• Energy Research
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Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649 Indoor environmental quality (IEQ) measurement and survey data of demonstration buildings in the hybridGEOTABS project

SummaryMultithreaded applications facilitate the exploitation of the computing power of multicore architectures. On the other hand, these applications can become extremely energy‐intensive, in contrast with the need for limiting the energy usage of computing systems. In this article, we explore the design of techniques enabling multithreaded applications to maximize their performance under a power cap. We consider two control parameters: the number of cores used by the application, and the core power state. We target the design of an autotuning power‐capping technique with minimal intrusiveness and high portability, which is agnostic about the workload profile of the application. We investigate two different approaches for building the strategy for selecting the best configuration of the parameters under control, namely a heuristic approach and a model‐based approach. Through an extensive experimental study, we evaluate the effectiveness of the proposed technique considering two different selection strategies, and we compare them with existing solutions.

AbstractEmbedding sustainability in day‐to‐day operations necessitates employees' proactiveness beyond compliance with standardised management practices. Exchanging information on safety and environmental issues is acknowledged as a component of workers' proactive behaviours for sustainability embeddedness, which is hardly promoted by codified procedures. Accordingly, identifying leverages to stimulate information exchange is a primary objective for academics and practitioners. The study investigates drivers of information exchange on safety and environmental near misses by examining five situation‐related antecedents: rewards/reinforcements, organisational boundaries, social proactiveness, stakeholder salience and connectedness. The study relies on a mixed method approach, by combining social network analysis and a questionnaire survey conducted among four large Italian companies. The findings partially support the hypothesis of the study, highlighting rewards/reinforcements and connectedness as relevant antecedents of information exchange, while organisational boundaries only partially relate with information exchange. Surprisingly, stakeholder salience and social proactiveness do not emerge as significant drivers of information exchange. The study contributes to the debate on extra‐role pro‐sustainability behaviours and provides implications for managerial practice.

Alpine grasslands on the Qinghai-Tibetan Plateau are sensitive and vulnerable to climate change and human activities. Climate warming and overgrazing have already caused degradation in a large fraction of alpine grasslands on this plateau. However, it remains unclear how human activities (mainly livestock grazing) regulates vegetation dynamics under climate change. Here, alpine grassland productivity (substituted with the normalized difference vegetation index, NDVI) is hypothesized to vary in a nonlinear trajectory to follow climate fluctuations and human disturbances. With generalized additive mixed modelling (GAMM) and residual-trend (RESTREND) analysis together, both magnitude and direction of climatic (in terms of temperature, precipitation, and radiation) and anthropogenic impacts on NDVI variation were examined across alpine meadows, steppes, and desert-steppes on the Qinghai-Tibetan Plateau. The results revealed that accelerating warming and greening, respectively, took place in 76.2% and 78.8% of alpine grasslands on the Qinghai-Tibetan Plateau. The relative importance of temperature, precipitation, and radiation impacts was comparable, between 20.4% and 24.8%, and combined to explain 66.2% of NDVI variance at the pixel scale. The human influence was strengthening and weakening, respectively, in 15.5% and 14.3% of grassland pixels, being slightly larger than any sole climatic variable across the entire plateau. Anthropogenic and climatic factors can be in opposite ways to affect alpine grasslands, even within the same grassland type, likely regulated by plant community assembly and species functional traits. Therefore, the underlying mechanisms of how plant functional diversity regulates nonlinear ecosystem response to climatic and anthropogenic stresses should be carefully explored in the future.

Data Availability: The data that support the findings of this study are available from the corresponding author, C. Wen, upon reasonable request. ; Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model’s superiority. ; This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, the Alexander von Humboldt Foundation of Germany, the BRIEF Award of Brunel University London, the National Natural Science Foundation of China under Grant 61973209, the Natural Science Foundation of Shanghai of China under Grant 20ZR1421200, and the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100.

Technical and economic developments in battery and fast-charging technologies could soon make fuel cell electric vehicles, which run on hydrogen, superfluous in road transport