Engineering Framework Research Articles

Metabolic engineering of oleaginous yeast in the lipogenic phase enhances production of nervonic acid

Insufficient biosynthesis efficiency during the lipogenic phase can be a major obstacle to engineering oleaginous yeasts to overproduce very long-chain fatty acids (VLCFAs). Taking nervonic acid (NA, C24:1) as an example, we overcame the bottleneck to overproduce NA in an engineered Rhodosporidium toruloides by improving the biosynthesis of VLCFAs during the lipogenic phase. First, evaluating the catalytic preferences of three plant-derived ketoacyl-CoA synthases (KCSs) rationally guided reconstructing an efficient NA biosynthetic pathway in R. toruloides. More importantly, a genome-wide transcriptional analysis endowed clues to strengthen the fatty acid elongation (FAE) module and identify/use lipogenic phase-activated promoter, collectively addressing the stagnation of NA accumulation during the lipogenic phase. The best-designed strain exhibited a high NA content (as the major component in total fatty acid [TFA], 46.3%) and produced a titer of 44.2 g/L in a 5 L bioreactor. The strategy developed here provides an engineering framework to establish the microbial process of producing valuable VLCFAs in oleaginous yeasts.

Determining The Main Criteria Required for The Green Transition of The Maritime Industry in Terms of MTME

In this study, the main criteria for the green transition of the maritime industry have been determined, taking into account the criteria determined by the International Maritime Organization (IMO) for the year 2050 and beyond. The criteria were prioritized using an AHP approach and the DEMATEL technique. The aim of the study is to determine whether the green conversion can meet the operational requirements of merchant ships. For this purpose, quantitative and qualitative research techniques were used as a hybrid. The ultimate goal of the study is to evaluate the impact of merchant ships, which will be built in the green concept, on the operational needs, within the framework of maritime transportation management engineering (MTME). At the end of the study, a gradual roadmap was presented to the maritime industry, which will prepare a strategic target plan within the scope of the green transition of the maritime industry.

Active learning based reverse design of hydrogen production from biomass fuel

Biomass hydrogen production holds substantial promise in addressing critical challenges within the realms of renewable energy and environmental sustainability. Nevertheless, the labor- and time-intensive nature of conducting combinatorial experiments and relying on traditional trial-and-error methods significantly constrains the exploration of biomass fuel solutions. This paper introduces a novel framework for the reverse engineering of hydrogen production systems, which seamlessly integrates Bayesian active learning with machine learning models. In Bayesian active learning, 307 biomass fuel samples were expanded to 1354 in order to ensure the accuracy of the model prediction between the seven characteristics of biomass fuel and hydrogen production. Four machine learning algorithms including RF, XGBoost, KNNR, AdaBoost were used to build the prediction model. A stochastic search reverse design approach based on the XGBoost model with the highest coefficient of determination (R2 = 0.91) was innovatively used to realize the “reverse design” from “target performance” to “composition and process”. Through reverse design, a two-order-of-magnitude increase in design space is achieved in hundreds of times less time than conventional design time, resulting in a significant improvement in hydrogen production efficiency. The practical application of this method for hydrogen production from biomass fuels may go far beyond linear extrapolation based on the current state of the art and can be applied to a wider range of material designs.

BECO (Business, Engineering, Construction, and Other) Framework for Providing Effective and Comprehensive Supportive Services to Disadvantaged Business Enterprises in the United States

The Disadvantaged Business Enterprise program, established by the United States Department of Transportation, aims to remove barriers to participation of Disadvantaged Business Enterprises (DBEs) in highway projects, to promote the use of DBEs in federally assisted contracts, and to assist the development of DBEs. A DBE is a small, for-profit business owned by socially and economically disadvantaged individuals such as women or minorities. DBEs need various supportive services, depending on many factors, such as business area, size, and strategy. State Departments of Transportation often provide or hire third parties to provide supportive services to DBEs that are relatively expensive and inefficient. These services may also support only some and unintentionally exclude other DBEs. Under many challenges, one ultimate goal is to find a framework that covers all valuable DBE supportive services. This study proposes, tests, and validates a framework for providing effective and comprehensive DBE supportive services in the transportation sector. Based on discussions with DBE liaison officers and service providers, we propose the business, engineering, construction, and other (BECO) framework to provide DBE supportive services. We then use a sequential explanatory design in mixed methods, collecting quantitative and qualitative data to evaluate and validate the BECO framework. We analyze quantitative data using confirmatory factor analysis and qualitative data using pattern coding techniques. The results provide insights and reveal useful DBE supportive services using the BECO framework. The framework is useful for assessing DBE needs, informing DBE liaison officers and service providers, and offering the most useful supportive services to DBEs.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Developing and tailoring business process management methods using the situational method engineering approach

Effective business process management is one of the key tasks of every organization if it wants to compete successfully on the market and satisfy the needs of its customers. Although there are many established best practices, techniques, and more or less complex methods in business process management, no approach can fulfill a typical organization’s needs and wants. The key to success lies in developing new or adapting existing approaches for business process management to suit the unique characteristics of individual organizations and projects. This demands a solid methodological framework and a clearly defined work process. The aim of the paper is to apply situational method engineering concepts within the realm of business process management, thereby tackling the challenge mentioned above. As a result, a situational method engineering framework is defined to construct and customize business process management methods. The study proposes the conceptual foundations of the framework and a set of processes for constructing holistic and tailored methods that cover various aspects of business process management (analysis, modeling, management, etc.). The basis of the framework is represented by general method components at a higher level of abstraction, which are assembled into holistic methods via interfaces and are transformed into versions adapted to specific situations using tailoring rules. The practical applicability of the proposed framework is validated through its implementation in a project at a large manufacturing company, where it is used to develop both a general and customized business process management method.

Modelling of non-structural components of an industrial multi-storey frame for seismic risk assessment

Seismic risk assessment of industrial facilities is complex due to the presence of different types of equipment. It represents a research issue that requires further investigation. To this end, some analytical approaches have been developed in the framework of performance-based earthquake engineering. Nonetheless, their accuracy in the case of complex critical facilities, such as nuclear and non-nuclear industrial plants, is still under investigation. Thus, the proposed study intends to research in depth, in a risk assessment framework, some critical aspects related to: (1) modelling of industrial facilities and their secondary equipment with different degrees of accuracy, also taking into account their dynamic interaction; (2) selection of seismic records for fragility analysis, due to the narrow distribution of frequency values for non-structural components; (3) effectiveness of performance-based earthquake engineering applied to this particular class of coupled structure-equipment for risk assessment. In this context, the proper selection of seismic records becomes relevant, and SCoRes, an innovative algorithm for accelerograms selection is worthy of investigation. On these premises, two different configurations of a primary industrial structure, i.e. a moment resisting frame and a braced frame, equipped with non-structural components and subjected to shake table test campaigns are selected as case studies. For the two configurations, a vulnerability assessment of two vertical tanks installed on the first floor was carried out. Along these lines, to establish the effectiveness of the proposed method for both the moment resisting frame and braced frame configurations, the mean annual frequency of exceedance of the bottom-wall strain of the above-mentioned tanks, both at the design basis and safe shutdown earthquake has been evaluated.

Molecular Control of Floquet Topological Phase in Non-adiabatic Thouless Pumping.

Non-adiabatic Thouless pumping of electrons is studied in the framework of topological Floquet engineering, particularly with a focus on how atomistic changes to chemical moieties control the emergence of the Floquet topological phase. We employ real-time time-dependent density functional theory to investigate the extent to which the topological invariant, the winding number, is impacted by molecular-level changes to trans-polyacetylene. In particular, several substitutions to trans-polyacetylene are studied to examine different effects on the electronic structure, including the mesomeric effect, inductive effect, and electron conjugation effect. Maximally localized Wannier functions are employed to relate the winding number to the valence bond description by expressing the topological pumping as the transport dynamics of the localized Wannier functions. By further exploiting the gauge invariance of the quantum dynamics in terms of the minimal particle-hole excitations, the topological pumping of electrons can be also represented as a cyclic transition among the bonding and antibonding orbitals. Having connected the topological invariant to the chemical concepts, we demonstrate molecular-level control of the emergence of the Floquet topological phase, presenting an exciting opportunity for the intuitive engineering of molecular systems with such an exotic topological phase.

