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Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

The papers in this special section focus on green communications, computing, and systems engineering. Computing technologies are used extensively in network systems, such as cloud computing and grid computing, and the Internet is the most well-known and widely used network infrastructure for computing. Networked software and hardware applications, especially wireless and mobile ones, have been made remarkable and fast increasing impacts on society development and human lives. They touch a great number of the human population in the world through ubiquitous mobile phones and devices. Computing and communications are indispensable components in many diverse systems. All these systems have energy issues. Their increasing spread, particularly with the emergence of the Internet of Things, is a great challenge to a world seeking to reduce its reliance on fossil fuels and address global climate changes. At the same time, although energy concerns are one of the dominant “green” topics, the green issues could be more generally defined as those making the world and the components of man-made systems both sustainable and friendly in an environmental, economic, social, and/or technical sense. In this sense, the word “green,” includes not only the sustainability objectives but also the “most positive and friendly” characteristics concerning human environments and societies.

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics. Economics

Abstract This paper presents a sparse quaternion‐valued minimization (SQVM) based control technique of a two‐stage grid supportive photovoltaic (PV) power system with power conditioning capabilities. The grid side converter (GSC) is controlled by utilizing an SQVM control technique. The proposed algorithm enables the GSC to mitigate the load harmonics current and provides reactive power compensation at the point of common coupling (PCC). The proposed control is tuned to mitigate the DC offset error, harmonics current and to improve the frequency response of the proposed system. A DC link pre‐predictive is incorporated to improve the dynamic response by reducing the burden on the outer Proportional‐Integral (PI) voltage controller loop. Further, it is used to extract the fundamental component of load currents for generating the reference currents. An adjustable DC link voltage loop is incorporated in the proposed control technique to adapt the PCC voltage variation, which helps in minimizing the losses of the PV system. For real‐time execution of the adjustable DC link voltage concept, the DC link voltage is adapted with a variation in the PCC voltage. The operation and control of the system topology are validated experimentally under various scenarios.

We propose a new scaling ansatz in the neutrino Dirac mass matrix to explain the low energy neutrino oscillations data, baryon number asymmetry and neutrinoless double beta decay. In this work, a full reconstruction of the neutrino Dirac mass matrix has been realized from the low energy neutrino oscillations data based on type-I seesaw mechanism. A concrete model based on $A_4$ flavor symmetry has been considered to generate such a neutrino Dirac mass matrix and imposes a relation between the two scaling factors. In this model, the right-handed Heavy Majorana neutrino masses are quasi-degenerate at TeV mass scales. Extensive numerical analysis studies have been carried out to constrain the parameter space of the model from the low energy neutrino oscillations data. It has been found that the parameter space of the Dirac mass matrix elements lies near or below the MeV region and the scaling factor $|\kappa_1|$ has to be less than 10. Furthermore, we have examined the possibility for simultaneous explanation of both neutrino oscillations data and the observed baryon number asymmetry in the Universe. Such an analysis gives further restrictions on the parameter space of the model, thereby explaining the correct neutrino data as well as the baryon number asymmetry via a resonant leptogenesis scenario. Finally, we show that the allowed space for the effective Majorana neutrino mass $m_{ee}$ is also constrained in order to account for the observed baryon asymmetry. Comment: 25 pages, 10 figues, revised version

Due to the diversification efforts of energy sources, use of natural gas that was newly introduced into Turkish economy, has been growing rapidly. Turkey has large reserves of coal, particularly of lignite. The proven lignite reserves are 8.0 billion tons. The estimated total possible reserves are 30 billion tons. Turkey, with its young population and growing energy demand per person, its fast growing urbanization, and its economic development, has been one of the fast growing power markets of the world for the last two decades. It is expected that the demand for electric energy in Turkey will be 580 billion kWh by the year 2020. Turkey’s electric energy demand is growing about 6–8% yearly due to fast economic growing. This paper deals with energy demand and consumption for environmental issues in Turkey.

Purpose: To investigate the perceived clinical outcome and parents' satisfaction after dental rehabilitation under general anesthesia over a follow-up period of 2 years. Materials and Methods: A prospective study of questionnaire data obtained from 352 pediatric patients before and after treatment of early childhood caries with full dental rehabilitation under general anesthesia. Questionnaires focused on oral symptoms, functional limitations, and emotional and social well-being before and after dental treatment. Cases were followed up for 2 years postoperatively. Results: A dramatic disappearance of symptoms was reported from parents' perspective. There was a high satisfaction rate (99.14%) also among parents of the children included in the study. Conclusion: Children with early childhood caries do not necessarily express it verbally as pain. The disease has a lot of other expressions affecting children's behavior and habits, including the ability to sleep, thrive, and socialize. This study contributes to the existing literature that full dental rehabilitation under general anesthesia [dental general anesthesia (DGA)] has an immediate positive impact on the physical and social quality of life of children suffering from early childhood caries as well as on their families. Postoperative preventive care, early diagnosis, and treatment of recurrent caries are key factors to maintain postoperative outcome of DGA.

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them. Accepted for publication in IEEE Access

Background: Inhaled Corticosteroid therapy is the cornerstone of asthma treatment. Yet, the reported prevalence of steroid phobia among parents of asthmatic children has been concerning. This study aimed to assess the impact of steroid phobia on ICS adherence, and asthma management. Method: A multicenter, cross-sectional study was held among 500 parents of asthmatic children over 12-months. Each participant completed a structured questionnaire that recorded patients' demographic data, and explored participants’ main concerns regarding ICS. Additionally, participants level of asthma control was assessed by the Arabic childhood asthma control test C-ACT. Result: Of 500 interviewed asthmatic children, up to 66.6% reported having ICS fears, yet only 25.8% reported discussing their concerns with their healthcare providers. In addition, over 50% of parents reported requesting ICS sparing. Regarding ICS adherence, a significant difference (<0.001) was reported as 33.3% vs 40.1% for concerned and non-concerned parents respectively. Participants with ICS fears had children with significantly (<0.001) lower mean C-ACT scores of 33.3% versus 46.7% for those with no fear, respectively. In addition, Request for ICS sparing was reported as 61.5% vs 53.9% for concerned and non-concerned parents respectively. However, asthma severity and discussing ICS concerns was not significantly affected by ICS fear. Conclusion: This study suggests that steroid phobia is a significant factor that influence ICS adherence and asthma control. Proper asthma education should be targeted to alleviate unjustifiable steroid use concerns. Future research should be more oriented to crafting proper interventional strategies to better address the ICS negative perceptual barriers