Emissions control policies under uncertainty and rational learning in a linear-state dynamic model

We compare the use of price-based policies or taxes, and quantity-based policies or quotas, for controlling emissions in a dynamic setup when the regulator faces two sources of uncertainty: (i) market-related uncertainty; and (ii) ecological uncertainty. We assume that the regulator is a rational Bayesian learner and the regulator and firms have asymmetric information. In our model the structure of Bayesian learning is general. Our results suggest that the expected level of emissions is the same under taxes and quotas. However, the comparison of the total benefits related to these policies suggests that taxes dominate quotas, that is, they provide a higher social welfare. Even though taxes have some benefits over quotas, neither learning nor ecological uncertainty affect the choice of policy, i.e., the only factor having such an impact is uncertainty in the instantaneous net emissions benefits (market-related uncertainty). Besides, the more volatile is this uncertainty, the more benefits of taxes over quotas. Ecological uncertainty leads to a difference between the emissions rule under the informed and the rational learning assumptions. However, the direction of this difference depends on the beliefs bias with regard to ecological uncertainty. We also find that a change in the regulator’s beliefs toward more optimistic views will increase the emissions.     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

A Model for Identifying the Behavior of Alzheimer’s Disease Patients in Smart Homes

Wireless Personal Communications - In recent years, Smart Cities and Smart Homes have been studied as an important field of research. The design and construction of smart homes have flourished so...     

Betula alba Bark Extract and Empetrum nigrum Fruit Juice,a Natural Alternative to Niacinamide for Skin Barrier Benefits

The Scandinavian region is home to a unique biome with endemic plant species. The aim of this study was to explore this natural diversity and identify plant extracts providing positive skin barrier effects. Six plant extracts were identified as starting material. Following biochemical screening, two candidates outperformed the rest: Betula alba (BA) and Empetrum nigrum (EN). Quantitative PCR analysis showed that BA and EN upregulated barrier genes, when used individually and in combination. Betula alba increased AQP3 and OCLN protein expression, something niacinamide was incapable of. Additionally, the skin barrier was strengthened, evidenced by inhibition of KLK5 and hyaluronidase and showed strong antioxidant and anti-inflammatory activity through DPPH and COX2 inhibition, respectively. A first split-face clinical study was conducted using the combination of extracts versus placebo. There was a significantly better skin restructuring effect and corneocyte cohesion on the side treated with combined extracts. A second split-face clinical study assessed the combined extracts versus 3% niacinamide. Significant variations in skin hydration and TEWL were observed in favor of the extract treated side. In conclusion, we identified a natural alternative to niacinamide for improving skin barrier health, in Scandinavian plant extracts, which yield strong performance, but at a lower concentration.     

How Can We Improve Vaccination Response in Old People? Part I: Targeting Immunosenescence of Innate Immunity Cells

Vaccination, being able to prevent millions of cases of infectious diseases around the world every year, is the most effective medical intervention ever introduced. However, immunosenescence makes vaccines less effective in providing protection to older people. Although most studies explain that this is mainly due to the immunosenescence of T and B cells, the immunosenescence of innate immunity can also be a significant contributing factor. Alterations in function, number, subset, and distribution of blood neutrophils, monocytes, and natural killer and dendritic cells are detected in aging, thus potentially reducing the efficacy of vaccines in older individuals. In this paper, we focus on the immunosenescence of the innate blood immune cells. We discuss possible strategies to counteract the immunosenescence of innate immunity in order to improve the response to vaccination. In particular, we focus on advances in understanding the role and the development of new adjuvants, such as TLR agonists, considered a promising strategy to increase vaccination efficiency in older individuals.     

Quantifying the Differences in Geometry and Size Distributions of Buildings Within Cities

There have been many studies on the spatial configuration of cities, but few attempts to quantify the difference in building patterns between the old and new parts of cities. This may be partly attributable to lack of suitable study methods. This paper presents a new application of statistical methods for quantifying the geometric difference between different parts of a city using, as a case study, the old (historical) and new parts of the city of Yazd in Iran. We measured 341 edge lengths of 4 bazaars, 302 edge lengths of 5 mosques and tombs, and 239 edge lengths of 3 schools. We also measured 6,804 edge lengths and the areas of 1,243 well-preserved courtyard houses in the old part and 4,948 edge lengths and the areas of 1,237 houses in the new part of the city. In the old part, all edge-length and house-area frequency distributions, to a first approximation, follow power laws, indicating that there are many small and very few large buildings. By contrast, in the new part the edge-length and house-area frequency distributions follow bimodal (two-peak) distributions. The calculated entropies (measures of dispersion) of the house edge lengths and areas in the old part are much higher than of those in the new part and provide a clear, quantitative measure of the geometric differences between the built-up structures of old and the new parts of the cities. The power-law distributions in the old part indicate a gradual and continuous variation in sizes of edge lengths and house areas, whereas the bimodal distributions in the new part indicate abrupt (discontinuous) changes in the edge lengths and house areas. The entropy results imply that the size distributions of houses in the old part are more dispersed than those in the new part, indicating more interconnected structures in the old part of the city. The results also demonstrate quantitatively that there is a lack of coherence between the structures of old and new parts of city.     

