. In particular, the performance of biofilters treating sesquiterpenes has not been widely studied. In the research described herein, a laboratory-scale biofilter was operated to treat a synthetic waste gas stream containing â-caryophyllene, a sesquiterpene emitted by conifer trees and industrial wood processing operations. An enrichment culture developed in an initial experiment conducted in a sparged gas reactor was used to seed a laboratory-scale biofilter that was subsequently operated under mesophilic conditions for more than 262 days. During the first 244 days of continuous operation, there were seven distinct periods of biofilter operation, designated as Periods 1, 2, 3A, 3B, 4, 5A, and 5B. Period 1 was the initial period of biofilter operation following startup, and Periods 2 to 5 involved progressively higher gas flow rates and pollutant loading rates. To assess the impact of nutrient supply on biofilter performance, the concentrations of nutrients supplied to the biofilter changed at various time intervals. An additional experiment was conducted to evaluate the capacity of the system to recover following a 14-day interval of no â-caryophyllene supply. Collectively, data presented herein demonstrated that â-caryophyllene can be successfully treated using biofilters. This expands the classes of compounds successfully treated in biofilters to include sesquiterpenes. Data reported herein also demonstrate that local nutrient limitations can cause diminished treatment performance, a phenomenon observed in previous studies involving other pollutants. The biofilter was capable of relatively rapid recovery following resumption of pollutant loading following a 14-day starvation interval

Human posture recognition based on multiple features and rule learning

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random sub-Weili Ding space methods to create different samples and features for improved classification of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs

Industry convergence in rural tourism development: a China-featured term or a new initiative?

Industry convergence is a popular term that has been widely referenced in the context of rural tourism development in China. All levels of government (local, regional, national) in China have repeatedly addressed the significance of industry convergence in their tourism plans and related policies. Despite its popularity, limited studies at present have explored this concept in-depth. Using Huai’an as a case, this study applied a path analysis and reported the industry convergence process in a destination. The findings of this study can provide both theoretical and practical implications that are useful for tourism planners and policy makers

Coffee Consumption and Risk of Cardiovascular Diseases and All-Cause Mortality Among Men With Type 2 Diabetes

OBJECTIVE: Coffee consumption has been linked to detrimental acute metabolic and hemodynamic effects. We investigated coffee consumption in relation to risk of CVDs and mortality in diabetic men. RESEARCH DESIGN AND METHODS: We conducted a prospective cohort study including 3,497 diabetic men without CVD at baseline. RESULTS: After adjustment for age, smoking, and other cardiovascular risk factors, relative risks (RRs) were 0.88 (95% CI 0.50–1.57) for CVDs (P for trend = 0.29) and 0.80 (0.41–1.54) for all-cause mortality (P for trend = 0.45) for the consumption of ≥4 cups/day of caffeinated coffee compared with those for non–coffee drinkers. Stratification by smoking and duration of diabetes yielded similar results. RRs for caffeine intake for the highest compared with the lowest quintile were 1.02 (0.70–1.47; P for trend = 0.96) for CVDs and 0.96 (0.64–1.44; P for trend = 0.69) for mortality. CONCLUSIONS: These data indicate that regular coffee consumption is not associated with increased risk for CVDs or mortality in diabetic men

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm

Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer

Purpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm