A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation; in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal; the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces

A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation; in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal; the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces

Homoacetogenesis and microbial community composition are shaped by pH and total sulfide concentration

Biological CO2 sequestration through acetogenesis with H-2 as electron donor is a promising technology to reduce greenhouse gas emissions. Today, a major issue is the presence of impurities such as hydrogen sulfide (H2S) in CO2 containing gases, as they are known to inhibit acetogenesis in CO2-based fermentations. However, exact values of toxicity and inhibition are not well-defined. To tackle this uncertainty, a series of toxicity experiments were conducted, with a mixed homoacetogenic culture, total dissolved sulfide concentrations ([TDS]) varied between 0 and 5 mM and pH between 5 and 7. The extent of inhibition was evaluated based on acetate production rates and microbial growth. Maximum acetate production rates of 0.12, 0.09 and 0.04 mM h(-1) were achieved in the controls without sulfide at pH 7, pH 6 and pH 5. The half-maximal inhibitory concentration (IC50qAc) was 0.86, 1.16 and 1.36 mM [TDS] for pH 7, pH 6 and pH 5. At [TDS] above 3.33 mM, acetate production and microbial growth were completely inhibited at all pHs. 16S rRNA gene amplicon sequencing revealed major community composition transitions that could be attributed to both pH and [TDS]. Based on the observed toxicity levels, treatment approaches for incoming industrial CO2 streams can be determined

Glucocorticoids rapidly inhibit oxytocin-stimulated adrenocorticotropin release from rat anterior pituitary cells, without modifying intracellular calcium transients

Glucocorticoid hormones suppress the secretion of ACTH evoked by secretagogues such as CRF and arginine vasopressin. In this study, we investigated the effects of glucocorticoids on ACTH release induced by oxytocin (OT) and on intracellular free calcium ion levels in corticotropes prepared from the adenohypophyses of female Wistar rats. Pulsatile additions of physiological concentration of OT (10 nM) to superfused anterior pituitary cells caused pulsatile ACTH release about 4-fold above basal secretion with similar peak amounts of ACTH during subsequent OT pulses. Exposure of the cells to corticosterone (100 nM) or to a selective glucocorticoid receptor agonist RU 28362 (100 nM) for 30 min suppressed OT-stimulated but not basal ACTH release by approximately 60%. Inhibition gradually disappeared during subsequent pulses of OT in the absence of corticosterone. Pretreatment with the selective antagonist RU 38486 (1 microM) completely blocked the inhibitory effect of corticosterone on OT-induced ACTH secretion. Changes in free cytosolic calcium levels in single cultured pituitary cells were measured using the calcium indicator Fura-2. OT caused calcium transients in corticotropes, which were identified by immunocytochemistry. They responded in a similar manner to a second OT stimulus when preincubated for 30 min with corticosterone (1 microM) or with RU 28362 (1 microM). Our data indicate that glucocorticoids, via glucocorticoid receptors, rapidly inhibit OT-stimulated ACTH secretion by corticotropes without affecting intracellular calcium transients due to OT. Therefore, we conclude that rapid inhibition of ACTH release by glucocorticoids interferes with cellular signal transduction beyond the step of calcium mobilization

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned

Longitudinal changes in gambling, buying and materialism in adolescents: A population-based study

Gambling disorder, gambling-related cognitive biases, compulsive buying, and materialistic values lead to impaired functioning in important areas of life. The aims of the present longitudinal study are (1) to evaluate the change produced after one year in those mentioned variables and (2) to examine the gender role in these changes and to analyze the mediational mechanisms among the variables of the study. The sample was composed of 182 adolescents (103 females and 79 males) from secondary education Spanish institutions who completed self-administered questionnaires. Structural equation modeling has been used to explore associations between the different variables. Our results show significant decreases in compulsive buying, materialism, and cognitive biases related to gambling after one year. Gambling disorder severity was directly related to cognitive distortions of gambling and being a man. Compulsive buying was associated with older age and the female gender. Materialism was associated with compulsive buying and the male gender. In conclusion, gambling disorder, gambling-related cognitive biases, compulsive buying, and materialistic values change over time in different ways, according to gender. The understanding of gambling disorder and compulsive buying in adolescents could potentially lead to early prevention and treatment programs for the specific needs of gender and age

A selected annotated bibliography for spaceborne multiprocessing study

Bibliography on application of multiprocessor systems to space mission

On time preference, rational addiction and utility satiation

A basic consumer problem with a unique good is considered, current consumption of this good influencing in a positive manner consumer intertemporal utility, while past consumption exerts a negative influence. Moreover, in the line of Fisher, a specification of preferences is retained so that the rate of time preference, assumes a long-run value – this means for a stationary consumption-path – that is non-monotonic as a function of consumption: impatience increases for low level of consumptions but decreases for higher ones. Such a framework allows for an integrated appraisal of addiction, satiation and the rate of time preference. It is shown that the emergence of an addiction phenomenon in the neighbourhood of an unsatiated long-run position exactly corresponds to letting the rate of time preference be an increasing function of past consumption habits. When addiction becomes sufficiently strong, the unsatiated stationary state becomes unstable and the satiated steady state becomes the only admissible stationary position.Impatience; Consumption habits; Rational addiction; Satiation

“Texting & Driving” Detection Using Deep Convolutional Neural Networks

The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate