Facial-sketch Synthesis: A New Challenge

Machine Intelligence Research - This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a...     

The effectiveness of adaptive versus non-adaptive learning with digital educational games

For the training of academic skills, digital educational games with integrated adaptivity are promising. Adaptive games are considered superior to non-adaptive games, because they constantly assess children's performance, and accordingly adapt the difficulty of the tasks corresponding to the children's individual level. However, empirical evidence with regard to the effectivity of adaptive compared to non-adaptive games is limited. A study was conducted with 191 children from the third year of Kinder garten who were enrolled in one of three conditions, that is, playing an adaptive version of the reading game (RG), a non-adaptive version of the RG or training with pen-and-paper exercises. In all three conditions, children trained emergent reading (phonological awareness and letter knowledge) once a week for 30 min over a period of 5 weeks. Children's performance on cognitive (phonological awareness, letter knowledge, reading fluency) and non-cognitive (motivation, self-concept) factors was assessed. Results revealed a significant improvement in phonological awareness and letter knowledge in all conditions. However, no differences between the conditions were observed with respect to children's improvement on phonological awareness and letter knowledge or on their post-test scores for reading fluency. With regard to motivation and self-concept, again, no differences in these non-cognitive factors were observed across conditions.     

Regulatory RNAs: A Universal Language for Inter-Domain Communication

In eukaryotes, microRNAs (miRNAs) have roles in development, homeostasis, disease and the immune response. Recent work has shown that plant and mammalian miRNAs also mediate cross-kingdom and cross-domain communications. However, these studies remain controversial and are lacking critical mechanistic explanations. Bacteria do not produce miRNAs themselves, and therefore it is unclear how these eukaryotic RNA molecules could function in the bacterial recipient. In this review, we compare and contrast the biogenesis and functions of regulatory RNAs in eukaryotes and bacteria. As a result, we discovered several conserved features and homologous components in these distinct pathways. These findings enabled us to propose novel mechanisms to explain how eukaryotic miRNAs could function in bacteria. Further understanding in this area is necessary to validate the findings of existing studies and could facilitate the use of miRNAs as novel tools for the directed remodelling of the human microbiota.     

Controlling thickness to tune performance of high-mobility transparent conducting films deposited from Ga-doped ZnO ceramic target

Structure modification has been found to tune significantly the transparent-conducting performance, especially mobility and conductivity of hydrogenated Ga-doped ZnO (HGZO) films. The strong correlation between film thickness and mobility of the films is revealed. The mobility increases quickly with increasing the thickness from 350 to 900 nm, and then tends to be saturated at further thicknesses. A higher mobility than 50 cm²/Vs can be achieved, which is an extra-high value for polycrystalline ZnO films deposited by using the sputtering technique. The thickness-dependent mobility originates from scatterings on grain boundaries and dislocation-induced defects controlled by thin-film growth. Based on the Volmer-Weber model, an expansion model is built up to describe the thickness-dependent crystal growth of the HGZO films, especially at the thick films. As a result, the 800 nm-thick HGZO film obtains the highest performance with high mobility of 51.5 cm²/Vs, low resistivity of 5.3 × 10^?4 Ωcm, and good transmittance of 83.3 %.     

Advanced Surface of Fibrous Activated Carbon Immobilized with FeO/TiO2 for Photocatalytic Evolution of Hydrogen under Visible Light

FeO-doped TiO₂ nanoparticle photocatalysts were immobilized onto the surface of fibrous activated carbon (ACF) via a sol-gel process. As an adsorbent and photocatalyst, FeO-TiO₂ on immobilized ACFs (FeO-TiO₂/ACF) greatly improved the photocatalysis rate of hydrogen production as compared with pure TiO₂ and ACF-TiO₂ under UV irradiation and visible light. The addition of ACFs surface significantly reduced the photogenerated pairs of electrons-hole recombination, thereby promoting the photocatalysis action of doped photo-metal oxides of FeO-TiO₂. Co-doping of FeO onto the lattice of the TiO₂ approach can improve the absorption activity of visible light through photo-metal oxide of TiO₂ and further enhance hydrogen production under visible light. The photocatalytic fabrics (FeO-TiO₂/ACF) were effortlessly split out from the experimental solution for re-utilization and exhibited high stability even after five complete regeneration cycles.     

COVID-19 hospitalization rates rise exponentially with age,inversely proportional to thymic T-cell production

Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R² = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus, MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49–75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.