Biogas residue parameterization for soil organic matter modeling

A variety of biogas residues (BGRs) have been used as organic fertilizer in agriculture. The use of these residues affects the storage of soil organic matter (SOM). In most cases, SOM changes can only be determined in long-term observations. Therefore, predictive modeling can be an efficient alternative, provided that the parameters required by the model are known for the considered BGRs. This study was conducted as a first approach to estimating the organic matter (OM) turnover parameters of BGRs for process modeling. We used carbon mineralization data from six BGRs from an incubation experiment, representing a range of substrate inputs, to calculate a turnover coefficient k controlling the velocity of fresh organic matter (FOM) decay and a synthesis coefficient describing the SOM creation from FOM. An SOM turnover model was applied in inverse mode to identify both parameters. In a second step, we related the parameters k and to chemical properties of the corresponding BGRs using a linear regression model and applied them to a long-term scenario simulation. According to the results of the incubation experiment, the k values ranged between 0.28 and 0.58 d-1 depending on the chemical composition of the FOM. The estimated values ranged between 0.8 and 0.89. The best linear relationship of k was found to occur with pH (R2 = 0.863). Parameter is related to the Ct/Norg ratio (R2 = 0.696). Long-term scenario simulations emphasized the necessity of specific k and values related to the chemical properties for each BGR. However, further research is needed to validate and improve these preliminary results. © 2018 Prays et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Non-linear regression models for time to flowering in wild chickpea combine genetic and climatic factors

Background: Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. Results: We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R 2=0.97. Conclusions: We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

To break down the frequency and related risk elements of sorrow in patients with type 2 diabetes mellitus in the nearby local area and give logical references to clinical anticipation and treatment of diabetes mellitus with wretchedness. Proposed strategies use AI more than 58 patients with type 2 diabetes mellitus were chosen by efficient inspecting, and pertinent surveys examined segment factors and related clinical variables and misery sub-scale (PHQ) was utilized to assess the level of sadness. Social help scale was utilized to survey the patients. SSRS are utilized to assess individual social help levels and lead measurable investigation. After assessment it's seen that among the 58 patients with type 2 diabetes, the rate of consolidated melancholy was 58%; the age, conjugal status, training level, occupation, family ancestry, term of diabetes, entanglements, work out. The distinction in friendly help was genuinely critical. The impacting elements of type 2 diabetes confounded with gloom incorporate age, conjugal status, training level, occupation, and family ancestry, span of diabetes, presence or nonappearance of confusions, exercise and social help. They have a high gamble of muddled sorrow and influence the improvement of diabetes

Genome-wide association study and scan for signatures of selection point to candidate genes for body temperature maintenance under the cold stress in Siberian cattle populations

Design of new highly productive livestock breeds, well-adapted to local climatic conditions is one of the aims of modern agriculture and breeding. The genetics underlying economically important traits in cattle are widely studied, whereas our knowledge of the genetic mechanisms of adaptation to local environments is still scarce. To address this issue for cold climates we used an integrated approach for detecting genomic intervals related to body temperature maintenance under acute cold stress. Our approach combined genome-wide association studies (GWAS) and scans for signatures of selection applied to a cattle population (Hereford and Kazakh Whiteheaded beef breeds) bred in Siberia. We utilized the GGP HD150K DNA chip containing 139,376 single nucleotide polymorphism markers

Managing Expatriates: Success Factors in Private and Public Domains

This volume provides in-depth examinations of a variety of individual, social, and environmental factors that contribute to the success of expatriate employees. Using data from numerous large-scale studies from both the public and private sectors, this volume provides valuable insights into expatriate success with implications for both theoretical understanding and practical management. The authors explore factors that influence employees to pursue expatriation, contribute to expatriate adjustment and satisfaction, and ultimately drive expatriate performance, well-being, and success. The chapters in this book consider the role of social demographic characteristics, personality and individual differences, training and preparation, and social and organizational support in contributing to each of these outcomes. Using findings from diverse countries and sectors and data-focused analytic techniques, this volume provides novel insights into factors promoting expatriate success

miRNAs and COVID-19 Therapy Review

These days, the extreme intense respiratory condition Coronavirus 2 (SARS-CoV-2) disease is recognised on the grounds that the primary cause behind mortality in people. SARS-CoV-2 is transmitted through human-to-human contact and is a symptomless in many patients. furthermore, to approved vaccines against SARS-CoV-2 infection, miRNAs may additionally be promising decisions against the current new virus. miRNAs are small and noncoding RNAs 18–25 nucleotides in length that focus on the mRNAs to degrade them or block their interpretation miRNAs go about as an observer in cells.This review in regards to evaluated the writing on the potential role of cellular miRNAs inside the SARS-CoV-2-have collaboration as a therapeutic option in COVID-19 patients