Creation of innovative technologies for transport operations in the preparation of rough feed

The article deals with the influence of technologies for carrying out transport operations in the preparation of rough feed (hay, as well as valuable varieties of straw) on their quality and efficiency indicators of the entire technological process. To improve the quality of these operations, a combined transport technology has been proposed. During its development and research, the methods of simulation modeling, experiment planning and correlation-regression analysis were used. Regression equations were obtained in the form of the Cobb-Douglas function. The proposed innovative technology for combined transport of forage has proven its effectiveness during production tests in private conditions of agricultural enterprises. Evaluation of economic efficiency through the cost of transporting one ton showed an effect of 7-15%, depending on the quantitative and brand composition of the complex. This technology makes it possible to increase the efficiency of forage procurement, which affects the efficiency of animal husbandry and food security of the country.

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.

VR Technology to the Adjustment of Piano Playing Mentality

Piano performance requires not only skillful and excellent playing skills, but also good psychological quality and improvisation. The perfect performance is the natural language of a pianist's usual skill and mood. The various emotional activities and expressions of the overall situation in relation to the player's performance, mental issues such as the intensity and horror of the scene, seriously affect the pianist’s actual performance while playing. The piano is therefore very important for mind control. Based on this, this paper attempts to apply the current hot VR technology to piano performance and simulate the virtual piano performance scene through VR technology, so as to enhance the on-the-spot experience for piano players and continuously improve their psychological quality, so that they can better adjust their nervous bad mentality in actual combat. This article first has carried on the 3D virtual reality piano stage modeling experiment, and then selected the 20 young pianist piano training involved in day-to-day contrast test, the test group of 10 people for daily training, virtual piano stage under the environment of normal control group of 10 people ordinary daily training, training time four hours a day, for three months, all participants will take part in The Provincial Youth Piano Competition since the end of the training. The final test results showed that in the competition site, the performance level of 10 players in the test group was generally better than that of the control group. Among them, there were 2 subjects in the test group who played for a long time, while one of the control groups gave up playing in the middle. In addition, in the horizontal comparison, it is found that subjects who are older and have been learning piano for a long time have better on-the-spot mental adjustment ability and more stable performance. Boys performed better than girls, and subjects with positive, optimistic, and confident personalities performed better than those with introverted and low self-confidence.

Application and Potential Use of Advanced Bioinformatics Techniques in Agriculture and Animal Sciences

The amount of biological information generated in the last two decades is enormous because of Next generation sequencing (NGS) discovery that has enabled researches to sequence and model almost every organism and also due to rapid advancements in techniques and tools in experimental research. The research which was first carried out at fields, labs and clinics is now started with computational analysis (in-silico) of information, modeling, experiment planning and hypothesis development. Various applications of bioinformatics are algorithms, databases, and other data analysis tools and softwares that enable storage, analysis, retrieval, annotation and visual interpretation of biological information which in turn increases the knowledge of various biological systems that help in making new discoveries regarding production, human health, animal health and plant health keeping in mind the challenges of climate change, water and area shortage. This will help not only in increased plant and animal production but also in management and treatment of various human, animal and plant diseases in addition to the underlying mechanisms and strategies of the rapidly evolving pathogenic microorganism and antibiotic resistance.

New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Social Modeling and Eating Behavior—A Narrative Review

Social modeling of eating is the adjustment of the amount of food eaten to the intake of the accompanying person. In this paper we provide a narrative review of literature on social modeling of eating with a particular focus on recent studies. Firstly, we describe the structure of a typical modeling experiment. Secondly, we present a variety of research in this field: experiments with various types of confederates, experiments aimed at the evaluation of the influence of gender, partner’s body weight, type of food, hunger, personal characteristics, etc. Thirdly, we present practical implications of this knowledge. The common conclusion is that social modeling of eating occurs in different situations and consumption is adapted to the standards established by the eating partner, but is not their direct reflection. Social influence of eating is not restricted to "artificial" laboratory situations; social modeling and social norms manipulations may be used to change people’s dietary practices, especially in children and young adults. Within the home environment parental modeling has been shown to promote children’s snacking and fruit and vegetable consumption. Social modeling may be used in nutrition interventions aimed at the improvement of children’s diet and in obesity prevention programs.

Evaluation and interpretation of modeling bias for biomass burning aerosols in AeroCom models

<p>Biomass burning (BB) injects aerosols into the atmosphere and can thereby affect the earth climate and human health. Yet the modeling of BB aerosols exhibits significant bias. Here we present a comprehensive evaluation of AeroCom model simulations with satellite observations to understand such uncertainties. A total of 59 model runs using 17 models from three AeroCcom Phase III experiments (i.e., Biomass Burning emissions, CTRL2016, and CTRL2019 experiment) and 14 satellite products are involved. AOD (aerosol optical depth) at 550 nm wavelength during the fire season over three typical fire regions (Amazon, South Hemisphere Africa, and Boreal North America, or AMAZ, SHAF, and BONA) is the focus of our study, although we also consider AE and SSA from POLDER.</p><p>The 14 satellite products are shown to have quite substantial differences from AERONET observation. But we show that such differences have little impact on the model evaluation which is mainly affected by modeling bias. Through the comparison with POLDER observation, we found the modeled AOD are biased by -93% ~ 174% with most models showing significant underestimations even for the most recent modeling experiment (i.e., CTRL19). SHAF is among the three regions with the strongest underestimation in general. By scaling up the input emissions, such negative bias would be significantly reduced, which, however, has little impact on the day-to-day correlation between models and observations.</p><p>On top of the satellite-based model evaluation, we interpret the model diversity from the aspect of aerosol emissions, lifetime, and MEC (mass extinction coefficient), which are further linked with specific parameters in models. These three parameters cause similar levels of AOD diversity, which is quite different from the modeled aerosols during non-fire season when the contribution of lifetime is predominant. During the fire season, diversity caused by lifetime is strongly affected by local deposition; as a matter of fact, models tend to do quite poorly in simulating precipitation strength. Modeled MECs show significant correlations with aerosol wet-growth (which is known to be challenging to models) and AE (Angstrom Exponent) for some involved models. Comparisons with POLDER observed AE suggests some models tend to underestimate AE and thus MEC, which might be responsible for the overall AOD underestimation in certain models. Additionally, we show that model AOD biases correlate with satellite observed formaldehyde columns, suggesting SOA formation may be insufficiently captured by models.</p>

Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on AdaBoost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the AdaBoost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.