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In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches. [ABSTRACT FROM AUTHOR]

A three-dimensional computer model was developed for the integrated chemical engineering process of water shutoff of a high-water-cut oil-bearing rock formation at given geophysical characteristics of the bottomhole formation zone and given rate and volume of a water shutoff solution injected into the formation. The model makes it possible to determine the parameters of filling of pores of the formation with the water shutoff solution, which are necessary for calculating the well's oil and water production rates. The adequacy of the developed mathematical model was checked against the results of a comparative analysis of the calculated values of the water cut and the oil production rate with the actual well performance data after the water shutoff: the mean absolute errors in oil production rate and water cut were 0.5 t/day and 0.9%, respectively. [ABSTRACT FROM AUTHOR]

In the year 2020, the Global Journal of Flexible Systems Management (GJFSM) has celebrated its two decades of publication. This study is an attempt to commemorate the two decades of publication by presenting the overview of the GJFSM along with the trajectory of flexibility research in various journals. By using multiple bibliometric tools and indicators, the study finds that the GJFSM has grown over the years in terms of total publications and citations. The contributors are from across the globe, i.e., South America, North America, Asia, Europe, Africa, and the Pacific. The Journal publishes several flexibility areas, including information systems flexibility, financial flexibility, supply chain flexibility, technology management flexibility, marketing flexibility, organizational flexibility, strategic flexibility, and manufacturing flexibility. The GJFSM is cited by authors from various countries across the globe. It has been cited across different Scopus categorizations, including "Strategy and Management," "Business and International Management," "General Business, Management and Accounting," "Industrial and Manufacturing Engineering," and "Management Science and Operations Research." Keyword co-occurrences analysis helps to analyze the various groups of keywords cited together in the GJFSM. Co-citation analysis of references helps to identify crucial clusters of GJFSM, i.e., strategic flexibility, manufacturing flexibility, the conceptual framework of flexibility, supply chain flexibility, modeling flexibility, and application of TISM. Overall, GJFSM has seen an increase in both publications and citations, reflecting its increasing presence among the journals publishing flexibility research. The diversity of flexibility research and its contributions to research under-one-roof make this Journal unique. The paper concludes with the gap areas of flexibility research and the way forward. [ABSTRACT FROM AUTHOR]

Cappelen (Fixing language, Oxford University Press, Oxford, 2018) proposes a radically externalist framework (the 'Austerity Framework') for conceptual engineering. This approach embraces the following two theses. Firstly, the mechanisms that underlie conceptual engineering are inscrutable: they are too complex, unstable and non-systematic for us to grasp. Secondly, the process of conceptual engineering is largely beyond our control. One might think that these two theses are peculiar to the Austerity Framework, or to metasemantic externalism more generally. However, Cappelen argues that there is no reason to think that internalism avoids either commitment. Cappelen argues that to do so she must provide arguments for 3 claims: (a) there are inner states that are scrutable and within our control; (b) concepts supervene on these inner states; and (c) the determination relation from supervenience base to content is itself scrutable and within our control. In this paper, I argue that internalist conceptual role theories of content can meet Cappelen's challenge. [ABSTRACT FROM AUTHOR]

Microbial iron reduction (MIR) is an important and ubiquitous natural process in the biogeochemical cycling of iron and carbon in anaerobic sedimentary and subsurface environments. The objectives of this study were (1) to determine if the MIR process can enhance the inactivation of Escherichia coli cells under anaerobic conditions and (2) to identify potential inactivation mechanisms. Laboratory microcosm experiments showed that the presence of MIR activity significantly enhanced E. coli inactivation, and the inactivation rate under the MIR condition was significantly larger than those under other anaerobic redox conditions. Under anoxic condition, higher Fe2+concentrations exhibited a linear function to larger E. coli inactivation rates, indicating that the production of Fe2+by MIR was one of the important roles in E. coli inactivation. When E. coli cells were amended as the sole electron source to the MIR process, increased Fe2+ production was observed, which corresponded to decreasing TOC concentration. Together, the results suggest that MIR enhanced E. coli inactivation through the production of Fe2+ as metabolic waste, and the inactivation benefited the MIR process as the inactivated cells were used as an electron source, which represents a potential new mechanism for bacterial inter-species competition. This knowledge could further improve our understanding of the fate of fecal bacteria in natural environments where the MIR process is prevalent, and may also be explored for enhanced removal of bacterial pathogens in engineering processes. [ABSTRACT FROM AUTHOR]

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge. [ABSTRACT FROM AUTHOR]

The theory of fractional integral inequalities plays an intrinsic role in approximation theory also it has been a key in establishing the uniqueness of solutions for some fractional differential equations. Fractional calculus has been found to be the best for modeling physical and engineering processes. More precisely, the proportional fractional operators are one of the recent important notions of fractional calculus. Our aim in this research paper is developing some novel ways of fractional integral Hermite–Hadamard inequalities in the frame of a proportional fractional integral with respect to another strictly increasing continuous function. The considered fractional integral is applied to establish some new fractional integral Hermite–Hadamard-type inequalities. Moreover, we present some special cases throughout discussing this work. [ABSTRACT FROM AUTHOR]

As governments around the world prepare for a transition period to a decarbonised energy and economic future, petroleum is needed to smoothen that transition. Based on the analysis of the International Energy Agency's 2020 projections, around 770 billion barrels of oil are required to meet demand from now to 2040. However, according to British Petroleum's Statistical Review of World Energy 2020, as at the end of 2019, the global total reserves of recoverable conventional and unconventional oils is approximately 1734 billion barrels. Out of that, the conventional easy-to-produce light oil accounts for only 30% (i.e. accounts for only 520.2 billion barrels). Therefore, the remaining 249.8 billion barrels of oil needed to satisfy demand up to 2040 must come from unconventional oils, namely heavy oils and bitumen. However, these unconventional resources are very difficult to produce and the current production methods have very high environmental footprints. Consequently, in accordance with climate crisis mitigations, the vast reserves of the virtually unexploited heavy oils and bitumen must be developed using advanced and greener extraction technologies, such as the yet-to-be-fully-understood THAI process which provides partial upgrading of heavy oil/bitumen via in situ combustion. Using validated numerical models which are developed using the CMG's reservoir thermal simulator, the STARS, which is also used in this study, field scale reservoir simulations of the THAI process were performed with the wells arranged in staggered line drive (SLD) and direct line drive (DLD). Over the 834 days of operating time, the cumulative oil recovery in SLD is 32% of oil originally in place (OOIP) which is equivalent to 26,100 m3 whilst that in DLD is 27% OOIP. This shows that more oil (i.e. an additional 5% OOIP) was cumulatively recovered in SLD compared to in DLD model. It is found that smaller reservoir volume was swept by the combustion front in DLD and thus making the heat-affected reservoir volume smaller than that in SLD model. Furthermore, in DLD, due to the nearness of the injector well to the toe of the horizontal producer (HP) well, oxygen production began much earlier, compared to in the SLD. It is also found that the temperature of the mobile oil zone is higher in the SLD model compared to that in the DLD model. This implies that higher quality oil is produced when the wells are configured in the SLD pattern. Therefore, this first-of-a-kind work has shown that SLD arrangement is far more efficient, safer, and produces higher quality oil than DLD pattern and that actual process engineering designs should use SLD wells configuration. [ABSTRACT FROM AUTHOR]

