Search

Proton exchange membrane fuel cells (PEMFC) possess features like high specific power density, low operating temperature, and low operating pressure and thus are most widely used. The performance of PEMFC highly depends on its output voltage which further affects its efficiency. This research paper aims to improve this output voltage by minimizing the losses through parameter estimation technique for three well-known commercial PEMFC stacks, namely, 250W, BCS 500W, and Ballard Mark V. The multiobjective function framed for optimization serves two goals. One is to extract the unknown parameters of FC, and second is to achieve the minimum sum of squared errors (SSE) that occur due to the difference between experimental voltage and estimated voltage. Chaotic teaching-learning-based optimization (CTLBO) algorithm is used for optimization process. SSE values obtained for three commercial PEMFC stacks 250W, BCS 500W, and Ballard Mark V are 7.02267, 4.00150, and 0.8100, respectively. The applicability of this technique is further checked for different operating conditions of each model. The I-P (current-power) curve and I-V (current-voltage) curve obtained using estimated data closely matched the experimental data that showed the practical relevance and efficacy of the algorithm. A comparison is done among the SSE results obtained using CTLBO to other competitive algorithms mentioned in the literature. CTLBO showed its superiority over other algorithms in estimating the unknown parameters of PEMFC stack parameters., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Telephone and video appointments are still common post-pandemic, with an estimated 25%-50% of kidney appointments in the United Kingdom still conducted remotely. This is important as remote consultations may exacerbate pre-existing inequalities in those from underserved groups. Those from underserved groups are often not represented in health research and include those with learning disability, mental health needs, hearing/sight problems, young/older people, those from ethnic minority groups., Objectives: The aim was to develop a Toolkit to improve the quality of remote kidney care appointments for people from different underserved groups., Design: A parallel mixed methods approach with semistructured interviews/focus groups and survey. We also conducted workshops to develop and validate the Toolkit., Participants: Seventy-five renal staff members completed the survey and 21 patients participated in the interviews and focus groups. Patients (n = 11) and staff (n = 10) took part in the Toolkit development workshop, and patients (n = 13) took part in the Toolkit validation workshop., Results: Four themes from interviews/focus groups suggested areas in which remote appointments could be improved. Themes were quality of appointment, patient empowerment, patient-practitioner relationship and unique needs for underserved groups. Staff reported difficulty building rapport, confidentiality issues, confidence about diagnosis/advice given, technical difficulties and shared decision making., Conclusion: This study is the first to explore experiences of remote appointments among both staff and those from underserved groups living with kidney disease in the United Kingdom. While remote appointments can be beneficial, our findings indicate that remote consultations need optimisation to meet the needs of patients. The project findings informed the development of a Toolkit which will be widely promoted and accessible in the United Kingdom during 2024., (© 2024 The Author(s). Journal of Renal Care published by John Wiley & Sons Ltd on behalf of European Dialysis & Transplant Nurses Association/European Renal Care Association.)

Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents' states or the type of network dictates the individual agents' behavior. In particular, agents' observations depend on both the underlying hypothesis as well as the empirical distribution of the agents' states. Hence, our framework captures a high degree of coupling, in that an individual agent's behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.

This paper describes the development and testing of a polynomial variety-based matrix completion (PVMC) algorithm. Our goal is to reduce computational effort associated with reaction rate coefficient calculations using variational transition state theory with multidimensional tunneling (VTST-MT). The algorithm recovers eigenvalues of quantum mechanical Hessians constituting the minimum energy path (MEP) of a reaction using only a small sample of the information, by leveraging underlying properties of these eigenvalues. In addition to the low-rank property that constitutes the basis for most matrix completion (MC) algorithms, this work introduces a polynomial constraint in the objective function. This enables us to sample matrix columns unlike most conventional MC methods that can only sample elements, which makes PVMC readily compatible with quantum chemistry calculations as sampling a single column requires one Hessian calculation. For various types of reactions─S N 2, hydrogen atom transfer, metal-ligand cooperative catalysis, and enzyme chemistry─we demonstrate that PVMC on average requires only six to seven Hessian calculations to accurately predict both quantum and variational effects.

This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The harmonic variety-based matrix completion (HVMC) algorithm, developed in a previous study [S. J. Quiton et al., J. Chem. Phys. 153, 054122 (2020)], exploits the low-rank character of the polynomial expansion of potential energy to recover vibrational frequencies (square roots of eigenvalues of nuclear Hessians) constituting the reaction path using a small sample of its entities. These frequencies are essential for calculating rate coefficients using variational transition state theory with multidimensional tunneling (VTST-MT). HVMC performance is examined for four S N 2 reactions and five hydrogen transfer reactions, with each H-transfer reaction consisting of at least one vibrational mode strongly coupled to the reaction coordinate. HVMC is robust and captures zero-point energies, vibrational free energies, zero-curvature tunneling, and adiabatic ground state and free energy barriers as well as their positions on the reaction coordinate. For medium to large reactions involving H-transfer, with the sole exception of the most complex Ir catalysis system, less than 35% of total eigenvalue information is necessary for accurate recovery of key VTST-MT observables.

Public goods are biomolecules that benefit cellular populations, such as by providing access to previously unutilized resources. Public good production is energetically costly. To reduce this cost, populations control public good biosynthesis, for example using density-dependent regulation accomplished by quorum sensing. Fitness costs and benefits of public good production must be balanced, similar to optimal investment decisions used in economics. We explore the regulation of a public good that increases the carrying capacity, through experimental measurements of growth in Escherichia coli and analysis using a modified logistic growth model. The timing of public good production showed a sharply peaked optimum in population fitness. The cell density associated with maximum public good benefits was determined by the trade-off between the cost of public good production, in terms of reduced growth rate, and benefits received from public goods, in the form of increased carrying capacity., (© 2022 The Author(s).)

Aims: To explore younger adults' experiences of stroke rehabilitation to inform practice, education and future health policy., Design: Qualitative analysis of digital and other media sources on public platforms., Methods: Between March and June 2020, the experiences of younger adult stroke survivors aged 18 to 45 at the time of the stroke were collected. Data were gathered from publicly available sources, including social media, and from English-speaking users. In total, 117 accounts from 103 participants were identified from films, autobiographical books, blogs, websites, videos, Twitter and Instagram. Data analysis followed narrative and multimodal analysis with a focus on rehabilitation needs., Results: Younger adult stroke survivors make sense of their experience by reflecting on how stroke has impacted their lives. Accounts reflected an emotional journey between the past self, the present self and evolving self, as well as associated challenges such as the impact on relationships and careers. The majority of accounts presented transitions as problematic, including the receipt of the initial diagnosis, or sometimes misdiagnosis, to returning home and achieving long-term rehabilitation goals. Specialist stroke nurses were considered essential in the rehabilitation process., Conclusion: A complex process of recovery follows stroke for younger adult stroke populations. Challenges to the rehabilitation process need to be better understood and the role of nursing highlighted in future service provision. A series of age-related challenges were highlighted that require attention to improve the care and support offered., Impact: This article informs clinicians, educators, and policymakers of the age-related needs of young adult stroke survivors. Focusing on the individual and the development of age-appropriate person-centred stroke care is important. The study highlights the role of stroke nursing and challenges the current policy focus on older stroke populations as well as arguing for greater awareness of age-appropriate stroke rehabilitation in younger adults following stroke., (© 2021 John Wiley & Sons Ltd.)