Showing papers by "Cranfield University published in 2020"

Chemistry, Structures, and Advanced Applications of Nanocomposites from Biorenewable Resources.

Abstract: Researchers have recently focused on the advancement of new materials from biorenewable and sustainable sources because of great concerns about the environment, waste accumulation and destruction, and the inevitable depletion of fossil resources. Biorenewable materials have been extensively used as a matrix or reinforcement in many applications. In the development of innovative methods and materials, composites offer important advantages because of their excellent properties such as ease of fabrication, higher mechanical properties, high thermal stability, and many more. Especially, nanocomposites (obtained by using biorenewable sources) have significant advantages when compared to conventional composites. Nanocomposites have been utilized in many applications including food, biomedical, electroanalysis, energy storage, wastewater treatment, automotive, etc. This comprehensive review provides chemistry, structures, advanced applications, and recent developments about nanocomposites obtained from biorenewable sources.

Recent advances in carbon dioxide utilization

Abstract: Carbon dioxide (CO2) is the major contributor to greenhouse gas (GHG) emissions and the main driver of climate change. Currently, CO2 utilization is increasingly attracting interest in processes like enhanced oil recovery and coal bed methane and it has the potential to be used in hydraulic fracturing processes, among others. In this review, the latest developments in CO2 capture, utilization, conversion, and sequestration are examined through a multi-scale perspective. The diverse range of CO2 utilization applications, including mineralization, biological utilization, food and beverages, energy storage media, and chemicals, is comprehensively presented. We also discuss the worldwide research and development of CO2 utilization projects. Lastly, we examine the key challenges and issues that must be faced for pilot-scale and industrial applications in the future. This study demonstrates that CO2 utilization can be a driver for the future development of carbon capture and utilization technologies. However, considering the amount of CO2 produced globally, even if it can be reduced in the near-to mid-term future, carbon capture and storage will remain the primary strategy and, so, complementary strategies are desirable. Currently, the main CO2 utilization industry is enhanced oil and gas recovery, but considering the carbon life cycle, these processes still add CO2 to the atmosphere. In order to implement other CO2 utilization technologies at a large scale, in addition to their current technical feasibility, their economic and societal viability is critical. Therefore, future efforts should be directed toward reduction of energy penalties and costs, and the introduction of policies and regulation encouraging carbon capture, utilization and storage, and increasing the public acceptance of the strategies in a complementary manner.

Opportunities and Challenges for Biosensors and Nanoscale Analytical Tools for Pandemics: COVID-19.

Abstract: Biosensors and nanoscale analytical tools have shown huge growth in literature in the past 20 years, with a large number of reports on the topic of 'ultrasensitive', 'cost-effective', and 'early detection' tools with a potential of 'mass-production' cited on the web of science Yet none of these tools are commercially available in the market or practically viable for mass production and use in pandemic diseases such as coronavirus disease 2019 (COVID-19) In this context, we review the technological challenges and opportunities of current bio/chemical sensors and analytical tools by critically analyzing the bottlenecks which have hindered the implementation of advanced sensing technologies in pandemic diseases We also describe in brief COVID-19 by comparing it with other pandemic strains such as that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for the identification of features that enable biosensing Moreover, we discuss visualization and characterization tools that can potentially be used not only for sensing applications but also to assist in speeding up the drug discovery and vaccine development process Furthermore, we discuss the emerging monitoring mechanism, namely wastewater-based epidemiology, for early warning of the outbreak, focusing on sensors for rapid and on-site analysis of SARS-CoV2 in sewage To conclude, we provide holistic insights into challenges associated with the quick translation of sensing technologies, policies, ethical issues, technology adoption, and an overall outlook of the role of the sensing technologies in pandemics

Can a Paper-Based Device Trace COVID-19 Sources with Wastewater-Based Epidemiology?