Wetting is a fundamental and important phenomenon that is encountered in various engineering processes, and particularly, the understanding of mesoscopic dynamic wetting of not only pure liquids but also suspensions with nanoparticles is required with the recent miniaturization of devices. Therefore, it is important to establish a technique to precisely measure the dynamic behavior of liquid thin films near the three-phase contact line. In the present study, the mesoscopic wetting behavior of nonvolatile polydimethylsiloxane (PDMS, 20 cSt) and a PDMS suspension containing PDMS-modified SiO2 nanoparticles at a concentration of 1 wt% is measured using a phase-shifting imaging ellipsometer (PSIE) developed in our previous study, although the aggregates of nanoparticles are formed in the suspension. The PSIE can measure the two-dimensional thickness profile of liquid thin films from nanometer to micrometer scales. From the measured thickness profiles, we investigate the effect of the suspended nanoparticles on the apparent contact angle θa and precursor film length Lp. The dependence of θa and Lp on the velocity of contact line U ranging from 10–8 to 10–6 m/s is characterized. The contact angles of both fluids reflect the Cox–Voinov law, i.e., the θa is proportional to U1/3. At the same U, the θa of the suspension is higher than that of pure PDMS. The Lp of both fluids is proportional to U−1 and is the same at the same U. Furthermore, the Lp and thickness profiles at the nanometer scale are consistent with adiabatic precursor film theory. [ABSTRACT FROM AUTHOR]

The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical programming model for the regenerative scheduling problem with deterministic processing times is formulated in the present paper to evaluate the economic advantage of accepting a new order in an engineer to order (ETO) manufacturing organization. The real case of an Italian ETO company which produces hydraulic marine and offshore cranes is afterwards presented. [ABSTRACT FROM AUTHOR]

We examine the temporal evolution of the maximum concentration of a dissolved inert solute in spatially heterogeneous subsurface flows. The maximum concentration of a given substance is at the basis of most of environmental regulatory practices where maximum tolerable levels of concentration are typically prescribed for a variety of known contaminants. Through the use of the Lagrangian framework, we elaborate over a physically based, semi-analytical model for the maximum concentration. Specifically, we address how the maximum concentration is affected by key geostatistical parameters (i.e., logconductivity variance), local-scale dispersion processes and engineering design variables such as the dimensions of the solute injection zone. The model will help in identifying the major components that determine the maximum concentration, which is important in order to better allocate resources toward site characterization and reduce uncertainty in predictions. The ultimate scope is to provide a theoretical framework that is application-oriented to estimate the maximum concentration in natural aquifers and provide some guidance in applications. It also provides an useful tool for preliminary, screening analysis and testing scenarios. We test the performance of the model against the MADE transport experiment, with reasonably good agreement. [ABSTRACT FROM AUTHOR]

This paper considers the fitting of a CAD template model to tessellated data as strategy to implement a reverse engineering process that aims at the reconstruction of a parametric associative CAD model. The reconstruction methodology, called Template-Based CAD Reconstruction (TCRT), has been presented and fully discussed in a previous paper Buonamici et al. (J Comput Des Eng 5:145–159, 2018). The present paper focuses on the study of a fast and robust strategy to perform the fitting of the Template CAD Model to reference data. The study explores how different optimization strategies and evaluation metrics can affect a parametric CAD-fitting methodology. Two different optimization algorithms (PSO and GA) and three formulations of the objective function are tested to find the most effective combination. Reconstruction test cases are presented and discussed in the text. [ABSTRACT FROM AUTHOR]

Design thinking has an important role in STEM education. However, there has been limited research on how students engage in various modalities throughout the design process in hands-on design tasks. To promote middle school students' engineering literacy, it is necessary to examine the use of design modalities during design. Using a case study approach, we examine middle school students' design stages and modalities during design activities. We also identify the patterns of design processes in the teams with different design outcomes. Drawing on theories in design thinking and embodied interaction, we proposed a framework and devised a video analysis protocol to examine students' design stages and modalities. Middle school students attending a design workshop engaged in two design activities in teams of 3–4 people. The design sessions were video recorded and analyzed using the video analysis protocol. The teams engaged in the stages of planning, building, and testing, while employing the verbal, the visual, and the physical modalities. The teams that varied in design outcomes exhibited different patterns in the use of multiple modalities during the design stages. This study contributes to research on design thinking by proposing a framework for analyzing middle school students' multimodal design processes and presenting data visualization methods to identify patterns in design stages and modalities. The findings suggest the necessity to examine students' use of design modalities in the context of design stages and imply the potential benefits of using multiple modalities during design. The implications for future research and education practices are also discussed. [ABSTRACT FROM AUTHOR]

We analyze the accumulated experience of application of the technologies of utilization of the wastes of machine-building and metallurgy. It is shown that the most promising directions of utilization of wastes are connected with the development of technologies combining the procedures of recycling with the processes of formation of the functional coatings intended for the restoration and hardening of machine parts. We also indicate the efficiency of the procedure of electroerosion dispersion of the wastes of conducting materials used to obtain nano- and micro-sized powders suitable for the deposition of functional coatings. It is shown that the electric-contact welding of the wastes of tool-making industry enables us to obtain functional coatings as a result of combination of the processes of recycling, hardening, and restoration of machine parts without their melting. We also propose the technology of hardening of the plane surfaces of working elements of the agricultural equipment by electric-contact welding of the wastes of tool steel, which makes it possible to make their service life 1.8–2.0 times longer as compared with the service life of new commercially produced working tools. [ABSTRACT FROM AUTHOR]