THE TIDE IS HIGH: NEW INSIGHTS ON COASTAL FLOODING TRENDS AND FUTURES

Coastal flood frequencies are expected to increase around the world due to sea-level rise (SLR). This is largely related to the emergence of tidal flooding, where coastal flood thresholds are exceeded by astronomical tides alone. We will discuss the concept of tidal flooding and the observed trends from Australia and the United States. We then apply a new risk assessment framework (Hague et al. 2022) to identify which locations are most vulnerable to chronic flooding due to SLR this century. Finally, we discuss the implications of a global application of this framework for coastal engineering and management applications.

Analysis of cognitive framework and biomedical translation of tissue engineering in otolaryngology

Tissue engineering is a relatively recent research area aimed at developing artificial tissues that can restore, maintain, or even improve the anatomical and/or functional integrity of injured tissues. Otolaryngology, as a leading surgical specialty in head and neck surgery, is a candidate for the use of these advanced therapies and medicinal products developed. Nevertheless, a knowledge-based analysis of both areas together is still needed. The dataset was retrieved from the Web of Science database from 1900 to 2020. SciMAT software was used to perform the science mapping analysis and the data for the biomedical translation identification was obtained from the iCite platform. Regarding the analysis of the cognitive structure, we find consolidated research lines, such as the generation of cartilage for use as a graft in reconstructive surgery, reconstruction of microtia, or the closure of perforations of the tympanic membrane. This last research area occupies the most relevant clinical translation with the rest of the areas presenting a lower translational level. In conclusion, Tissue engineering is still in an early translational stage in otolaryngology, otology being the field where most advances have been achieved. Therefore, although otolaryngologists should play an active role in translational research in tissue engineering, greater multidisciplinary efforts are required to promote and encourage the translation of potential clinical applications of tissue engineering for routine clinical use.

The NEF-SPA Approach as a Framework for Developing a Neurobiologically Inspired Spiking Neural Network Model for Speech Production.

The computer-based simulation of the whole processing route for speech production and speech perception in a neurobiologically inspired way remains a challenge. Only a few neural based models of speech production exist, and these models either concentrate on the cognitive-linguistic component or the lower-level sensorimotor component of speech production and speech perception. Moreover, these existing models are second-generation neural network models using rate-based neuron approaches. The aim of this paper is to describe recent work developing a third-generation spiking-neuron neural network capable of modeling the whole process of speech production, including cognitive and sensorimotor components. Our neural model of speech production was developed within the Neural Engineering Framework (NEF), incorporating the concept of Semantic Pointer Architecture (SPA), which allows the construction of large-scale neural models of the functioning brain based on only a few essential and neurobiologically well-grounded modeling or construction elements (i.e., single spiking neuron elements, neural connections, neuron ensembles, state buffers, associative memories, modules for binding and unbinding of states, modules for time scale generation (oscillators) and ramp signal generation (integrators), modules for input signal processing, modules for action selection, etc.). We demonstrated that this modeling approach is capable of constructing a fully functional model of speech production based on these modeling elements (i.e., biologically motivated spiking neuron micro-circuits or micro-networks). The model is capable of (i) modeling the whole processing chain of speech production and, in part, for speech perception based on leaky-integrate-and-fire spiking neurons and (ii) simulating (macroscopic) speaking behavior in a realistic way, by using neurobiologically plausible (microscopic) neural construction elements. The model presented here is a promising approach for describing speech processing in a bottom-up manner based on a set of micro-circuit neural network elements for generating a large-scale neural network. In addition, the model conforms to a top-down design, as it is available in a condensed form in box-and-arrow models based on functional imaging and electrophysiological data recruited from speech processing tasks.

