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Beyond empiricism: a unified physics-based framework for resonance avoidance and carbon-efficient mass optimization of machine foundations
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Dynamic machine foundations can be considered as a necessary component of the industrial infrastructure. Design of the dynamic equipment foundations has, however, traditionally been grounded on a rule of thumb that is inaccurate and rigid to use at the discretion of the engineers. The conventional rule of thumb, which includes minimum weight ratios and resonance avoidance criteria, has been used singularly with two poles, which can be either conservatively designed systems that are too heavy, or systems that are going to experience too much vibration and fatigue. This paper presents a novel, analytical framework for the reinterpretation of traditional design practices, using a physics-based approach, and results in a single, unified overall performance metric: the Combined Safety Index (CSI). The method utilizes frequency-dependent soil-foundation interaction models, allowing for a systematic evaluation of both inertially related and resonantly related stability under harmonic excitations. Using large-scale validations of real-world, global operational and geotechnical data from numerous case studies, including centrifugal compressors, blowers, and horizontal equipment, the reliability of the framework was demonstrated to be high (> 97%), with greater than 97% of the simulated designs meeting CSI ≥ 1.0. In addition, the method allows for mass optimization resulting in reductions in the amount of concrete used, and thus reductions in cost and environmental impact, of up to 45%. Unlike rule-of-thumb methods, this model allows designers to make informed decisions regarding the trade-off between the amount of mass of the foundation and detuning of the operating frequency, and thus supports economic efficiency and environmental sustainability. Statistical analyses, including local and global sensitivity analysis and Monte Carlo uncertainty quantification of the results, confirmed that the primary variables controlling system safety are the damping ratio (ζ) and the mass of the foundation (Wf). This work therefore provides practicing engineers with a practical, computationally efficient tool for designing safer, more sustainable foundations, and assists in advancing the state-of-the-art in design practice and in advancing digital engineering. © Springer Nature Switzerland AG 2026.

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Publication Date
Thu Jul 01 2010
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The Inclusion of Science Process Skills in Yemeni Secondary School Physics Textbooks
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The aim of this study is to compare and contrast the science process skills (SPS) included in the 10th-12th grade physics textbooks content utilized in Yemeni schools. The study revealed weaknesses and strengths in the textbooks’ content. For instance, a number of science process skills (SPS), such as measuring, predicting and hypothesizing, have been neglected in the 11th grade textbook. For all three textbooks content, the maximum percentage of basic science process skills (BSPS) was devoted to observation. However, the maximum percentage of integrated science process skills (ISPS) was different for each grade: experimenting had the highest percentage for 10th grade, interpreting data for 11th grade, and operational definitions for 12th

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Publication Date
Thu Aug 30 2018
Journal Name
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Mass Transfer Study for Bio-Synergy in Dairy Wastewater Treatment Plant
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The present study addresses the behavior of gases in cultivation media as an essential factor to develop the relationship between the microorganisms that are present in the same environment. This relationship was explained via mass transfer of those gases to be a reasonable driving force in changing biological trends. Stripping and dissolution of oxygen and carbon dioxide in water and dairy wastewater were investigated in this study. Bubble column bioreactor under thermal control system was constructed and used for these processes. The experimental results showed that the removal of gases from the culture media requires more time than the dissolution. For example, the volumetric mass transfer coefficient for the removal

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Publication Date
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The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

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Mon Jun 01 2020
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