Clostridium acetobutylicum DSM 792 has been characterized as a native acetone-butanol-ethanol (ABE) fermenting bacterium. C. acetobutylicm is used as a model organism to improve solvent production by metabolic engineering strategies. In this study, the ABE pathway in C. acetobutylicum was modified by overexpressing the pyruvate decarboxylase gene from Zymomonas mobilis under the control of 2 promoters, namely Pferr and Ppta (promoters of genes encoding pyruvate ferredoxin oxidoreductase and phosphotransacetylase) and the recombinant strains were designated as DSM 792(pPDC1) and DSM 792(pPDC2). The specific activity of the alcohol dehydrogenases in the pdc-expressing strains was higher than that in the wild-type strain. In batch fermentation experiments, the total ABE production from the recombinant strains DSM 792 (pPDC1) and DSM 792 (pPDC2) was 1.57 and 7.9 g/L, respectively, which was higher when compared to the wild type ABE production (0.64 g/L) under uncontrolled pH. The alcohol to acetone ratio (BE/A) was found to be 2.28, 2.77, and 5.26 in wild type, DSM 792 (pPDC1), and DSM 792 (pPDC2), respectively. This suggests that the re-routing of pyruvate by overexpression of the pdc could play a role in the generation of more reducing equivalents towards ethanol and butanol production. [ABSTRACT FROM AUTHOR]

The processing and interpretation of microseismic (MS) signals are the basis for obtaining source information of MS events in rock engineering. However, MS signal processing is subjective and time-consuming, in which different strategies have to be empirically adopted to cope with different issues. To facilitate MS signal analysis, this study proposes an entirely data-driven method based on local mean decomposition (LMD), which has been applied in various signal processing fields. Through some numerical experiments, the superiorities (e.g., smaller reconstruction errors and fewer sifting iterations) of the proposed method over other methods are demonstrated, and the proposed method is identified as an effective filtering and frequency analysis tool to process recorded MS signals. Our analysis results show that the proposed method can effectively remove interfering noise existing in MS signals collected from the Lianghekou hydropower station during underground rock excavation. Moreover, the proposed method can be employed to analyze the frequency revolution features of MS signals before and after rock mass failure. In addition, it is found that MS signal frequency migrates to a low-frequency range when approaching rock failure, which can be regarded as an early warning indicator of rock instability. The proposed method is believed to facilitate MS monitoring and offer a new clue to conduct MS signal processing and interpretation. [ABSTRACT FROM AUTHOR]

Mass customization (MC) is recognized as a new manufacturing paradigm for market competition, and several empirical studies have discussed elements of organizational structure that are conducive to MC. A previous study proposed a model of quality at source and identified cultural values that can be changed by cultural artifacts, such as the stop-the-line system and the Andon system. This paper explores the impacts of three groups of MC enabler variables (personalized design, modular design, and automated processes) on two internal dimensions of organizational culture (work cooperation and relationship harmony).This paper surveyed 160 samples across five manufacturing industries. The results confirm that the three MC enablers (personalized design, modular design, and automated processes) correlate positively with two internal dimensions of the organizational culture of Chinese manufacturers (work cooperation and relationship harmony). Of the two internal dimensions, work cooperation enhances organizational performance, while relationship harmony has no impact on organizational performance. Modular design, automated processes, and work cooperation are powerful tools in the implementation of MC and organizational performance. Personalized design and relationship harmony are useful in MC. However, these tools are intermediate and limited, and they do not contribute to the general performance of the entire organization. Thus, in terms of internal- and team-oriented cultural factors, work cooperation is more important than relationship harmony in the implementation of MC. Relationship harmony is a tool for managing work atmosphere, and this is a new characteristic feature of MC as a new operational paradigm. [ABSTRACT FROM AUTHOR]

The firefly algorithm is nature-inspired, and it belongs to swarm intelligence category optimization. Firefly algorithm is the latest algorithm developed under the nature-inspired algorithm (NIA), which is fruitful for business and engineering optimization. So, for business optimization, healthcare industries are affected through big data where big data affects in different ways such as patients understanding and care, improved personalized care, analysis trends, predict health outcomes, etc. Big data analysis is required to optimize a broad set of data generated by the different mediums in the healthcare and engineering sectors. A firefly algorithm can be used to optimize data analysis and results. Therefore, the firefly algorithm becomes essential to optimize the healthcare sector application process and provides optimized solutions. This paper discussed firefly algorithm implementations, optimization solutions for an engineering problem, fireflies' algorithm (FA) performance through MATLAB 2019b, formulation, optimization problems in healthcare, firefly algorithms in other applications, and conclusions. This paper focuses on how the firefly algorithm (FA) can be used, modified, and merged with other optimization algorithms to solve engineering problems. [ABSTRACT FROM AUTHOR]

Smart contracts are seen as the major building blocks for future autonomous blockchain- and Distributed Ledger Technology (DLT)-based applications. Engineering such contracts for trustless, append-only, and decentralized digital ledgers allows mutually distrustful parties to transform legal requirements into immutable and formalized rules. Previous experience shows this to be a challenging task due to demanding socio-technical ecosystems and the specificities of decentralized ledger technology. In this paper, we therefore develop an integrated process model for engineering DLT-based smart contracts that accounts for the specificities of DLT. This model was iteratively refined with the support of industry experts. The model explicitly accounts for the immutability of the trustless, append-only, and decentralized DLT ecosystem, and thereby overcomes certain limitations of traditional software engineering process models. More specifically, it consists of five successive and closely intertwined phases: conceptualization, implementation, approval, execution, and finalization. For each phase, the respective activities, roles, and artifacts are identified and discussed in detail. Applying such a model when engineering smart contracts will help software engineers and developers to better understand and streamline the engineering process of DLTs in general and blockchain in particular. Furthermore, this model serves as a generic framework which will support application development in all fields in which DLT can be applied. [ABSTRACT FROM AUTHOR]

Ferulic acid (p-hydroxy-3-methoxycinnamic acid, FA) is a natural active substance present in plant cell walls, with antioxidant, anticancer, antithrombotic and other properties; it is widely used in medicine, food, and cosmetics. Production of FA by eco‐friendly bioprocess is of great potential. In this study, FA was biosynthesized by metabolically engineered Escherichia coli. As the first step, the genes tal (encoding tyrosine ammonia-lyase, RsTAL) from Rhodobacter sphaeroides, sam5 (encoding p-coumarate 3-hydroxylase, SeSAM5) from Saccharothrix espanaensis and comt (encoding Caffeic acid O-methytransferase, TaCM) from Triticum aestivum were cloned in an operon on the pET plasmid backbone, E. coli strain containing this construction was proved to produce FA from L-tyrosine successfully, and confirmed the function of TaCM as caffeic acid O-methytransferase. Fermentation result revealed JM109(DE3) as a more suitable host cell for FA production than BL21(DE3). After that the genes expression strength of FA pathway were optimized by tuning of promoter strength (T7 promoter or T5 promoter) and copy number (pBR322 or p15A), and the combination p15a-T5 works best. To further improve FA production, E. coli native pntAB, encoding pyridine nucleotide transhydrogenase, was selected from five NADPH regeneration genes to supplement redox cofactor NADPH for converting p-coumaric acid into caffeic acid in FA biosynthesis process. Sequentially, to further convert caffeic acid into FA, a non-native methionine kinase (MetK from Streptomyces spectabilis) was also overexpressed. Based on the flask fermentation data which show that the engineered E. coli strain produced 212 mg/L of FA with 11.8 mg/L caffeic acid residue, it could be concluded that it is the highest yield of FA achieved by E. coli K-12 strains reported to the best of our knowledge. [ABSTRACT FROM AUTHOR]

