The goal of the present study was to assess the family quality of life (FQOL) in families with children with developmental disabilities in Bosnia and Herzegovina. We examined the FQOL in relation to parents’ demographic variables (gender, age, employment status), children’s gender and age, type of children’s disability (mild and moderate intellectual disability, autism spectrum disorder) and parent’s depression, anxiety, and stress levels. The sample consisted of 270 parents of children with disabilities; 90 parents of children with autism spectrum disorder, 90 parents of children with mild intellectual disability, and 90 parents of children with moderate intellectual disability. We also included a control group of 90 parents of typically developing children. The results of this study showed that parents of children with autism spectrum disorder and parents of children with moderate intellectual disability had statistically significantly lower FQOL than parents of children with mild intellectual disability and parents of typically developing children. Child’s gender had a significant effect on FQOL. Also, depression, anxiety and stress levels significantly affected FQOL, while the parents’ gender, age, and employment status did not have a significant effect. Implications of these findings are discussed and some measures on supporting parents are proposed.

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.

Low temperature deposition of high-quality epitaxial GaN is crucial for its integration inelectronic applications. Chemical vapor deposition at approximately 800 °C using SiC with anAlN buffer layer or nitridized sapphire as substrates is used to facilitate the GaN growth. Here,we present a low temperature atomic layer deposition (ALD) process usingtris(dimethylamido)gallium(III) with NH3 plasma. The ALD process shows self-limitingbehaviour between 130-250 °C with a growth rate of 1.4 Å/cycle. The GaN films produced werecrystalline on Si(100) at all deposition temperatures with a near stochiometric Ga/N ratio withlow carbon and oxygen impurities. When GaN was deposited on 4H-SiC, the films grewepitaxially without the need for an AlN buffer layer, which has never been reported before. The bandgap of the GaN films was measured to be ~3.42 eV and the fermi level showed that the GaN was unintentionally n-type doped. This study shows the potential of ALD for GaN-basedelectronic devices.

This study was carried out in the Onigambari area of Oyo state to determine the choice of farmers to practice Ago-forestry in the area. The χ2 test result revealed that the educational level of the respondents correlated highly with the respondents major occupation (p<0.01). χ2 test shows that there was significant relationship (p<0.01) between the respondents’ level of education and awareness of agroforestry systems. This shows that farmers with a better educational background can accept agro forestry easily than those will lower education. Farmers’ awareness of Agroforestry systems, willingness to plant trees, purpose of planting trees and desired tree species were evaluated. It was observed that 74% of the respondents are not willing to practice agroforestry while 24% shows interest. 64% confirmed they are aware of the practice while 32% had never heard of such practice. The t-test result shows a significant difference with respect to the willingness of the respondents to practice agroforestry. Of those that areaware, 38% practice agroforestry for the purpose of fuel wood, 36% as source of income while only 2% practice for the purpose of wind breaking. Eucalyptus calmuldulensis, Magnifera indica, Gmelina aboreal are some of the tress used for this practice in the study area. Lack of seed and scarcity of land were identified as limiting factors that prevent farmers from practicing agroforestry. In summary it was observed that the respondent’s level of education correlated significantly to the level of education, also the level of education determines respondent’s willingness to practice  agroforestry. It is recommended that Seeds, Seedlings and land be made available to encourage the practice among farmers. Extension workers are encouraged to engage farmers more on the benefits of agroforestry. Keyword: Farmers, Agroforestry, Climate change, Land degradation, Natural Fallow

In offshore engineering design, nonlinear wave models are often used to propagate stochastic waves from an input boundary to the location of an offshore structure. Each wave realization is typically characterized by a high-dimensional input time-series, and a reliable determination of the extreme events is associated with substantial computational effort. As the sea depth decreases, extreme events become more difficult to evaluate. We here construct a low-dimensional characterization of the candidate input time series to circumvent the search for extreme wave events in a high-dimensional input probability space. Each wave input is represented by a unique low-dimensional set of parameters for which standard surrogate approximations, such as Gaussian processes, can estimate the short-term exceedance probability efficiently and accurately. We demonstrate the advantages of the new approach with a simple shallow-water wave model based on the Korteweg–de Vries equation for which we can provide an accurate reference solution based on the simple Monte Carlo method. We furthermore apply the method to a fully nonlinear wave model for wave propagation over a sloping seabed. The results demonstrate that the Gaussian process can learn accurately the tail of the heavy-tailed distribution of the maximum wave crest elevation based on only $$1.7\%$$ 1.7 % of the required Monte Carlo evaluations.

