VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

Data Mining and Knowledge Discovery - The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that...     

Skew Gaussian processes for classification

Machine Learning - Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have...     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.     

Oxidative post-treatments for enhanced corrosion resistance of aluminium electrodeposited from ionic liquids

In this paper we report the results of a study on the corrosion properties of carbon steel (UNI Fe360B) coated with a thin aluminium layer electrodeposited from ionic liquids and subsequently post-treated in an oxidizing environment. Two different types of treatments were investigated: thermal oxidation in air and electrochemical anodization. We found that heat treatments provided only a modest improvement of the anticorrosion properties while the electrochemical treatment produced a significant improvement.     

The morphological properties of PP coextruded tape fabrics

In the field of self‐reinforced composites many researchers have focused their attention on the coextruded tapes composed of polypropylene core and PP/PE copolymer skin. Two similar commercial fabrics (P and T) have been compared in respect of their peel resistance. For both materials, peel resistance has a periodic trend that regularly follows fabric weave style. T has demonstrated an average peel resistance and a well‐bonded area slightly greater than P. Skin/core interfacial properties have been investigated and a crosscheck between differential scanning calorimetry (DSC) and Raman spectroscopy has been adopted to understand the influence of skin structure on consolidated laminate. DSC curves exhibit three melting peaks during first heating for both fabrics, corresponding to copolymer, skin/core interface, and core melting. After consolidation at 140°C stretching‐induced superstructure and PP crystallinity degree are preserved. The presence of PP/PE copolymer + PE blend only in fabric P has been pointed out and PE content has been calculated. POLYM. ENG. SCI., 56:727–734, 2016. © 2016 Society of Plastics Engineers     

Synthesis,Computational Studies and Assessment of in Vitro Activity of Squalene Derivatives as Carbonic Anhydrase Inhibitors

We report novel molecules incorporating the nontoxic squalene scaffold and different carbonic anhydrase inhibitors (CAIs). Potent inhibitory action, in the low-nanomolar range, was detected against isoforms hCA II for sulfonamide derivatives, which proved to be selective against this isoform over the tumor-associate hCA IX and XII isoforms. On the other hand, coumarin derivatives showed weak potency but high selectivity against the tumor-associated isoform CA IX. These compounds are interesting candidates for preclinical evaluation in glaucoma or various tumors in which the two enzymes are involved. In addition, an in silico study of inhibitor-bound hCA II revealed extensive interactions with the hydrophobic pocket of the active site and provided molecular insights into the binding properties of these new inhibitors.     

The influence of consolidation and artificial weathering on all‐PP composite behavior

The purpose of this work was to evaluate the effect of consolidation parameters on the mechanical properties of all‐polypropylene composites. Consolidation time does not induce any significant influence, whereas processing temperature and in particular coextruded tape characteristics deeply affect laminate performance. The DSC analysis demonstrated that the proximity of copolymer and homopolymer melting points results in a reduction in the operating window. This suggests the implementation of a more effective temperature control during thermoforming to avoid reinforcement relaxation and localized melting. Artificial weathering was conducted under noon summer light condition to assess the effect of ultraviolet exposure on laminates. After 100 and 250 h, sample performance drops significantly, with overall strength and elongation reduction up to 70 and 88%, respectively. Low molecular weight by‐products were identified using Raman spectroscopy on specimens surface, confirming the mechanical property decrement due to photo‐oxidation process. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41283.     

A genetic approach to joint routing and link scheduling for wireless mesh networks

Wireless mesh networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self-organized networks in places where wired infrastructures are not available or not deemed to be worth deploying. This paper investigates the joint link scheduling and routing issues involved in the delivery of a given backlog from any node of a wireless mesh network towards a specific node (which acts as a gateway), within a given deadline. Scheduling and routing are assumed to be aware of the physical interference among nodes, which is modeled in the paper by means of a signal-to-interference ratio. Firstly, we present a theoretical model which allows us to formulate the task of deriving joint routing and scheduling as an integer linear programming problem. Secondly, since the problem cannot be dealt with using exact methods, we propose and use a technique based on genetic algorithms. To the best of our knowledge, these algorithms have never been used before for working out these kinds of optimization problems in a wireless mesh environment. We show that our technique is suitable for this purpose as it provides a good trade-off between fast computation and the overall goodness of the solution found. Our experience has in fact shown that genetic algorithms would seem to be quite promising for solving more complex models than the one dealt with in this paper, such as those including multiple flows and multi-radio multi-channels.