• Energy Research
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Wireless body sensor networks (WBSNs) mostly consist of low‐cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle‐computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity‐driven green‐oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128‐bit RN, which is much more efficient when compared to the electrocardiography‐based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.

Recent theories and data suggest that adapted behavior involves economic computations during which multiple trade-offs between reward value, accuracy requirement, energy expenditure, and elapsing time are solved so as to obtain rewards as soon as possible while spending the least possible amount of energy. However, the relative impact of movement energy and duration costs on perceptual decision-making and movement initiation is poorly understood. Here, we tested 31 healthy subjects on a perceptual decision-making task in which they executed reaching movements to report probabilistic choices. In distinct blocks of trials, the reaching duration (“Time” condition) and energy (“Effort” condition) costs were independently varied compared to a “Reference” block, while decision difficulty was maintained similar at the block level. Participants also performed a simple delayed-reaching (DR) task aimed at estimating movement initiation duration in each motor condition. Results in that DR task show that long duration movements extended reaction times (RTs) in most subjects, whereas energy-consuming movements led to mixed effects on RTs. In the decision task, about half of the subjects decreased their decision durations (DDs) in the Time condition, while the impact of energy on DDs were again mixed across subjects. Decision accuracy was overall similar across motor conditions. These results indicate that movement duration and, to a lesser extent, energy expenditure, idiosyncratically affect perceptual decision-making and action initiation. We propose that subjects who shortened their choices in the time-consuming condition of the decision task did so to limit a drop of reward rate.

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.

Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the smart grid is its vulnerability to cyber threats. In the event of a cyber attack, grid data may be missing; subsequently, load forecast and power planning that rely on these data cannot be processed by generation centers. To address this issue, this paper proposes a transfer learning-based framework for smart grid scheduling that is less reliant on local data while capable of delivering schedules with low operating cost. Specifically, the proposed framework contains (1) a power forecasting model based on transfer learning which can provide high quality load prediction with limited training data, (2) a novel adaptive time series prediction method with modeling time series from a covariate shift perspective that aims to train the forecasting model with a strong generalization capability, and (3) a day-ahead optimal economic power scheduling model considering a shared energy storage station.

Lithium metal is a promising anode material with extremely high theoretical specific capacity (3,860 mA h g-1), low density (0.59 g cm-3), and the lowest negative electrochemical potential of all potential candidates (-3.04 V vs. the standard hydrogen electrode). However, uncontrollable Li dendrite growth leads to a short lifespan and catastrophic safety hazards, which has restricted its practical application for many years. Some effective strategies have been adopted regarding these challenges, including electrolyte modification, introducing a protective layer, nanostructured anodes, and membrane modification. Carbon-based materials have been demonstrated to significantly address the challenge of Li dendrites. In this review, carbon-based materials and their application and challenges in lithium metal anode protection have been discussed in detail. In addition, the applications of lithium anodes protected by carbon-based materials in Li-S batteries and Li-O2 batteries have been summarized.

A combined enzymatic treatment/acid hydrolysis technique was utilized to synthesize cellulose nanocrystals (CNCs) from sugar beet pulp. CNCs were functionalized with magnetite nanoparticles and dopamine making a versatile nano-carrier (DA/Fe3O4NPs@CNCs) for covalent enzyme immobilization. Oxygene/amine functionalities, high magnetization value, and specific surface area of DA/Fe3O4NPs@CNCs made it a reusable and green candidate for conjugation to hydrolytic enzyme cocktails (three cellulases, two hemicellulases, and their combinations) to prepare an innovative and practical nano-biocatalyst for biomass conversion. The conjugated enzymes showed an enhanced optimum temperature (∼ 10 °C), improved thermal stability, and shifted optimum pH toward alkaline pHs. Covalent attachment could successfully suppress the enzyme leaching and provide easy recovery/reuse of the nano-biocatalyst up to 10 cycles, with > 50% of initial activity. Application of the nano-biocatalyst in hydrolysis of rice straw and sugar beet pulp showed an increase (20-76%) in the yield of fermentable sugars compared to the free enzyme cocktails.