First Year Engineering Students’ Difficulties with Math Courses- What Is the Starting Point for Academic Teachers?

The discussion about first-year engineering students’ difficulties in mathematics is continuous in the fields of engineering, mathematics and higher education. The present study aimed to examine the initial barriers academic math teachers need to have in mind if they want to improve students’ performance in engineering math courses through appropriate teaching practices in order to face their initial interindividual differences. During the first phase of the study, we examined first year engineering students’ initial beliefs about the nature of mathematics, their self-efficacy beliefs about mathematics and their basic mathematical knowledge. The math school grade was used for their previous mathematical performance. Results indicated the predominant role of the previous mathematical knowledge and the important role of the formalistic disposition toward mathematics. The lack of experience of using mathematics for problem-solving situations within an engineering framework prevented students from recognizing and appreciating the value of mathematical courses during the engineering studies. The second phase of the study examined, through an interview with a group of students, their perceptions of the teaching practices which were introduced after their teacher attended a training program. The discussion concentrates on how academics can use teaching processes for equity and not equality in order to motivate their students.

Probabilistic assessment of the rules used to combine peak floor responses of special concentrically braced frames under orthogonal seismic effects

AbstractEstimating seismic demands and the associated damage to nonstructural components (NSCs) is a critical step in the performance‐based earthquake engineering (PBEE) framework. To facilitate this type of assessment, NSCs are classified as being acceleration, velocity, or displacement sensitive. When two‐dimensional structural models and response history analyses are used in the PBEE framework, heuristics are used to combine the orthogonal responses. Although there has been significant research to develop and evaluate combination rules for structural component responses, comparatively much less attention has been placed on NSC demands. FEMA P‐58 does provide a combination rule specifically for NSC seismic demands; however, the basis and accuracy of these provisions have never been examined. In this paper, the effectiveness of three popular combination rules used to estimate peak floor acceleration (PFA) and peak floor velocity (PFV) is investigated. Incremental dynamics analyses under unidirectional and bidirectional record‐sets are performed on six special concentrically braced frame buildings with different heights and torsional irregularity. The associated floor responses are used to examine the efficacy of the combination rules. Subsequently, a probabilistic approach is proposed to enhance the accuracy of combination rules. The performance of the enhanced combination rules is compared to that of a surrogate model that estimates PFA and PFV under orthogonal seismic effects. The results show that the performance of existing combination rules is slightly affected by building height and plan irregularity. Also, the combination rule suggested by FEMA P58 is found to overestimate the PFA and PFV demands by 10–15% compared to the demands derived from bidirectional response history analysis.

Integrated Value Engineering and Risk Assessment Performance Measurement Framework in Ship-Manufacturing Industry towards Net Zero Emissions Using Fuzzy DEMATEL-AHP

This paper aims to apply the novel integrated Value Engineering and Risk Assessment (VENRA) framework for measuring shipyard performance based on the combination of fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Hierarchy Process (AHP) tools. These are used to assess the criteria cause-effect and weight ranking analysis, considering the causal and affected groups while prioritising the criteria and sub-criteria ranking. A shipyard case study was used to apply the proposed framework, showing that the shipyard with a high personnel’s safety group is majorly more important than the environmental impact. The combination of hybrid MCDM tools has enhanced the process of determining the criteria analysis. Waste management has become the most impacting attribute amongst the criteria group, while the HSE department is the most critical criterion. However, the green energy used is still a minor factor as it is still not fully exploited within the existing shipyard and has not been fully supported by existing regulations yet.

Floquet-engineered chiral-induced spin selectivity.

