Effects of Fertilizers on Biomass, Sugar Content and Ethanol Production of Sweet Sorghum

Sweet sorghum (Sorghum bicolor) is a promising alternative crop for bioethanol production in developing countries. However, to extend the cultivative area of this crop, it needs to develop an appropriate growing protocol for farmers. This chapter describes the examination of different doses of fertilizers combined with manure and micronutrients, in various applied times, on biomass, sugar content and ethanol production of sweet sorghum. It was observed that the application of 90 N + 90 P2O5 + 60 K2O provided maximum stem yield and optimum contents of sugar and ethanol yield, however nontreatment of any among P, P2O5 and K2O caused significant reduction of biomass and ethanol production. Higher fertilization >90 N may provide greater productivity of this crop but it may cause lodging and economic deficit for farmers in developing countries. It was also found that the applied times of fertilization should be at 3–4 to 7–8 leaf stage. In contrast, when the fertilization was as close to the flowering stage caused remarkable reduction of stem yield and ethanol production. The supplementation of (NH4)2MO7O2.4H2O at 5 kg/ha provided an increase of 10–12 tons/ha of stem yield and a remarkable enrichment of ethanol production. Findings of this study are useful for farmers and agricultural extensionists to promote biomass and ethanol productivity of this crop for bioethanol production. This research also highlights a greater possibility of exploiting sweet sorghum cultivation in infertile and hilly, abandoned areas for ethanol production

Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria

Improving milling performances is an effective solution to decrease the costs required. This paper addressed a multi-response optimization to simultaneously decrease the machining power consumed Pm, arithmetical roughness Ra, and ten-spot roughness Rz. The Grey-Response Surface Method-Multi Island Genetic Algorithm (GRMA) consisting of grey relational analysis (GRA), response surface method (RSM), and multi-island genetic algorithm (MA) was proposed to predict the optimal parameters and yield optimum milling performances. The experimental trials were conducted with the support of a CNC milling center. The influences of spindle speed (S), depth of cut (ap), feed rate (fz), and tip radius (r) were explored using GRA. The nonlinear relationship between machining parameters and grey grade (GG) model was developed using RSM. Finally, two optimization techniques, including desirability approach (DA) and MA were performed to observe the optimal values. The results indicated that the machining power was greatly affected by processing factors and the radius has a significant impact on the roughness criteria. The measured reductions using optimal parameters of Pm, Ra, and Rz are approximately 77.05%, 50.00%, and 58.02%, respectively, as compared to initial settings. The GRMA can be considered as an effective approach to generate reliable values of processing conditions and technological performances in the milling process

Experiments and optimization for the WEDM process: A trade-off analysis between surface quality and production rate

This work addressed a parameter optimization to simultaneously decrease the root mean square roughness (Rq) as well as the thickness of the white layer (TW) and improve the material removal rate (MRR) for the wire electro-discharge machining (WEDM) of a stainless steel 304 (SS304). The factors considered are the discharge current (C), the gap voltage (VO), the pulse on time (POT), and the wire drum speed (SP). The interpolative radius basic function (RBF) is applied to show the correlation between the varied factors and WEDM performances measured. The optimal selection is chosen using the multi-objective particle swarm optimization (MOPSO). Moreover, a traditional one using the response surface method (RSM) and desirability approach (DA) is adopted to compare the working efficiency of two optimization techniques. The results showed that the optimal findings of the C, POT, VO, and SP are 5.0 A, 1.0 µs, 61.0 V, and 8.0 m/min, respectively. The values of the Rq and TW are decreased by approximately 33.33% and 23.53%, respectively, while the MRR enhances 47.42% at the optimal selection, as compared to the common values used. The BRF-MOPSO can provide better performance than the RSM-DA

A Target Threat Assessment Method for Application in Air Defense Command and Control Systems

Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems.Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems

Rice farmers' perception and determinants of climate change adaptation measures: a case study in Vietnam

The study used Mann Kendall's and Sen's slope tests to elicit rice farmers' perceptions of climate change due to extreme weather occurrences and compared them to hydro-meteorological data. According to the findings, temperatures increased by 0.4 degrees during the last 35 years. While rainfall has increased, the pattern has been difficult to discern. The test results corroborated farmers' perceptions of increased heat spells, but rainfall frequency and intensity vary and are difficult to anticipate. Three adaptation strategies are frequently employed in the Nong Cong district: adjusting the seasonal calendar to alter transplanting and harvesting timing; increasing fertiliser and pesticide application; and changing variety to short-time kinds. Due to the interdependence of adaption techniques, the study used a multivariate probit model. The regression findings indicated that several relevant variables influence the decision to apply adaption methods. Numerous policy ideas for enhancing adaptation to climate change can be derived from the results of this study. District governments must improve their capacity to forecast weekly weather and train how to adapt production to climate change.Le Phuong Nam (Viet Nam National University of Agriculture (VNUA)), Nguyen Dang Que (National Academy of Public Administration (NAPA)), Nguyen Van Song (Viet Nam National University of Agriculture (VNUA)), Tran Thi Hoang Mai (Vinh University (VU)), Nguyen Thi Minh Phuong (Vinh University (VU)), Nguyen Thi Xuan Huong (Viet Nam National University of Forestry (VNUF)), Nguyen Cong Tiep (Viet Nam National University of Agriculture (VNUA)), Tran Ba Uan (Dien Bien Technical Economic College)Includes bibliographical references

Green machining for the dry milling process of stainless steel 304

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This paper presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed (V), depth of cut (a), feed rate (f), and nose radius (r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85 % and 57.65 %, respectively, and the power factor improves around 28.83 %, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices

The Potential of Beauveria against Root Mealybugs Formiccoccus sp. (Homoptera: Pseudococidae) Black Pepper in Dak Lak Province of Vietnam

Root mealybugs Formicoccus sp.(Hemiptera: Pseudococcidae) currently emerge as an economically important soil-borne insect pest in the production of black pepper (Piper nigrum L.) in Dak Lak province of Vietnam. Entomopathogenic fungi can be used in agricultural production as feasible safe biocontrol agents and plant growth promoters. This study aimed to isolate, select and identify the local Beauveria sp. strains from the black pepper fields, as well as evaluate the effects of the selected strains on the growth of black pepper. The results showed that the frequency of positive Beauveria sp. soil samples collected from organic black pepper fields was significantly higher than that from conventional black pepper fields. Twenty-one Beauveria sp. strains in black pepper organic fields and four Beauveria sp. strains in black pepper conventional fields were successfully isolated, among them, the Beauveria bassiana BB1 was examined and evaluated for its biocontrol potential against Formicoccus sp. and its ability to promote the growth of black pepper seedlings. In the laboratory experiments, the mortality of both adults and first instar nymph root mealybugs Formicoccus sp. caused by B. bassiana BB1 culminated 100% at 21 days after treatments. In the nethouse, at 6 months after treatment, the efficacy of B. bassiana BB1 against black pepper root mealybugs reached 99.18%. The levels of photosynthetic pigments in the leaves of the black pepper seedlings increased about 2 times compared to the control. Moreover, B. bassiana BB1 strain also remarkably promoted the growth of the black pepper seedlings under the nethouse conditions