Synthesis of tetrahydrofuran from maleic anhydride on Cu–ZnO–ZrO2/H-Y bifunctional catalysts

A series of bifunctional Cu–ZnO–ZrO₂/H-Y catalysts of different compositions were prepared by coprecipitating sedimentation method and were characterized by surface area and XRD analyses. The catalytic performance in synthesis of tetrahydrofuran was evaluated and optimized in a three-phase slurry batch reactor. The experimental results showed that the appropriate ratio of Cu/ZnO in the hydrogenation catalyst was 50/45, for which the conversion of maleic anhydride (MA) and selectivity of tetrahydrofuran (THF) reached 100% and 46%, respectively, at 50 bar and 493 K after 6 h of operation. Also, according to these results, it was demonstrated that the incorporation of zirconium oxide in the catalyst formulation enhanced the catalytic activity, and tetrahydrofuran selectivity was increased to 55%. Ultimately, it was concluded that the bifunctional catalyst of Cu–ZnO–ZrO₂/H-Y was an appropriate catalyst to produce THF from MA with high activity, selectivity and stability.     

CuI Immobilized on Tricationic Ionic Liquid Anchored on Functionalized Magnetic Hydrotalcite (Fe3O4/HT-TIL-CuI) as a Novel,Magnetic and Efficient Nanocatalyst for Ullmann-Type C–N Coupling Reaction

One of the most important reactions in organic synthesis is Ullmann-type C–N coupling reaction which has been used for preparation of numerous biologically active compounds. In this work, CuI immobilized on tricationic ionic liquid anchored on functionalized magnetic hydrotalcite (Fe₃O₄/HT-TIL-CuI) has been successfully prepared and fully characterized by different techniques, including fourier-transform infrared spectroscopy, vibrating sample magnetometer, thermo gravimetric analysis, transmission electron microscopy, field-emission scanning electron microscopy, energy dispersive X-ray spectroscopy, elemental mapping, zeta potential, X-ray diffraction, temperature programmed desorption of ammonia (NH₃-TPD), temperature-programmed reduction and inductively coupled plasma. The results showed that the as-prepared nanocatalyst possesses plate-like morphology with approximate size of 50 nm and superparamagnetic behavior. Also, total acidity and total hydrogen consumption of the nanocatalyst were measured to be 8.5 and 1.41 mmol g^?1, respectively. This nanocatalyst exhibited favorable performance for C–N coupling reaction among a variety of aryl halides and N(H)-heterocycles (benzimidazoles, pyrazoles and triazoles) in the presence of 2.5 mol% of nanocatalyst without any additives under air atmosphere revealing high yields in all cases. Besides, it is noted that in the present system the desired product can be easily and quickly isolated and nanocatalyst also recovered magnetically from the reaction mixture employing a permanent magnet for at least six consecutive trials without a discernible decrease in catalytic activity which makes the proposed methodology appropriate for industrial. The findings demonstrated the advantages of the present method as no need for neutral atmosphere, appropriate times, recyclability of the catalyst, broad substrate scope, minimization of chemical waste, simple purification of products, easy workup process, and high yields.

     

From Feather Waste to Valuable Nanoparticles

Feather is a waste product generated in large quantities from industrial poultry processing. Recycling of this renewable source of biopolymers has been the objective of many researches due to its high protein content, biodegradability, and biocompatibility. This study investigates the feasibility of producing nanoparticles from feather waste by enzymatic hydrolysis, followed by ultrasonic treatment. The effects of enzyme concentration, hydrolysis time and substrate concentration on particles size were evaluated to optimize the best condition in order to attain the smallest particles by a Box-Behnken Design. The optimum hydrolysis conditions were found to be: enzyme concentration: 3.6%, substrate concentration: 5 g/l and hydrolysis time: 243 h. Scanning electron micrographs indicated fiber fibrillation and degradation as it was progressively converted into particles form. The results of particle size analysis indicated the positive effect of sonication on reducing particles size. Fourier transform infrared spectra showed no remarkable changes in the chemical composition of treated samples. Moreover, crystallinity and thermal stability of feather nanoparticles enhanced upon enzymatic hydrolysis and ultrasonic treatment.