The control of electron spin, crucial to the stability of matter, offers new possibilities for manipulating the properties of molecules and materials with potential applications in spintronics and chemical reactions. Recent experiments have demonstrated that electron transmission through chiral molecules depends on the electron spin orientation, a phenomenon known as chiral-induced spin selectivity (CISS). In this study, we show that CISS can be observed in achiral systems driven by an external circularly polarized laser field in the framework of Floquet engineering. By using the Floquet theory for a time-periodically driven system to investigate spin-dependent electron transport in a two-terminal setup, we demonstrate that the spin polarization can approach unity if the light intensity is sufficiently strong, the rate of dephasing is sufficiently low, and the average chemical potential of the two leads is within an appropriate range of values, which is narrow because of the high frequency of the laser field. To obtain a broader range of energies for large spin polarization, a combination of chiral molecules and light-matter interactions is considered, and the spin polarization of electrons transported through a helical molecule driven by a laser field is evaluated.

Model-Based System Engineering Framework for Superconducting Accelerator Magnet Design

The design specification of future high-field accelerator magnets require innovation in and integration of multiple disciplines. Numerical models underpin each step of the design. In this paper, we present a methodology for collaborative modeling based on relevant concepts of model-based systems engineering. The methodology is composed of three pillars: encapsulated computing environments with service query interface, model notebooks with auto-generated model views, and model query interface with results caching. The methodology aims at a de-centralized approach to multi-model and multi-scale collaboration that maintains the cornerstones of traceability and reproducibility. It is demonstrated with a multi-model coil design optimization of a high-field superconducting magnet.

Language Translation from English to Malay in Solid Waste Engineering

Engineering translations are dense with technical jargon and terms that necessitate linguistic and field expertise. The challenges arise from the requirement that the target texts be idiomatic and retain the same phrasing order, meaning, and nuances as the source text, which a layperson cannot translate directly. The Malay language is Malaysia’s official language. Because Malay is the official language in the governing and executive constitutions, the research instrument should be available in Malay. The purpose of this research is to translate from English to Malay a qualitative risk framework for solid waste engineering. On three psychometric scales, 26 items were translated using a back-translation method involving eight linguistic experts. The Malaysian Institute of Translation and Books (ITBM) provided the primary translation, which was then reviewed by a panel of experts as a secondary translation. Next, content validation on a 5-point Likert scale was conducted with five civil engineering field experts to assess instrument structure and reliability agreement. To represent the expert validation process, a descriptive analysis of mean score agreement was performed. The studies discovered deviation losses in forward (8.98%) and backward (17.95%) translation. The results also revealed experts produce accurate translations, particularly the equivalents of engineering expressions, acronyms, measurements, and terminology. Eventually, expert consensus on six aspects was achieved for 27.8/30 (92.7%) and affirmed that the framework is valid and thus applicable. This paper recommends that translation requires quality control, which comprises three processes: conversion to the target language, comparison, and reconciliation by subject matter experts.

Realisation of responsive and sustainable reconfigurable manufacturing systems

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55± 4.7, 84± 4.8, and 79± 5.7min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2± 0.19, 6.0± 0.68, and 14± 2.8min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

Cyber oriented digital engineering

AbstractThe purpose of cyber oriented digital engineering (CODE) is to provide a repeatable systems‐engineering process for systems to resist Stuxnet‐class advanced persistent threat (APT) cyberattacks. CODE integrates cyber thinking with systems thinking. CODE accomplishes this by extending the US Department of Defense (DoD) digital engineering (DE) framework with functional mission analysis for cyber (FMA‐C), hybrid cloud architecture, zero trust (ZT) principles, threat analysis, and hardware cyber hardening (HCH). The lack of success of red team attacks conducted in our laboratory against an exemplar system demonstrates how following the CODE systems engineering process actually does “bake cybersecurity into the system”, making the resulting systems architecture and implementations more resilient. In a recent Pilot Project, CODE enhanced the systems requirements document of the top left side of the systems engineering Vee. CODE Pilot requirements embodied ZT principles, including machine to machine (M2M) credential exchanges and internal self‐checking. We anticipate working with International Council on Systems Engineering (INCOSE), the object management group (OMG,) and others towards standardizing CODE's cyber‐systems engineering processes for broader use of the global systems engineering community.