133 research outputs found
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
OVERALL ASSESSMENT OF DEFORMATION AND FORCE OF DIAPHRAGM WALL JOINTS DURING THE STAGES OF DEEP EXCAVATION CONSTRUCTION
In the realm of geotechnical engineering, deep excavation projects face intricate challenges, especially concerning the stability of barrette walls, which are highly susceptible to deformation and stress at their joints. This study focuses on evaluating the deformation and force behavior of barrette wall joints at the position of greatest deformation. The Finite Element Method (FEM) is utilized to simulate the behavior of these structures under various load conditions. The Analysis of Variance (ANOVA) method is employed to statistically analyze the FEM data, assessing the impact of different factors on deformation and force distributions within the barrette wall joints. The specific objective of this study is to determine the statistical significance of the observed deformations and understand the influence of construction stages on joint integrity. This methodological synergy enhances the predictability of engineering assessments and ensures that design and construction decisions are grounded in solid empirical evidence. The study's findings emphasize the importance of precise monitoring and advanced predictive techniques to mitigate potential risks associated with deep excavations, particularly at critical joint locations. The results indicate that the deformation patterns are primarily influenced by the geometrical setup of the walls and the mechanical properties of the soils. The greatest deformations were typically observed where the wall joints experienced the highest bending moments and shear forces, conditions exacerbated by unfavorable soil mechanics and hydrostatic pressures. The clear and consistent increase in total displacement highlights the progressive destabilization of the wall as the excavation depth increases. By integrating ANOVA with FEM, this study contributes to enhancing safety and efficiency in deep excavation projects by ensuring that decisions are grounded in empirical evidence
ZAC: Efficient Zero-Knowledge Dynamic Universal Accumulator and Application to Zero-Knowledge Elementary Database
—Zero-knowledge universal accumulator generates the succinct commitment to a set and produces the short (non) membership proof (universal) without leaking information about the set (zero-knowledge). In order to further support a generic set and zero-knowledge, existing techniques generally combine the zero-knowledge universal accumulator with other protocols, such as digital signatures and hashes to primes, which incur high overhead and may not be suitable for real-world use. It is desirable to commit a set of membership concealing the information with the optimal complexity. We devise ZAC, a new zero-knowledge Dynamic Universal Accumulator by taking the existing cryptographic primitives into account to produce a new efficient accumulator. Our underlying building blocks are Bloom Filter and vector commitment scheme in [19], utilizing the binary expression and aggregation to achieve efficiency, generic set support, zero-knowledge and universal properties. As a result, our scheme is improved in terms of proof size and proof time, also comparable to the RSA-based set accumulator in [8] in the verifying complexity. With 128 bit security, our proof size is 48 bytes while theirs is 1310 bytes and the running time of elliptic curve-based methods is faster than RSA-based counterpart. ZAC is proved to be complete, ϵ-sound and zero-knowledge. Extensively, based on ZAC as building block, we construct a new Zero-Knowledge Elementary Database (ZKEDB), which consumes 5 times less storage space, O(log N) less bandwidth, and O(log N) more efficient in proving and verification than the state-of-art work in [13] (where N is the domain space size). ZKEDB is proved to be complete, ϵ-sound and zero-knowledge. ZKEDB supports a new type of select top ℓ query, and can be extended to non-elementary databases
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
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