Hasil untuk "Engineering"

E. Place, N. D. Evans, M. Stevens

Harris H. Wang, Farren J. Isaacs, P. Carr et al.

S. Easterbrook, J. Singer, M. Storey et al.

B. Chan, K. Leong

R. Vehring

This review covers recent developments in the area of particle engineering via spray drying. The last decade has seen a shift from empirical formulation efforts to an engineering approach based on a better understanding of particle formation in the spray drying process. Microparticles with nanoscale substructures can now be designed and their functionality has contributed significantly to stability and efficacy of the particulate dosage form. The review provides concepts and a theoretical framework for particle design calculations. It reviews experimental research into parameters that influence particle formation. A classification based on dimensionless numbers is presented that can be used to estimate how excipient properties in combination with process parameters influence the morphology of the engineered particles. A wide range of pharmaceutical application examples—low density particles, composite particles, microencapsulation, and glass stabilization—is discussed, with specific emphasis on the underlying particle formation mechanisms and design concepts.

J. Bronzino

Physiological systems bioelectric phenomena biomechanics biomaterials biosensors biomedical signal analysis imaging medical instruments and devices biological effects of non-ionizing biotechnology tissue engineering human performance engineering physiological modelling, simulation and control clinical engineering and artificial intelligence. (Part contents).

A. Salgado, O. Coutinho, R. Reis

Although several major progresses have been introduced in the field of bone regenerative medicine during the years, current therapies, such as bone grafts, still have many limitations. Moreover, and in spite of the fact that material science technology has resulted in clear improvements in the field of bone substitution medicine, no adequate bone substitute has been developed and hence large bone defects/injuries still represent a major challenge for orthopaedic and reconstructive surgeons. It is in this context that TE has been emerging as a valid approach to the current therapies for bone regeneration/substitution. In contrast to classic biomaterial approach, TE is based on the understanding of tissue formation and regeneration, and aims to induce new functional tissues, rather than just to implant new spare parts. The present review pretends to give an exhaustive overview on all components needed for making bone tissue engineering a successful therapy. It begins by giving the reader a brief background on bone biology, followed by an exhaustive description of all the relevant components on bone TE, going from materials to scaffolds and from cells to tissue engineering strategies, that will lead to "engineered" bone. Scaffolds processed by using a methodology based on extrusion with blowing agents.

Fang Yang, R. Murugan, Shu Wang et al.

V. Martin, D. Pitera, Sydnor T. Withers et al.

J. Beck, K. Arnold

E. Ziegel

L. Feisel, A. Rosa

Heungsoo Shin, S. Jo, A. Mikos

B. Kitchenham, D. Budgen, P. Brereton

William J. Martin, Federica Sarro, Yue Jia et al.

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges.

PhD Amy E. Keating, J. Stein, PhD Eliezer Calo et al.

O. Kammouh, P. Gardoni, G. Cimellaro

Abstract Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.

Tian Ma, B. Shi, Ziling Ye et al.

Saccharomyces cerevisiae is an efficient host for natural-compound production and preferentially employed in academic studies and bioindustries. However, S. cerevisiae exhibits limited production capacity for lipophilic natural products, especially compounds that accumulate intracellularly, such as polyketides and carotenoids, with some engineered compounds displaying cytotoxicity. In this study, we used a nature-inspired strategy to establish an effective platform to improve lipid oil-triacylglycerol (TAG) metabolism and enable increased lycopene accumulation. Through systematic traditional engineering methods, we achieved relatively high-level production at 56.2 mg lycopene/g cell dry weight (cdw). To focus on TAG metabolism in order to increase lycopene accumulation, we overexpressed key genes associated with fatty acid synthesis and TAG production, followed by modulation of TAG fatty acyl composition by overexpressing a fatty acid desaturase (OLE1) and deletion of Seipin (FLD1), which regulates lipid-droplet size. Results showed that the engineered strain produced 70.5 mg lycopene/g cdw, a 25% increase relative to the original high-yield strain, with lycopene production reaching 2.37 g/L and 73.3 mg/g cdw in fed-batch fermentation and representing the highest lycopene yield in S. cerevisiae reported to date. These findings offer an effective strategy for extended systematic metabolic engineering through lipid engineering.

Christopher E. Lawson, J. M. Martí, Tijana Radivojević et al.

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser et al.

Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.