Abstract: A recent outbreak of novel coronavirus pneumonia (COVID-19) caused by SARS-CoV-2 infection has spread rapidly around the globe, with cases now confirmed in 130 countries worldwide. Although public health authorities are racing to contain the spread of COVID-19 around the world, the situation is still grim. About 158 111 confirmed cases and 5946 cumulative deaths (81 059 confirmed cases and 3204 cumulative deaths from China) have been reported around the globe as of March 15, 2020. Some clinical cases have found that some carriers of the virus may be asymptomatic, with no fever, and no, or only slight symptoms of infection. Without the ability to screen these asymptomatic patients quickly and effectively, these unsuspecting carriers have the potential to increase the risk of disease transmission if no early effective quarantine measures are implemented. Therefore, to trace unknown COVID-19 sources, fast and accurate screening of potential virus carriers and diagnosis of asymptomatic patients is a crucial step for intervention and prevention at the early stage. It remains a highly challenging logistical exercise for medical professionals to practically and effectively screen suspected infectious cases from individual households. Such a massive undertaking is time-consuming and labor intensive and is constrained by the availability of testing technologies at this extremely critical time. However, an alternative method utilizing wastewater-based epidemiology (WBE), may provide an effective approach to predict the potential spread of the infection by testing for infectious agents in wastewater, which has been approved as an effective way to trace illicit drugs, and obtain information on health, disease, and pathogens. Faeces and urine from disease carriers in the community will contain many biomarkers that can enter the sewer system. A recent study demonstrated that live SARS-CoV-2 was isolated from the faeces and urine of infected people, which would then enter the wastewater treatment system. A further study has shown that SARS-CoV-2 can typically survive for up to several days in an appropriate environment after exiting the human body. There is potential, therefore, that the analysis of SARS-CoV-2 in community wastewater could trace COVID-19 sources through sewage pipe networks and determine whether there are potential SARS-CoV-2 carriers in certain local areas. If SARS-CoV-2 can be monitored in the community at the early stage through WBE, effective intervention can be taken as early as possible to restrict the movements of that local population, working to minimize the pathogen spread and threat to public health. Using a WBE approach in developing an early warning system and consequent effective intervention system will require a rapid analytical method for the on-site detection of viruses at the wastewater collection point. Currently, the most direct method for the detection of SARS-CoV-2 is a nucleic acid−based polymerase chain reaction(PCR) assay, which is also a means for confirmation of COVID-19 patients throughout China. Although PCR has high sensitivity and specificity, requirements for complicated sample handling in the laboratory, skilled personnel, and a long period of data processing and analysis (4−6 h) are not conducive to real-time and effective monitoring of samples on location. Therefore, it is critical to develop efficient transportable and robust analytical tools to accurately and quickly trace low-level

Carbon-Based Polymer Nanocomposite for High-Performance Energy Storage Applications.

Abstract: In recent years, numerous discoveries and investigations have been remarked for the development of carbon-based polymer nanocomposites. Carbon-based materials and their composites hold encouraging employment in a broad array of fields, for example, energy storage devices, fuel cells, membranes sensors, actuators, and electromagnetic shielding. Carbon and its derivatives exhibit some remarkable features such as high conductivity, high surface area, excellent chemical endurance, and good mechanical durability. On the other hand, characteristics such as docility, lower price, and high environmental resistance are some of the unique properties of conducting polymers (CPs). To enhance the properties and performance, polymeric electrode materials can be modified suitably by metal oxides and carbon materials resulting in a composite that helps in the collection and accumulation of charges due to large surface area. The carbon-polymer nanocomposites assist in overcoming the difficulties arising in achieving the high performance of polymeric compounds and deliver high-performance composites that can be used in electrochemical energy storage devices. Carbon-based polymer nanocomposites have both advantages and disadvantages, so in this review, attempts are made to understand their synergistic behavior and resulting performance. The three electrochemical energy storage systems and the type of electrode materials used for them have been studied here in this article and some aspects for example morphology, exterior area, temperature, and approaches have been observed to influence the activity of electrochemical methods. This review article evaluates and compiles reported data to present a significant and extensive summary of the state of the art.

Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms

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Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine

Abstract: As 5G mobile networks are bringing about global societal benefits, the design phase for 6G has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery simultaneously for mass autonomy, human machine interfacing, and targeted healthcare. Trust will become increasingly critical for 6G as it manages a wide range of mission-critical services. As we migrate from traditional mathematical model-dependent optimization to data-dependent deep learning, the insight and trust we have in our optimization modules decrease. This loss of model explainability means we are vulnerable to malicious data, poor neural network design, and the loss of trust from stakeholders and the general public -- all with a range of legal implications. In this review, we outline the core methods of explainable artificial intelligence (XAI) in a wireless network setting, including public and legal motivations, definitions of explainability, performance vs. explainability trade-offs, and XAI algorithms. Our review is grounded in case studies for both wireless PHY and MAC layer optimization and provide the community with an important research area to embark upon.