A feature line, which is a narrow and long curved surface on a smooth panel, is an essential element of the exterior style of the car. However, it is difficult to make the feature lines accurately, especially with a small radius and a large angle, in a typical automotive panel stamping process. A simple stamping process to find the minimum formable radius of the subtle feature line for automotive outer panel manufacturing was employed. This study presented three methods to find the minimum formable radius of the feature line; analytical approach, finite element analysis, and experiments. The three approaches showed similar and consistent results. In the experiment, the minimum formable radius of the subtle feature line was 20 mm, 8 mm, 7 mm, and 4 mm, when the punch angles of 175 °, 170 °, 165 °, and 160 °, respectively. This study showed that no matter how well-engineered dies and punches, there are limits to stamping the subtle feature line in automotive manufacturing. The results of this study could be used in the design process and the die engineering process of automotive panel making. [ABSTRACT FROM AUTHOR]

It is a consensus that intelligent manufacturing plants should be automated, especially in what concerns automated processes to Industry 4.0. This new manufacturing approach is based on multifunctional distributed systems that, for its turn, depend on a sound design requirements phase. Consequently, the requirement specifications became even more significant in the design of intelligent manufacturing systems, where the possibility to analyze requirements previously could save time and effort. Frequently, relevant requirements assumptions change during the engineering process due to emergent and volatile requirements—called "scope creep." A revision of the requirements approach should be demanding to minimize this problem. The proposal presented in this work anticipates the formal representation of automated systems requirements to allow the proper analysis and validation. Also, since conventional production lines have been replaced by a product-service (production) systems (PSS), there is pressure to review the requirements phase, mainly to automated systems. To increase the accuracy, we propose a requirements cycle composed of modeling, analysis, and (formal) verification where formalization is anticipated by capturing visual modeled requirements. Alternative approaches to the functional method are also explored, considering the Goal-oriented Requirements Engineering approach, the best fit to PSS. A formal process to treat requirements in a functional approach, represented by UML diagrams, is transferred to Petri Nets and submitted to formal analysis. Traditional translation from UML to Petri Nets is replaced by an object-oriented match to Petri Nets extensions covered by the standard ISO/IEC 15.909. A comparison between this approach and the goal-oriented raises a discussion about the requirements efficiency dilemma, where functionality could be pointed as the main reason to scope creep. Therefore, a strictly functional approach is proposed with a new transference algorithm, and the results are used to support the discussion about using goal-oriented alternatives. A case study of the chemical industry illustrates the practical use of the proposal. [ABSTRACT FROM AUTHOR]

Tapping is an important furnace operation in the ferroalloy industry and poses a number of complex and coupled challenges of both practical and economical importance. Owing to the hazardous high-temperature conditions surrounding the tap hole, the application of various modeling techniques allows for development and acquisition of both scientific and engineering knowledge of the process through physical or numerical proxies. In this review, earlier work on modeling of ferroalloy tapping is summarized and main principles of the tapping process and multiphase interaction of slag and metal are discussed and summarized. The main focus is on drainage of slag and alloys, but some attention will also be given to metal loss, metal overflow and health, safety and environment. Our review shows that although considerable progress has been made in computational capability over the last decades, However, it is clear that research and development in the field of ferroalloy furnace tapping remains at a relatively nascent stage. The most progress up to date has happened in the area of so called reduced-order models. Such models are robust and simple, and may be easily fitted to process data from a particular operation in order to develop tailored solutions. Such models are more easily combined with software and instruments, ultimately enabling improved automation, process control and ultimately improved tapping consistency. [ABSTRACT FROM AUTHOR]

The history of the formation and development of a new field of research in chemical technology, such as the theory of chemical process systems engineering, including methods for the analysis, optimization, and synthesis of chemical process systems (CPSs), is briefly surveyed. A classification of the modern engineering main types is given. Methods and techniques for chemical processes (CPs) and chemical process systems intensification and methods for digitalized physical and chemical engineering, the computer simulation of the texture of nanocomposites, and computer-aided chemical diagnostics in materials science are described. Methods and algorithms are given for the analysis of fractal and statistical characteristics of unsteady gas flows in complex gas pipelines. Methods and techniques for energy conservation in chemical process systems are briefly described; the basic concepts of resource and energy-saving logistics are outlined. A brief description of the principles of the computer-aided synthesis of optimal energy- and resource-efficient chemical process systems is given. The essence of digital transformation and automated management of the operation of production facilities of the chemical, petrochemical, and fuel and energy complexes is briefly outlined. The essence of the multilevel training of chemical engineers and technologists in energy-saving environmentally safe chemical process systems engineering is stated. The main topical priority fields of research in energy- and resource-efficient environmentally safe chemical process systems engineering are proposed. [ABSTRACT FROM AUTHOR]

Ethyl-acetate is important for the flavor and aroma of the alcoholic beverages, therefore, there have been extensive efforts toward increasing its production by engineering yeast strains. In this study, we reported a new approach to breed non-genetic modified producing yeast strain with higher ethyl-acetate production for beer brewing. First, we demonstrated the positive effect of higher acetic acid concentration on inducing the expression of acetyl-CoA synthetase (ACS). Then, we applied adaptive laboratory evolution method to evolve strain with higher expression level of ACS. As a result, we obtained several evolved strains with increased ACS expression level as well as ethyl-acetate production. In 3 L scale fermentation, the optimal strain EA60 synthesized more ethyl-acetate than M14 at the same time point. At the end of fermentation, the ethyl-acetate production in EA60 was 21.4% higher than M14, while the other flavor components except for acetic acid were changed in a moderate degree, indicating this strain had a bright prospect in industrial application. Moreover, this study also indicated that ACS1 played a more important role in increasing the acetic acid tolerance of yeast, while ACS2 contributed to the synthesis of cytosol acetyl-CoA, thereby facilitating the production of ethyl-acetate during fermentation. [ABSTRACT FROM AUTHOR]