Significance Dysregulation of the alternative complement pathway due to impaired binding of complement factor H (CFH) to self-ligands or altered self-ligands (e.g. malondialdehyde [MDA]-modified molecules) involved in homeostasis can promote the development of complement-related diseases, such as age-related macular degeneration (AMD). We identified, in an unbiased GWAS approach, that common genetic variants within the CFH gene family (rs1061170 and the deletion of the complement factor H-related protein 1 and 3 genes [CFHR3 and CFHR1]) act as major modulators of CFH recruitment and its ability to regulate complement on MDA-epitopes. These findings demonstrate the importance of the genetic status within the CFH/CFHR3/CFHR1 locus in tissue homeostasis and provide a mechanistic explanation as to why deletion of CFHR3/CFHR1 is protective in AMD development. Genetic variants within complement factor H (CFH), a major alternative complement pathway regulator, are associated with the development of age-related macular degeneration (AMD) and other complementopathies. This is explained with the reduced binding of CFH or its splice variant factor H-like protein 1 (FHL-1) to self-ligands or altered self-ligands (e.g., malondialdehyde [MDA]-modified molecules) involved in homeostasis, thereby causing impaired complement regulation. Considering the critical role of CFH in inhibiting alternative pathway activation on MDA-modified surfaces, we performed an unbiased genome-wide search for genetic variants that modify the ability of plasma CFH to bind MDA in 1,830 individuals and characterized the mechanistic basis and the functional consequences of this. In a cohort of healthy individuals, we identified rs1061170 in CFH and the deletion of CFHR3 and CFHR1 as dominant genetic variants that modify CFH/FHL-1 binding to MDA. We further demonstrated that FHR1 and FHR3 compete with CFH for binding to MDA-epitopes and that FHR1 displays the highest affinity toward MDA-epitopes compared to CFH and FHR3. Moreover, FHR1 bound to MDA-rich areas on necrotic cells and prevented CFH from mediating its cofactor activity on MDA-modified surfaces, resulting in enhanced complement activation. These findings provide a mechanistic explanation as to why the deletion of CFHR3 and CFHR1 is protective in AMD and highlight the importance of genetic variants within the CFH/CFHR3/CFHR1 locus in the recognition of altered-self in tissue homeostasis.

Concentrations of dangerous and harmful substances (PCB, TPH and heavy metals) were determined in soils in an industrial zone near the center of Banja Luka and the Vrbas River. PCB, TPH and heavy metals were found in the analysis location as a result of general anthropogenic factors. Contaminated soils have a negative impact on human health and the environment. The mean concentrations of Pb, TPH, Cu, PCB, Ni, Cd and Hg were 4874, 4105, 545.7, 282.1, 225.7, 12.15 and 5.896 mg/kg, respectively. Results show that concentrations were very high for all analyzed parameters, and these values indicated that the soil was highly polluted. Principal component analysis has shown that industrial factors and human activities are the cause of pollution. At the location it is necessary to determine the origin of pollution and recultivation and remediation activity of planned activity.

In this paper, we report user preferences regarding color and animation patterns to support the interaction between Automated Vehicles (AVs) and pedestrians through an external Human-Machine-Interface (eHMI). Existing concepts of eHMI differ -- among other things -- in their use of colors or animations to express an AV's yielding intention. In the absence of empirical research, there is a knowledge gap regarding which color and animation leads to highest usability and preferences in traffic negotiation situations. We conducted an online survey (N=400) to investigate the comprehensibility of a light band eHMI with a combination of 5 color and 3 animation patterns for a yielding AV. Results show that cyan is considered a neutral color for communicating a yielding intention. Additionally, a uniformly flashing or pulsing animation is preferred compared to any pattern that animates sideways. These insights can contribute in the future design and standardization of eHMIs.

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

The determination of the effects of cadmium and mercury on the growth, biomass productivity and phytoremediation potential of Miscanthus × giganteus (MxG) grown on contaminated soil was the main aim of this paper. The use of bioenergy plants as an innovative strategy in phytotechnology gives additional benefits, including mitigation and adaptation to climate change, and soil remediation without affecting soil fertility. An experiment was set up as a randomized complete block design with the treatments varied in concentrations of Cd (0, 10 and 100 mg kg−1 soil) and Hg (0, 2 and 20 mg kg−1 soil) added to the soil. Three vegetative years were studied. Yield values ranged from 6.3–15.5 tDM ha−1, cadmium concentration in plants varied from 45–6758 µg kg−1 and Hg varied from 8.7–108.9 µg kg−1. Values between treatments and years were significantly different. MxG can accumulate and remove very modest amount (up to 293.8 µg Cd and 4.7 µg Hg) per pot per year in aboveground biomass. Based on this data it can be concluded that MxG, as a valuable energy crop, is a potential candidate for the phytostabilization and biomass production on soils contaminated with Cd and Hg moderately.