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Engineering systems provide essential services to society, e.g., power generation, transportation. Their performance, however, is directly affected by their ability to cope with uncertainty, especially given the realities of climate change and pandemics. Standard design methods often fail to recognize uncertainty in early conceptual activities, leading to rigid systems that are vulnerable to change. Real options and flexibility in design are important paradigms to improve a system's ability to adapt and respond to unforeseen conditions. Existing approaches to analyze flexibility, however, do not leverage sufficiently recent developments in machine learning enabling deeper exploration of the computational design space. There is untapped potential for new solutions that are not readily accessible using existing methods. Here, a novel approach to analyze flexibility is proposed based on deep reinforcement learning (DRL). It explores available datasets systematically and considers a wider range of adaptability strategies. The methodology is evaluated on an example waste-to-energy (WTE) system. Low and high flexibility DRL models are compared against stochastically optimal inflexible and flexible solutions using decision rules. The results show highly dynamic solutions, with action space parametrized via artificial neural network (ANN). They show improved expected economic value up to 69% compared with previous solutions. Combining information from action space probability distributions along expert insights and risk tolerance helps make better decisions in real-world design and system operations. Out of sample testing shows that the policies are generalizable, but subject to tradeoffs between flexibility and inherent limitations of the learning process. [ABSTRACT FROM AUTHOR]

• The impacts of climate change on global migratory patterns must be addressed in light of their energy access needs. • Plug and Play control developments allow nomadic communities to operate in stand-alone mode or connect to the main grid. • Flexibility in Design and Deep Reinforcement Learning are implemented for their energy systems planning under uncertainty. • The proposed method outperforms baseline systems on expected cost, equivalent emissions and unmet load over 30 years. • Results have important implications for policy, design, planning, and adaptable operations of future mobile energy systems. Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced "Plug and Play" control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access. [ABSTRACT FROM AUTHOR]

Energy access and waste management are two of the most pressing developmental and environmental issues on a global level to help mitigate the accelerating impacts of climate change. They are particularly relevant in Sub–Saharan Africa where electrification rates are significantly below global averages and rural areas are lacking a formal waste management sector. This paper explores the potential of integrating solar energy into a biomass pyrolysis unit as a potentially synergetic solution to both issues. The full design of a slow pyrolysis batch reactor targeted at biochar production, following a strict cost minimization approach, is presented in light of the relevant considerations. SPEAR is powered using a Cassegrain optics parabolic dish system, integrated into the reactor via a manual tracking system and optically optimized with a Monte-Carlo ray tracing methodology. The design approach employed has led to the development an overall cost efficient system, with the potential to achieve optical efficiencies up 72% under a 1.5° tracking error. The outputs of the system are biochar and electricity, to be used for soil amendment and energy access purposes, respectively. There is potential to pyrolyze a number of agricultural waste streams for the region, producing at least 5 kg of biochar per unit per day depending on the feedstock employed. Financial assessment of SPEAR yields a positive Net Present Value (NPV) in nearly all scenarios evaluated and a reasonable competitiveness with small scale solar for electrification objectives. Finally, SPEAR presents important positive social and environmental externalities and should be feasibly implementable in the region in the near term. [ABSTRACT FROM AUTHOR]

Background. Therapy with direct-acting antivirals (DAA) for HCV is safe and effective in the liver (LT) and kidney transplant (KT) recipients; however, data on the quality of life (QoL) of patients are scanty. This pilot study is aimed at prospectively evaluating the QoL in LT and KT recipients before and after DAA treatment. Methods. We prospectively enrolled 17 LT and 11 KT recipients with HCV infection starting a sofosbuvir-based antiviral therapy for 12 weeks. All participants before (T0), 12 (T12), and 24 (T24) weeks after the end of the therapy completed the Short Form Health Survey (SF-36) questionnaire, the Zung Self-rating Depression Scale, and State-Trait Anxiety Inventory (STAI—Y1–Y2). Results. At T0, LT and KT patients were similar for gender, age, BMI, smoking habits, marital status, mean liver stiffness values at Fibroscan, and HCV genotype distribution (p > 0.05). There were no significant differences between the 2 groups in STAI-Y1, STAI-Y2, Zung, and SF-36 scores (p > 0.05). At T12, all the participants showed a sustained virological response (SVR). All items of the SF-36 questionnaire improved from the pretreatment to posttreatment period within the LT group, and the 4 domains role-physical, bodily pain, social function, role-emotional, and mental health reached statistical significance (p < 0.05 in all cases). On the contrary, in KT patients, there was no significant improvement in SF-36 mean scores compared to at baseline at T12 and T24. Conclusions. This pilot study suggested that DAA therapy is associated with a significant improvement of the QoL only in LT recipients. Probably, KT recipients did not consider HCV a "central player" in the course of their disease, and HCV eradication did not significantly impact on their QoL. [ABSTRACT FROM AUTHOR]

The relevance of isolated autoimmunity elevation in orthotopic liver transplantation (OLT) patients is unknown. Our aim was to analyse how serum autoantibodies change in time and to evaluate their clinical relevance in OLT patients. Patients were invited to provide samples to evaluate ANA, AMA, ASMA, and LKM at the time of enrolment (T0), after 6 months (T6), and after 12 months (T12). We included 114 patients in the study (76% males, median age 62.5 years), finding isolated elevation of at least one serum antibody in up to 80% of them. We described fluctuating positive autoantibodies in the one year of observation, with only 45.6% of patients positive for ANA and less than 2% positive for ASMA, at all three times. Isolated elevation of tissue antibodies was not related to gender, age, HCC at transplant, early rejection, cause of transplantation, immunotherapy taken, and age at the time of the study. We did not detect a higher prevalence of positive autoimmunity in patients with signs of liver injury. ANA and ASMA evaluation in patients with liver transplantation and no history of autoimmune disease has no clinical relevance, since it varies in time and is not related to any risk factors or liver injury. Routine autoimmunity evaluation should be avoided. [ABSTRACT FROM AUTHOR]