History matching is an important reservoir engineering process whereby the values of uncertain attributes of a reservoir model are changed to find models that have a better chance of reproducing the performance of an actual reservoir. As a typical inverse and ill-posed problem, different combinations of reservoir uncertain attributes lead to equally well-matched models and the success of a history-matching approach is usually measured in terms of its ability to efficiently find multiple history-matched models inside the search space defined by the parameterization of the problem (multiple-matched models have a higher chance of better representing the reservoir performance forecast). While studies on history-matching approaches have produced remarkable progress over the last two decades, given the uniqueness of each reservoir's history-matching problem, no strategy is proven effective for all cases, and finding alternative, efficient, and effective history-matching methodologies is still a research challenge. In this work, we introduce a learning-from-data approach with path relinking and soft clustering to the history-matching problem. The proposed algorithm is designed to learn the patterns of input attributes that are associated with good matching quality from the set of available solutions, and has two stages that handle different types of reservoir uncertain attributes. In each stage, the algorithm evaluates the data of all-available solutions continuously and, based on the acquired information, dynamically decides what needs to be changed, where the changes shall take place, and how such changes will occur in order to generate new (and hopefully better) solutions. We validate our approach using the UNISIM-I-H benchmark, a complex synthetic case constructed with real data from the Namorado Field, Campos Basin, Brazil. Experimental results indicate the potential of the proposed approach in finding models with significantly better history-matching quality. Considering a global misfit quality metric, the final best solutions found by our approach are up to 77% better than the corresponding initial best solutions in the datasets used in the experiments. Moreover, compared with previous work for the same benchmark, the proposed learning-from-data approach is competitive regarding the quality of solutions found and, above all, it offers a significant reduction (up to 30 × less) in the number of simulations. [ABSTRACT FROM AUTHOR]

To meet lightweight demands, complex support or near-net shaped structures are increasingly applied in modern industry sectors. Wire arc additive manufacturing (WAAM) of these structures requires a profound knowledge of the interactions between material properties, welding process and mechatronic engineering. This study shows a new WAAM strategy, termed skeleton arc additive manufacturing (SAAM), adapted especially for building freeform wire-structured aluminium component, and further contributes a bead modelling study to establish welding parameters, geometric features and material properties relationship. The results show that good geometrical features and sound metallurgical properties can be achieved for wire-structure aluminium parts using the proposed approach. The strut diameter gradually increased with the amplified welding arc-on time and wire feed speed; however, they present significant changes in the microstructural evolution and mechanical strength. A sample demonstration further displays an effective path to create typically wire-structure part with good quality and offers the main processing challenge and coping strategy during SAAM. [ABSTRACT FROM AUTHOR]

Objective: Experimental procedures have been used to monitor cellular responses at the dentin/pulp interface. Aiming to divert from in vivo studies and oversimplified two-dimensional assays, three-dimensional (3D) models have been developed. This review provides an overview of existing literature, regarding 3D in vitro dentin/pulp reconstruction. Material & Methods: PubMed, Scopus, Cochrane Library and Web of Science- were systematically searched for attributes between 1998 and 2020. The search focused on articles on the development of three-dimensional tools for the reconstruction of a dentin/pulp complex under in vitro conditions, which were then screened and qualitatively assessed. Article grouping according to mode of implementation, resulted in five categories: the customised cell perfusion chamber (CPC) (n = 8), the tooth bud model (TBM) (n = 3), the 3D dentin/pulp complex manufactured by tissue engineering (DPC) (n = 6), the entire tooth culture (ETC) (n = 4) and the tooth slice culture model (TSC) (n = 5). Results: A total of 26 publications, applying nine and eight substances for pulp and dentin representation respectively, were included. Natural materials and dentin components were the most widely utilized. The most diverse category was the DPC, while the CPC group was the test with the highest longevity. The most consistent categories were the ETC and TSC models, while the TBM presented as the most complete de novo approach. Conclusions: All studies presented with experimental protocols with potential upgrades. Solving the limitations of each category will provide a complete in vitro testing and monitoring tool of dental responses to exogenous inputs. Clinical Relevance: The 3D dentin/pulp complexes are valid supplementary tools for in vivo studies and clinical testing. [ABSTRACT FROM AUTHOR]

Purpose: Traditionally, α-lactose monohydrate is the carrier of choice in dry powder inhaler (DPI) formulations. Nonetheless, other sugars, such as D-mannitol, have emerged as potential alternatives. Herein, we explored different particle engineering processes to produce D-mannitol carriers for inhaled delivery. Methods: Wet-sieving and spray-congealing were employed as innovative techniques to evaluate the impact of engineering on the particle properties of D-mannitol. To that end, the resulting powders were characterized concerning their solid-state, micromeritics and flowability. Afterwards, the engineered carrier particles were blended with inhalable size beclomethasone dipropionate to form low dose (1 wt%) DPI formulations. The in vitro aerosolization performance was evaluated using the NEXThaler®, a reservoir multi-dose device. Results: Wet-sieving generated D-mannitol particles with a narrow particle size distribution and spray-congealing free-flowing spherical particles. The more uniform pumice particles with deep voids and clefts of wet-sieved D-mannitol (Pearl300_WS) were beneficial to drug aerosolization, only when used in combination with a ternary agent (10 wt% of 'Preblend'). When compared to the starting material, the spray-congealed D-mannitol has shown to be promising in terms of the relative increase of the fine particle fraction of the drug (around 100%), when used without the addition of ternary agents. Conclusions: The wet-sieving process and the related aerosolization performance are strongly dependent on the topography and structure of the starting material. Spray-congealing, has shown to be a potential process for generating smooth spherical particles of D-mannitol that enhance the in vitro aerosolization performance in binary blends of the carrier with a low drug dose. [ABSTRACT FROM AUTHOR]

In the early 2000s, shipyards in South Korea were the most competitive in the world for commercial vessels because of their accumulated production and quality management capabilities. In the mid-2000s, the rising trend of international oil prices and predicted market growth of offshore structures caused large shipyards in South Korea to start focusing on the construction of offshore structures. However, these shipyards suffered massive losses because they did not properly reflect the characteristics of offshore structures, which differ from those of commercial vessels. Such losses can largely be classified into engineering losses and production losses. This study aimed to address problems related to outfitting installation work from a production perspective. An algorithm was developed to define an efficient spool installation sequence and was verified through actual data of offshore structure. The developed algorithm should make it possible to plan spool installation work by determining an efficient spool installation sequence. In addition, the algorithm can assist in prompt decision making and eliminate wasted cost and time by providing with an efficient spool installation sequence. [ABSTRACT FROM AUTHOR]

