Integrated Battery Chargers

Integrated battery chargers (IBCs) have been proposed as a low-weight, low-volume, and high-power solution to conventional conductive chargers. However, the design of such chargers is complicated, requiring special components or control techniques to solve inherent issues (such as galvanic isolation, torque generation, and system reconfiguration) associated with their design. Solutions vary based on charging power, drive topology, and motor technology. This chapter introduces designs for IBCs, including solutions as proposed in the literature. It also presents challenges in their industrial adoption. Finally, the chapter presents opportunities for fleet charging applications using IBCs.

Application of computational approach in plastic pyrolysis kinetic modelling: a review

During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

Data management in microservices

Microservices have become a popular architectural style for data-driven applications, given their ability to functionally decompose an application into small and autonomous services to achieve scalability, strong isolation, and specialization of database systems to the workloads and data formats of each service. Despite the accelerating industrial adoption of this architectural style, an investigation of the state of the practice and challenges practitioners face regarding data management in microservices is lacking. To bridge this gap, we conducted a systematic literature review of representative articles reporting the adoption of microservices, we analyzed a set of popular open-source microservice applications, and we conducted an online survey to cross-validate the findings of the previous steps with the perceptions and experiences of over 120 experienced practitioners and researchers. Through this process, we were able to categorize the state of practice of data management in microservices and observe several foundational challenges that cannot be solved by software engineering practices alone, but rather require system-level support to alleviate the burden imposed on practitioners. We discuss the shortcomings of state-of-the-art database systems regarding microservices and we conclude by devising a set of features for microservice-oriented database systems.

FORMAL MODELING AND ANALYSIS OF ENTITY FRAMEWORK USING ALLOY

Formal methods provide multiple benefits when applied in the software development process. For instance, they enable engineers to verify and validate models before working on their implementation, leading to earlier detection of design defects. However, most of them lack flexibility to be applied in agile software development projects. Alloy is a lightweight formal modeling language with a friendly tool that facilitates the agile approaches application. Unfortunately, its industrial adoption is hampered by the lack of methods and tools for current software development frameworks, such as Entity Framework. This platform is usually chosen by agile projects following the code-first approach that allows automatic generation of a database from domain classes coded in the C# language. We present a new method and tool for the formal specification and analysis of Entity Framework projects with Alloy. The proposal allows engineers to start the software development using Alloy for modeling, validation and verification, automatically translate Alloy specifications to C# domain classes and then generate the corresponding database with Entity Framework. We validate our approach with a real case study: an application required by a gas supplier company.

Software engineering techniques for statically analyzing mobile apps: research trends, characteristics, and potential for industrial adoption

Mobile platforms are rapidly and continuously changing, with support for new sensors, APIs, and programming abstractions. Static analysis is gaining a growing interest, allowing developers to predict properties about the run-time behavior of mobile apps without executing them. Over the years, literally hundreds of static analysis techniques have been proposed, ranging from structural and control-flow analysis to state-based analysis.In this paper, we present a systematic mapping study aimed at identifying, evaluating and classifying characteristics, trends and potential for industrial adoption of existing research in static analysis of mobile apps. Starting from over 12,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 261 primary studies along a time span of 9 years. We analyzed each primary study according to a rigorously-defined classification framework. The results of this study give a solid foundation for assessing existing and future approaches for static analysis of mobile apps, especially in terms of their industrial adoptability.Researchers and practitioners can use the results of this study to (i) identify existing research/technical gaps to target, (ii) understand how approaches developed in academia can be successfully transferred to industry, and (iii) better position their (past and future) approaches for static analysis of mobile apps.

Machine Learning for Additive Manufacturing

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

Toward polymer upcycling—adding value and tackling circularity

Plastics have revolutionized modern life, but have created a global waste crisis driven by our reliance and demand for low-cost, disposable materials. New approaches are vital to address challenges related to plastics waste heterogeneity, along with the property reductions induced by mechanical recycling. Chemical recycling and upcycling of polymers may enable circularity through separation strategies, chemistries that promote closed-loop recycling inherent to macromolecular design, and transformative processes that shift the life-cycle landscape. Polymer upcycling schemes may enable lower-energy pathways and minimal environmental impacts compared with traditional mechanical and chemical recycling. The emergence of industrial adoption of recycling and upcycling approaches is encouraging, solidifying the critical role for these strategies in addressing the fate of plastics and driving advances in next-generation materials design.

Preliminary Examination of the Toxicity of Spalting Fungal Pigments: A Comparison between Extraction Methods

Spalting fungal pigments have shown potential in technologies ranging from green energy generation to natural colorants. However, their unknown toxicity has been a barrier to industrial adoption. In order to gain an understanding of the safety of the pigments, zebrafish embryos were exposed to multiple forms of liquid media and solvent-extracted pigments with concentrations of purified pigment ranging from 0 to 50 mM from Chlorociboria aeruginosa, Chlorociboria aeruginascens, and Scytalidium cuboideum. Purified xylindein from Chlorociboria sp. did not show toxicity at any tested concentration, while the red pigment dramada from S. cuboideum was only associated with significant toxicity above 23.2 uM. However, liquid cultures and pigment extracted into dichloromethane (DCM) showed toxicity, suggesting the co-production of bioactive secondary metabolites. Future research on purification and the bioavailability of the red dramada pigment will be important to identify appropriate use; however, purified forms of the blue-green pigment xylindein are likely safe for use across industries. This opens the door to the adoption of green technologies based on these pigments, with potential to replace synthetic colorants and less stable natural pigments.

Microbial Electrolysis Cells for Decentralised Wastewater Treatment: The Next Steps

Traditional wastewater treatment methods have become aged and inefficient, meaning alternative methods are essential to protect the environment and ensure water and energy security worldwide. The use of microbial electrolysis cells (MEC) for wastewater treatment provides an innovative alternative, working towards circular wastewater treatment for energy production. This study evaluates the factors hindering industrial adoption of this technology and proposes the next steps for further research and development. Existing pilot-scale investigations are studied to critically assess the main limitations, focusing on the electrode material, feedstock, system design and inoculation and what steps need to be taken for industrial adoption of the technology. It was found that high strength influents lead to an increase in energy production, improving economic viability; however, large variations in waste streams indicated that a homogenous solution to wastewater treatment is unlikely with changes to the MEC system specific to different waste streams. The current capital cost of implementing MECs is high and reducing the cost of the electrodes should be a priority. Previous pilot-scale studies have predominantly used carbon-based materials. Significant reductions in relative performance are observed when electrodes increase in size. Inoculation time was found to be a significant barrier to quick operational performance. Economic analysis of the technology indicated that MECs offer an attractive option for wastewater treatment, namely greater energy production and improved treatment efficiency. However, a significant reduction in capital cost is necessary to make this economically viable. MEC based systems should offer improvements in system reliability, reduced downtime, improved treatment rates and improved energy return. Discussion of the merits of H2 or CH4 production indicates that an initial focus on methane production could provide a stepping-stone in the adoption of this technology while the hydrogen market matures.

Roughness Parameters for Classification of As-Built AM Surfaces

One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.