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What’s in a word? Modelling British History for a ‘Multi-racial’ Society
Get PDFIn March 2022 the United Kingdom (UK) government published Inclusive Britain: the government’s response to the Commission on Race and Ethnic Disparities. This accepts the ‘bad apple’ understanding of racism but is incurious as to the historical context and existing power relations shaping racist attitudes, thereby creating a tension with its stated aim of developing a model history curriculum. This article will address two, key issues resulting from this tension: Firstly, it unpicks Inclusive Britain’s handling of race and, secondly, adopts a decolonial standpoint to critique its recommendation on how to make the school history curriculum more inclusive. The article concludes that Inclusive Britain’s vision of the UK as ‘multi-racial’ serves to re-establish racial categories as an unquestioned and unproblematic series of fixed, reified identities, without acknowledging the hierarchies and uneven power relations inherent in racial terminology
An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster
Get PDFThe integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers’ behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5 and 8.78, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers
Regulating eEF2 and eEF2K in skeletal muscle by exercise
Get PDFSkeletal muscle is a flexible and adaptable tissue that strongly responds to exercise training. The skeletal muscle responds to exercise by increasing muscle protein synthesis (MPS) when energy is available. One of protein synthesis’s major rate-limiting and critical regulatory steps is the translation elongation pathway. The process of translation elongation in skeletal muscle is highly regulated. It requires elongation factors that are intensely affected by various physiological stimuli such as exercise and the total available energy of cells. Studies have shown that exercise involves the elongation pathway by numerous signalling pathways. Since the elongation pathway, has been far less studied than the other translation steps, its comprehensive prospect and quantitative understanding remain in the dark. This study highlights the current understanding of the effect of exercise training on the translation elongation pathway focussing on the molecular factors affecting the pathway, including Ca2+, AMPK, PKA, mTORC1/P70S6K, MAPKs, and myostatin. We further discussed the mode and volume of exercise training intervention on the translation elongation pathway
Critical considerations on tDCS‐mediated changes in corticospinal response to fatiguing exercise
Get PDFTechno-economic-environmental assessment and performance comparison of a building distributed multi-energy system under various operation strategies
Get PDFThe distributed energy system (DES) is a promising technology that could enable decarbonization in the building sector. Comprehensive DES system assessment from a holistic perspective is crucial for system design, operation strategy selection, and performance optimization. This paper proposes a techno-economic-environmental integrated assessment model for comprehensive system evaluation. The DES configuration mainly includes a photovoltaic panel, ground source heat pump, gas turbine, absorption heat pump, and thermal storage tank. The system is simulated under three operation strategies with MATLAB/Simulink, which are following thermal load (FTL), following electric load (FEL), and following electric load with thermal storage (FELTS). Entropy-TOPSIS method is used to evaluate the DES's techno-economic-environmental performance under various operation strategies. The results indicate that the DES' primary energy efficiency ratio under the three operation strategies of FTL, FEL and FELTS are 51.49%, 86.78%, and 125.69%, respectively. The dynamic annual values are 1.05 × 10 6 CNY, 7.23 × 10 5 CNY, and 5.94 × 10 5 CNY, respectively. The total greenhouse gas emissions are 36.2 kgCO2eq/( m 2 ∙ a ) , 22.8 kgCO2eq/( m 2 ∙ a ) , and 16.4 kgCO2eq/( m 2 ∙ a ) , respectively. The entropy-TOPSIS analysis results showed that under FELTS operation strategy, DES performs the best; it has the best indicators for technical and environmental evaluation
A cost-effective and ecological stochastic optimization for integration of distributed energy resources in energy networks considering vehicle-to-grid and combined heat and power technologies
Get PDFElectric vehicles (EVs) have the potential to decarbonize the transport sector and contribute to the attainment of the global Net-Zero goal. However, to achieve sustainable decarbonization, EVs’ power for grid-to-vehicle (G2V) operations should be sourced from carbon-free or low carbon power generating sources. Whilst the adoption of renewable energy sources (RES) in EVs’ G2V process has been extensively explored, combined heat and power (CHP) technologies are underexamined. Hence, this paper deploys harmonized natural gas and fuel cell CHP technologies alongside RES and battery energy storage systems (BESS) to facilitate EVs’ G2V and vehicle-to-grid (V2G) operations. While the BESS supports V2G operations and stores excess power from the CHP and RES, the CHP's by-product heat could be employed in heating homes and industrial facilities. Furthermore, to maximize environmental and economic benefits, the CHP technologies are designed following the hybrid electric-thermal load strategy, such that the system autonomously switches between following the electric load strategy and following the thermal load strategy. The proposed optimization problem is tested using three different case studies (CSs) to minimize the microgrid's (MG) operating costs and carbon dioxide (CO2) emissions in a