Implementing machine learning in an enterprise involves tackling a wide range of complexities with respect to requirements elicitation, design, development, and deployment of such solutions. Despite the necessity and relevance of requirements engineering approaches to the process, not much research has been done in this area. This paper employs a case study method to evaluate the expressiveness and usefulness of GR4ML, a conceptual modeling framework for requirements elicitation, design, and development of machine learning solutions. Our results confirm that the framework includes an adequate set of concepts for expressing machine learning requirements and solution design. The case study also demonstrates that the framework can be useful in machine learning projects by revealing new requirements that would have been missed without using the framework, as well as, by facilitating communication among project team members of different roles and backgrounds. Feedback from study participants and areas of improvement to the framework are also discussed. [ABSTRACT FROM AUTHOR]

The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management (PLM) and product, process, resource (PPR) modeling. These paradigms are complementary, and the latter could be considered a more neutral version of the former. There are two main technologies associated with these paradigms: ontologies and databases. Database technology is widespread in industry and is well established. Ontologies remain largely a plaything of the academic community which, despite numerous projects and publications, have seen limited implementations in industrial manufacturing applications. The main objective of this paper is to provide a comparison between ontologies and databases, offering both qualitative and quantitative analyses in the context of PLM and PPR. To achieve this, the article presents (1) a literature review within the context of manufacturing systems that use databases and ontologies, identifying their respective strengths and weaknesses, and (2) an implementation in a real industrial scenario that demonstrates how different modeling approaches can be used for the same purpose. This experiment is used to enable discussion and comparative analysis of both modeling strategies. [ABSTRACT FROM AUTHOR]

Recently, several socio-/bio-inspired algorithms have been proposed for solving a variety of problems. Generally, they perform well when applied for solving unconstrained problems; however, their performance degenerates when applied for solving constrained problems. Several types of penalty function approaches have been proposed so far for handling linear and non-linear constraints. Even though the approach is quite easy to understand, the precise choice of penalty parameter is very much important. It may further necessitate significant number of preliminary trials. To overcome this limitation, a new self-adaptive penalty function (SAPF) approach is proposed and incorporated into socio-inspired Cohort Intelligence (CI) algorithm. This approach is referred to as CI–SAPF. Furthermore, CI–SAPF approach is hybridized with Colliding Bodies Optimization (CBO) algorithm referred to as CI–SAPF–CBO algorithm. The performance of the CI–SAPF and CI–SAPF–CBO algorithms is validated by solving discrete and mixed variable problems from truss structure domain, design engineering domain, and several problems of linear and nonlinear in nature. Furthermore, the applicability of the proposed techniques is validated by solving two real-world applications from manufacturing engineering domain. The results obtained from CI–SAPF and CI–SAPF–CBO are promising and computationally efficient when compared with other nature inspired optimization algorithms. A non-parametric Wilcoxon's rank sum test is performed on the obtained statistical solutions to examine the significance of CI–SAPF–CBO. In addition, the effect of the penalty parameter on pseudo-objective function, penalty function and constrained violations is analyzed and discussed along with the advantages over other algorithms. [ABSTRACT FROM AUTHOR]

Some content forms, such as animation, are superior in many aspects but rarely created in UGC systems partially due to the lack of available content-producing tools. Requirements engineering of User-Generated Content project can be difficult because of the diversities of user types, user abilities and client performance resulted from heterogeneous stakeholders. Based on the joint perspective of requirement analysis and quality control, this paper aims to propose an Exploratory Factor Analysis (EFA) integrated Quality Function Deployment (QFD) method. It divides users into expert and regular users, and allows the former to directly participate in design decisions and the latter to participate in prioritization. A case study of the requirements engineering of a Web-based animation application was carried out. The results showed the applicability of the proposed method to develop and manage requirements from all stakeholders. The integration of EFA contributed to the identification of unstated requirements to some extent. Also, the House of Quality was constructed and the quality requirements were obtained during the analysis. This approach helps analysts to sort out the priorities of user and technical requirements and achieve balance between them. [ABSTRACT FROM AUTHOR]

There is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an existing design and proposing or ideally generating an optimal design using numerical optimization. In this context, efficient and robust realization of such a framework for numerical shape optimization is of prime importance. Another requirement of such a framework is modularity, such that the shape optimization can involve different physics. This requires that different physics solvers should be handled in black-box nature. The current contribution discusses the conceptualization and applications of a general framework for numerical shape optimization using the vertex morphing parametrization technique. We deal with both 2D and 3D shape optimization problems, of which 3D problems usually tend to be expensive and are candidates for special attention in terms of efficient and high-performance computing. The paper demonstrates the different aspects of the framework, together with the challenges in realizing them. Several numerical examples involving different physics and constraints are presented to show the flexibility and extendability of the framework. [ABSTRACT FROM AUTHOR]

Fixtures play a significant role in ensuring the quality of products in manufacturing engineering. The paper considers fork-type parts that have a complex spatial configuration and needs to be machined by drilling-milling-boring equipment in one setup. In fixture design, it is necessary to provide wide tool accessibility, and therefore the choice of locating surfaces of the workpiece is an important step. The mathematical approach for determining the coordinates of contact points between the workpiece and the locating and clamping elements fixtures in the coordinate system is developed in this paper. The geometric parameters of the workpiece are taken into account in determining the coordinates. It allows to apply the proposed approach for parts of another standard size, but the same design and technological features. The technology of augmented reality is an effective tool at the design stage of fixtures and allows to visualize the developed fixture configurations. This approach has been successfully applied for such parts as levers, connecting rods, flat parts. [ABSTRACT FROM AUTHOR]

Current rock mass quality classification methods include the rock mass rating (RMR), the Q system, the geological strength index (GSI) and the basic quality (BQ) system. Among these methods, the BQ system has been adopted as the standard for engineering classification of rock mass in China. The base value of the BQ system is determined by the intactness index of the rock mass (Kv) and the uniaxial compressive strength of intact rocks (UCS). However, it is not always possible to calculate Kv in engineering processes, thus making it difficult to classify a rock mass. In this study, the relationship among the Kv, the rock quality designation (RQD), and the Lugeon value (ω) is established using Maerdang hydropower station, China, as a case study. Results from this study enable the proposal of a modified BQ system based on UCS, RQD, and the Lugeon value. By comparing evaluation results obtained using the original BQ system with this proposed system, very similar results were obtained for rock mass at the Maerdang hydropower station. The addition of more engineering examples over time will enable further verification of the proposed modified BQ system. [ABSTRACT FROM AUTHOR]