Managing climate change risks in global supply chains: a review and research agenda

Abstract: Climate change is forcing governments and businesses to explore mitigation strategies to avoid future catastrophe. There is an urgent need to manage climate change risks in global supply chains. Fo...

An ensemble deep learning approach for driver lane change intention inference

Abstract: With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

Expertise in research integration and implementation for tackling complex problems: when is it needed, where can it be found and how can it be strengthened?

Abstract: Expertise in research integration and implementation is an essential but often overlooked component of tackling complex societal and environmental problems. We focus on expertise relevant to any complex problem, especially contributory expertise, divided into ‘knowing-that’ and ‘knowing-how.’ We also deal with interactional expertise and the fact that much expertise is tacit. We explore three questions. First, in examining ‘when is expertise in research integration and implementation required?,’ we review tasks essential (a) to developing more comprehensive understandings of complex problems, plus possible ways to address them, and (b) for supporting implementation of those understandings into government policy, community practice, business and social innovation, or other initiatives. Second, in considering ‘where can expertise in research integration and implementation currently be found?,’ we describe three realms: (a) specific approaches, including interdisciplinarity, transdisciplinarity, systems thinking and sustainability science; (b) case-based experience that is independent of these specific approaches; and (c) research examining elements of integration and implementation, specifically considering unknowns and fostering innovation. We highlight examples of expertise in each realm and demonstrate how fragmentation currently precludes clear identification of research integration and implementation expertise. Third, in exploring ‘what is required to strengthen expertise in research integration and implementation?,’ we propose building a knowledge bank. We delve into three key challenges: compiling existing expertise, indexing and organising the expertise to make it widely accessible, and understanding and overcoming the core reasons for the existing fragmentation. A growing knowledge bank of expertise in research integration and implementation on the one hand, and accumulating success in addressing complex societal and environmental problems on the other, will form a virtuous cycle so that each strengthens the other. Building a coalition of researchers and institutions will ensure this expertise and its application are valued and sustained.

Bioactive Compounds of Edible Fruits with Their Anti-Aging Properties: A Comprehensive Review to Prolong Human Life.

Abstract: Aging is a complicated biological process in which functional and structural alterations in a living organism take place over time. Reactive oxygen species is one of the main factors responsible for aging and is associated with several chronic pathologies. The relationship between aging and diet is quite interesting and has attained worldwide attention. Healthy food, in addition to dietary antioxidants, are required to delay the process of aging and improve the quality of life. Many healthy foods such as fruits are a good source of dietary nutrients and natural bioactive compounds which have antioxidant properties and are involved in preventing aging and other age-related disorders. Health benefits linked with healthy consumption of fruit have drawn increased interest. A significant number of studies have documented the advantages of fruit intake, as it suppresses free-radical development that further reduces the oxidative stress created in the body and protects against several types of diseases such as cancer, type 2 diabetes, inflammatory disorders, and other cardiovascular diseases that ultimately prevent aging. In addition, fruits have numerous other properties like anti-inflammatory, anti-cancerous, anti-diabetic, neuroprotective, and have health-promoting effects. Mechanisms of various bioactive compounds that aids in preventing various diseases and increases longevity are also described. This manuscript provides a summary of various bioactive components present in fruits along with their health-promoting and antiaging properties.

Real-Time Reentry Trajectory Planning of Hypersonic Vehicles: A Two-Step Strategy Incorporating Fuzzy Multiobjective Transcription and Deep Neural Network

Abstract: A two-step strategy is developed for real-time trajectory planning of a hypersonic vehicle (HV) in the reentry phase. The first step generates the optimal trajectory for the HV using a recently proposed fuzzy multiobjective transcription method. In the second step, the optimally generated trajectories are utilized to train a deep neural network (DNN), which is then acted as the optimal command generator in real time. A detailed simulation study is carried out to verify the effectiveness and real-time applicability of the proposed integrated design. The DNN-driven controller is further compared against other optimization-based techniques existing in relative works. Moreover, extension works on the real-time trajectory planning of a six-degree-of-freedom HV model are performed. The results confirm the feasibility and reliability of applying the proposed method for the planning of the HV entry flight path in real time.