Consumer perception of extruded snacks containing brown rice and dried mushroom

Effects of mushroom type, seasoning and health benefit information (HBI) on consumers’ saltiness expectation, sensory liking, elicited emotions and purchase intent (PI) of extruded snacks were investigated. Five snacks were evaluated: straw mushroom (Volvariella volvacea) extrudates without (SME) or with seasoning (SMES), phoenix mushroom (Pleurotus pulmonarius) extrudates without (PME) or with seasoning (PMES), and the control without mushroom and seasoning. Hedonic scores and positive emotions were generally higher for seasoned mushroom-containing snacks (SMES and PMES) with 65% and 75.83% of consumers reporting willingness to purchase, respectively, after receiving HBI. Bored, interested and satisfied were identified as significant emotional predictors for PI odds. Flavour, saltiness, overall liking, bored, good and interested were critical attributes, differentiating snacks. This study demonstrated that sensory liking and PI of extruded brown rice-based snacks containing mushroom could be improved through savoury seasoning addition, which also allowed saltiness expectations to be met.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Characteristics of tiger nut milk: effects of milling

A standard method for the extraction of tiger nut milk has been introduced. It has been shown that, although milling duration improves the yield of tiger nut milk solids and its nutrient composition, there is a quantifiable loss of nutrient in the pressing residue during milk extraction. Milling duration improved the colloidal stability of the milk against creaming during 16 h of storage. A higher milling intensity resulted in the aggregation of biological polymers which resulted in colloidal destabilisation. Milling improved the lightness and stability and reduced browning rate of the tiger nut milk during storage. This report is important for the production of tiger nut milk of consistent and comparable characteristics. Milling has been introduced as a processing method for the qualitative and quantitative modulation of the properties of tiger nut milk. It is recommended to develop further strategies to improve the colloidal stability of tiger nut milk as a beverage.     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

Micro- and macroergonomic changes in mental workload and medication safety following the implementation of new health IT

Health information technology (IT) is a promising way to achieve safer medication management in the delivery of healthcare. However, human factors/ergonomics dictates that in order to make the complex, cognitive work of healthcare delivery safer, health IT must properly support human cognition. This means, for example, that new health IT must reduce, not increase, workload during safety-critical tasks. The present study was the first to quantitatively assess the short- and long-term impact of bar coded medication administration (BCMA) IT on nurses' mental workload as well as on perceived medication safety. One-hundred seventy registered nurses across 3 dissimilar clinical units at an academic, freestanding pediatric hospital in the Midwest US participated in surveys administered before, 3 months after, and 12 months after the hospital implemented BCMA. Nurses rated their external mental workload (interruptions, divided attention, being rushed) and internal mental workload (concentration, mental effort) during medication administration tasks as well as the likelihood of each of three medication safety events: medication administration errors, medication errors on the clinical unit, and clinical unit-level adverse drug events. Clinical unit differences were assessed. Findings generally confirmed the hypothesis that external but not internal mental workload was associated with the perceived likelihood of a medication safety event. Comparisons of mental workload from pre- to post-BCMA revealed statistically significant changes in the critical care unit only. Medication safety appeared to improve over the long term in the hematology/oncology unit only. In the critical care and medical/surgical units, medication safety exhibited short-term improvements that were eliminated over time. Changes in mental workload and medication safety, two classically microergonomic constructs, were deeply embedded in macroergonomic phenomena. These included the fit between the BCMA system and the nature of nursing work, the process of BCMA implementation, and BCMA interactions with concurrent changes occurring in the hospital. Findings raise questions about achieving sustainable performance improvement with health IT as well as the balance between micro- and macroergonomic approaches to studying technology change.Relevance to industryDesigners must consider how technology changes cognitive work, including mental workload. Hospitals and other implementers of technology must ensure that new technology fits its users, their tasks, and the context of use, which may entail tailoring implementation, for example, to specific clinical units. Evaluators must look over time to assess both changes in cognitive work and implementation issues. Healthcare practitioners must also recognize that new technology means a complex transformation to an already complex sociotechnical system, which calls for a macroergonomic approach to design and analysis.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.