Engineering design processes that are dependent on Computer-Aided Engineering (CAE) tools commonly involve complex patterns of tasks, including concurrency and intertwined iterations. There are often inefficiencies and improvement opportunities in such processes, but it can be difficult to identify and evaluate them—partly because of the complex iteration patterns, and partly because process knowledge is usually tacit and often distributed among process participants so that it is difficult to appreciate the causes and effects of iteration in an integrated way. The Applied Signposting Model (ASM) was developed to address these issues during more than a decade of research and case studies in the aerospace sector. This article describes an evolved version of the ASM, called ASM2.0, alongside a detailed case to demonstrate its application to improve CAE-driven, iteration-intensive engineering design processes. A review of applications is also provided. A differentiating feature of ASM2.0 is that it integrates visually-familiar flowchart mapping with quantitative analysis of process performance, to enable the modelling of complex iteration patterns that are typical of engineering design practice. [ABSTRACT FROM AUTHOR]

The software engineering field is continuously making an effort to improve the effectiveness of the software development process. This improvement is performed by developing quantitative measures that can be used to enhance the quality of software products and to more accurately describe, better understand and manage the software development life cycle. Even if the ontology engineering field is constantly adopting practices from software engineering, it has not yet reached a state in which metrics are an integral part of ontology engineering processes and support making evidence-based decisions over the process and its outputs. Up to now, ontology metrics are mainly focused on the ontology implementation and do not take into account the development process or other artefacts that can help assessing the quality of the ontology, e.g. its requirements. This work envisions the need for a metrics-driven ontology engineering process and, as a first step, presents a set of metrics for ontology engineering which are obtained from artefacts generated during the ontology development process and from the process itself. The approach is validated by measuring the ontology engineering process carried out in a research project and by showing how the proposed metrics can be used to improve the efficiency of the process by making predictions, such as the effort needed to implement an ontology, or assessments, such as the coverage of the ontology according to its requirements. [ABSTRACT FROM AUTHOR]

With the increasing depth of underground engineering, the risk of coal–rock dynamic disasters such as rockburst is becoming more and more serious and complex, which seriously threatens the safety of coal resource, mine production and the surface ecological environment. However, the existing risk indices and methods used for evaluating rockburst risk cannot be fully applied to deep goal seam group (DCG) mining. For the safe exploitation of coal resources, in this paper, based on statistical analyses of 300 cases of rockburst, six new indices are proposed for evaluating rockburst risk in the DCG, namely dip angle, moisture content, stability of coal seam, advancing speed of working face, disturbance factors and support patterns. In addition, the influence of multiple factors coupling and superposition on rockburst risk was considered. Thus, the Comprehensive Index Method of rockburst risk of Deep Coal seam Group (DCG–CIM) based on analytic hierarchy process was established. Finally, rockburst risk in the evaluation area was quantitatively assessed into four grades, including "No rockburst risk", "Weak rockburst risk", "Medium rockburst risk" and "Strong rockburst risk". Taking the 2233 working face of Hengda Coalmine as an example, the evaluation results show that the ranges of 0–184 m, 224–284 m, 324–384 m, 424–484 m, 524–584 m and 594–624 m from terminal line of haulage roadway on 2233 working face were the medium rockburst risk zones, which are in accordance with the on-site impact damage results and are more accurate than the traditional method. The DCG–CIM can consider more inducing factors and obtain more accurate and reliable evaluation results and is more suitable for deep coal seam group mining. [ABSTRACT FROM AUTHOR]

This study aimed to develop and assess the feasibility of different machine learning algorithms for predicting ore production in open-pit mines based on a truck-haulage system with the support of the Internet of Things (IoT). Six machine learning algorithms, namely the random forest (RF), support vector machine (SVM), multi-layer perceptron neural networks (MLP neural nets), classification and regression tree, k-nearest neighbors, and M5Tree model, were developed and investigated to estimate ore production at a limestone open-pit mine in South Korea. Systems of IoT were used to collect a massive database of 16,217 observations (big data). To process this big data, a downscaling method was applied to reduce the size of the original observations to improve the computational cost of the machine learning models. Subsequently, three validation datasets were selected from the original observations and used to evaluate (after downscaling the observations) the performance and accuracy of the machine learning models in practical engineering through various performance metrics. The results revealed that the models used can be potentially used for predicting ore production in open-pit mines. The SVM, MLP neural nets, and RF models demonstrated high accuracy, with the SVM model exhibiting the most superior performance and the highest accuracy. An assessment of the validation datasets also confirmed the feasibility and stability of the models for predicting ore production. Furthermore, a sensitivity analysis indicated that the relative operation start time, relative operation end time, and interval between the operation times were the most important input variables for improving ore production in practical engineering. [ABSTRACT FROM AUTHOR]

The aim of this article is to present an overview of impurities of alkali metals and alkaline earth elements included in smelter-grade alumina and other raw materials that primarily report to the electrolyte, i.e., bath, and to suggest pathways for improved control at primary aluminum smelters. Comments are included on how these impurities affect intermediate subprocesses, metal products, and cell condition. The authors have worked to create a reference that not only reports on the scientific aspects of how impurities react in a bath of molten salt under electrolysis but also covers the practical matters of concern regarding impurities that affect the electrolyte. By combining scientific knowledge with process engineering techniques, readers should be able to better manage impurities and related impacts in aluminum production processes. [ABSTRACT FROM AUTHOR]

Safe construction and operation of tunnels in high seismic risk regions require special aseismic design. A possible solution to this issue is to consider the earthquake damage that a tunnel may suffer beforehand in the process of engineering rock mass classification during the geotechnical investigation phase. Then, the tunnel support system is designed with this modified classification result. To verify this hypothesis, this work investigates the Chinese engineering rock mass classification system, the basic quality (BQ) rating system, considering seismic effects. The main contribution of the current work is to quantitatively modify the rock mass intactness index Kv in the BQ system considering the potential earthquake damage under various levels of design peak ground acceleration. The procedure to obtain the rock mass basic quality index considering the design peak ground acceleration is proposed and verified with real field data from the 2008 Wenchuan earthquake. This approach is a promising enhancement of the empirical methods for considering the seismic effects for tunnels that require special design against possible earthquake impacts. [ABSTRACT FROM AUTHOR]