stochastic framework considering the RES generations, the load consumption, and the behaviour patterns of charging/discharging periods of EVs as the uncertain parameters. The first CS tests the proposed algorithm using only CHP technologies. Secondly, the algorithm is examined using the CHP technologies and RES. Finally, the BESS is added to support and analyse the impacts of the V2G operations of EVs on the MG. Furthermore, the life cycle assessment is investigated to analyse the CO2 emissions of distributed generations. The results show a 32.22%, 44.49%, and 47.20% operating cost reduction in the first, second, and third CSs. At the same time, the CO2 emissions declined by 29.13%, 47.13% and 47.90% in the various corresponding CSs. These results demonstrate the economic and environmental benefits of applying CHP with RES in facilitating G2V and V2G operations towards achieving a decarbonized transport sector
Challenges and Opportunities for UK Seaports Toward Future Sustainability: The UK’s North East Smart Ports Testbed Case Study
No full textGlobalization and technological advancements in the transport sector have entailed a significant expansion of the maritime and offshore energy industry, while redefining the role of shipping and ports in global and national supply chains. Ports (often urban based) also play a significant role in their hinterland, through on-land business operations including logistics, renewable energy generation, and decommissioning/recycling activities. However, the ports and maritime sector have been identified as lagging behind other fields in terms of both a digital transformation strategy (to monitor, manage, and support decision-making and investment) and their readiness to accommodate technological innovation and sustainable development (especially vis-a-vis carbon emissions reductions) at a time of increasingly stringent environmental requirements in the domain of sustainability and energy efficiency
Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation
Get PDFMotion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using stateof- the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. We perform experiments to validate our framework and we conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian’s crossing
Inclusive recovery planning for incremental systemic change: A methodology, early outcomes, and limitations from the Falkland Islands' Covid‐19 recovery planning experience
Get PDFCrises do not affect populations equally but expose and exacerbate long-standing vulnerabilities and inequalities. Recovery language such as ‘build back better’, or ‘bounce forward’ has been criticised for neglecting underlying inequalities. This paper reports on the process and early outcomes of an inclusive Community Recovery Planning process for the Falkland Islands, in response to Covid-19. The Falkland Islands is home to a complex community, with close ties and short power distances (due to its small size and remoteness), with differences institutionalised in citizenship statuses and entitlements, and shaped by geopolitical tensions. We aimed to use the ‘pandemic as a portal’, seeking out previously ‘less heard’ voices, to make visible previously hidden impacts, and initiate incremental systemic change to tackle them. Community Impact Assessments evidenced specific areas of vulnerability (e.g., housing and income insecurity) and inequalities, largely shaped by differing citizenship status. In tandem with other government currents, the Community Recovery Planning process has contributed to progressive policy changes in Equalities legislation and Income Support. We offer this paper as a demonstration of our methodology for inclusive recovery planning that could be adapted elsewhere. We argue that the inclusion of previously unheard voices contributed to incremental systemic change to reduce inequalities
Undertaking the personal tutoring role with sports students at a United Kingdom university
Get PDFPersonal tutoring is renowned for the positive role it can play in supporting student satisfaction, engagement and attainment outcomes in higher education. Surprisingly though, few studies have specifically investigated the demands of this role from the perspective of the personal tutor. Through the theoretical lens of Role Theory, this study explored university tutors’ experiences of their personal tutoring role within a sport educational setting at a United Kingdom university. All data was collected through face-to-face semi structured qualitative interviews and analysed using thematic analysis. Key findings were the negative impact of personal tutoring on participants role multiplicity, intra-role accumulation and role identity. Most participants viewed the role as being time consuming, emotionally challenging and one they would prefer not to undertake (role multiplicity), feeling under qualified and ill-equipped in assisting their tutees because of the increasingly serious and complex nature of non-academic related issues presented (intra-role accumulation). Several lacked confidence and interest in the role, finding it to be stressful and instead favouring greater research responsibilities within their workloads (role identity). The collective findings provide academic colleagues and senior university management teams with evidence to inform future institutional policies and practices. This will help ensure personal tutors working across multiple disciplines and academic levels fully understand what the role is, the demands they are likely to encounter, the continued professional development required to facilitate and support the role and how the role should be better recognised in academic promotion criteria. Study limitations and future research avenues are discussed