The coexistence of single-crystallinity with a multidomain morphology is a paradoxical phenomenon occurring in biomineralization. Translating such feature to synthetic materials is a highly challenging process in crystal engineering. We demonstrate the formation of metallo-organic single-crystals with a unique appearance: six-connected half-rods forming a hexagonal-like tube. These uniform objects are formed from unstable, monodomain crystals. The monodomain crystals dissolve from the inner regions, while material is anisotropically added to their shell, resulting in hollow, single-crystals. Regardless of the different morphologies and growth mechanism, the crystallographic structures of the mono- and multidomain crystals are nearly identical. The chiral crystals are formed from achiral components, and belong to a rare space group (P622). Sonication of the solvents generating radical species is essential for forming the multidomain single-crystals. This process reduces the concentration of the active metal salt. Our approach offers opportunities to generate a new class of crystals. The coexistence of single-crystallinity with a multidomain morphology is a paradoxical crystallographic phenomenon. Here, the authors introduce a crystallographic morphology never reported before. The single-crystals with a curved and hollow morphology offer opportunities to generate a class of synthetic multidomain crystals. [ABSTRACT FROM AUTHOR]

There are different defect combinations in natural rock mass. In the process of underground engineering excavation, the existence of defect combination poses a serious threat to the construction safety. In this paper, the uniaxial compression test of red sandstone with prefabricated defects combination angles of 15°, 30°, 45°, 60° and 90° was conducted by Shimadzu universal testing machine respectively, and the crack propagation law and mechanism of red sandstone with different defects are studied, The test results showed that the red sandstone with different angle defects only produced tensile cracks, there had not shear cracks, and finally resulted in tensile failure. This was due to the existence of defects combination which weakened the local stress concentration and releases part of energy in advance. With the increasing of the defect combination angle, the weakening degree of stress concentration increased, and the uniaxial compressive strength of the specimen decreased as a whole, and with 45° as the boundary. There was an obvious strength area division, and the failure time of the specimen was obviously prolonged, furthermore, the brittle failure characteristics of specimens are obviously weakened. The test results have important guiding significance and reference value for the construction design in the field of underground engineering. [ABSTRACT FROM AUTHOR]

Objective: In this study, we aimed to maximize glutathione (GSH) production by a metabolically engineered Yarrowia lipolytica strain using a small-scale optimization approach. Results: A three levels four factorial Box–Behnken Design was used to assess the effect of pH, inulin extract, yeast extract and ammonium sulfate concentrations on cell growth and to generate a mathematical model which predict optimal conditions to maximize biomass production and thus GSH titer. The obtained results revealed that only yeast and inulin extract concentrations significantly affect biomass production. Based on the generated model, a medium composed of 10 g/L of yeast extract and 10 g/L of inulin extract from Jerusalem artichoke was used to conduct batch cultures in 2 L bioreactor. After 48 h of culture, the biomass and the glutathione titer increased by 55% (5.8 gDCW/L) and 61% (1011.4 mg/L), respectively, as compared to non-optimized conditions. Conclusion: From the obtained results, it could be observed that the model established from small scale culture (i.e. 2 mL) is able to predict performance at larger scale (i.e. 2 L bioreactor, two orders of magnitude scale-up). Moreover, the results highlight the ability of the optimized process to ensure high titer of glutathione using a low-cost carbon source. [ABSTRACT FROM AUTHOR]

At present, there is no good method or technology for quantitative evaluation of wellbore structure and engineering risk in the process of deep well drilling in complex formation, and there is no rational understanding of engineering risk mechanism. With the development of MWD and transmission technology, the formation pressure and rock mechanics parameters of the drilled section can be accurately determined in the process of drilling, which provides a basis for the well bore structure and engineering risk assessment in the process of drilling. This paper makes full use of the advantages of MWD and other technologies to seek a method to improve the accuracy of formation pressure prediction with narrow safe density window On this basis, the risk identification methods of leakage, blowout, collapse, sticking and other engineering risks in the process of drilling are studied, and the risk assessment method of dynamic wellbore structure in the process of drilling is established; at the same time, the uncertain parameters in the existing wellbore structure design method are studied, and the design method is improved, so as to minimize the risk of well structure design. [ABSTRACT FROM AUTHOR]

Background: This study focuses on probing preservice technology teachers' cognitive structures and how they construct engineering design in technology-learning activities and explores the effects of infusing an engineering design process into science, technology, engineering, and mathematics (STEM) project-based learning to develop preservice technology teachers' cognitive structures for engineering design thinking. Results: The study employed a quasi-experimental design, and twenty-eight preservice technology teachers participated in the teaching experiment. The flow-map method and metalistening technique were utilized to enable preservice technology teachers to create flow maps of engineering design, and a chi-square test was employed to analyze the data. The results suggest that (1) applying the engineering design process to STEM project-based learning is beneficial for developing preservice technology teachers' schema of design thinking, especially with respect to clarifying the problem, generating ideas, modeling, and feasibility analysis, and (2) it is important to encourage teachers to further explore the systematic concepts of engineering design thinking and expand their abilities by merging the engineering design process into STEM project-based learning. Conclusions: The findings of this study provide initial evidence on the effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers' engineering design thinking. However, further work should focus on exploring how to overcome the weaknesses of preservice technology teachers' engineering design thinking by adding a few elements of engineering design thinking pedagogy, e.g., designing learning activities that are relevant to real life. [ABSTRACT FROM AUTHOR]

The evolutionary behaviors of dislocations, sub-grain boundaries (SGBs), and precipitated carbide particles in 9Cr-1.7W-0.1C reduced activation ferritic–martensitic (RAFM) steel were studied by implementing varying levels of roll deformation and tempering processes. The microstructural parameters were analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electron backscatter diffraction (EBSD). The relationships between these parameters, the deformation levels, and different tempering processes were quantitatively determined. The obtained results demonstrate that both high-level deformation and dual tempering (medium-temperature tempering T1 followed by standard tempering T2) contribute to the refinement of M23C6/MX particles, especially M23C6, due to the greater abundance of nucleation sites for M23C6/MX particles supplied by SGBs. How the correlation among these different microstructural parameters (dislocations, SGBs, and carbide particles) influenced the decreased process of engineering strength both at room temperature (RT) and high temperature was qualitatively and quantitatively discussed. [ABSTRACT FROM AUTHOR]

The oil density at the bubble point is an important thermodynamic property required in reservoir simulation and production engineering. A higher-accuracy estimate of this property would improve the accuracy of reservoir and production engineering calculations. The bubble point oil density is obtained either from separator tests of reservoir fluids or from differential gas liberation tests. A new procedure utilizing separator and differential tests is proposed whereby the experimental data yield a unique value with high accuracy for the bubble point oil density. A consistent correction of other PVT properties, which are influenced by the bubble point oil density, is required to reflect the unique density value. A quantitative quality control index is defined to measure the quality of PVT laboratory reports. This is achieved by utilizing the unique property of the bubble point oil density, which is usually ignored. [ABSTRACT FROM AUTHOR]