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Animals digest food to fuel brain neurometabolism via cellular respiration. This study demonstrates the combination of a biofuel cell (BFC) and an animal brain stimulator (ABS) implanted in a pigeon. Glucose oxidation and oxygen reduction in an enzymatic BFC supplied electrical power to the ABS. Power from the BFC reached 0.12 mW in vitro and 0.08 mW in vivo using only the natural glucose and oxygen in the pigeon's body. A power management integrated circuit is used to harvest energy from the in vivo BFC at a rate of 28.4 mJ over 10 min, which is sufficient for intermittent neurostimulation.

Abstract In this paper, based on the perspective of carbon emissions, the regional environment and regional innovation strategies were analyzed by using cybernetics. The main idea was to calibrate the diesel engine simulation model with predictive function based on the data collected from the bench test, and then according to the simulation model, the genetic algorithm was applied to optimize the EGR parameters and fuel injection parameters with the target of emission generation and output torque. First of all, the combustion process and emission performance of diesel engine with internal exhaust re-circulation were studied by genetic algorithm, and the genetic algorithm model was constructed. Then, the genetic algorithm program was written based on Matlab. In addition, taking the NOx emission as the target, and the Soot emission and the output torque of the original machine as constraints, through the joint simulation of Matlab and GT-Power, the EGR parameters and fuel injection parameters of the 10% to 50% load conditions were optimized at the speed of 1500 r/min and 2000 r/min. Finally, the validity of this method was verified and analyzed.

Traumatic brain damage is dependent on energy transfer to the brain at impact. Different injury mechanisms may cause different types of brain injury. It is, however, unknown if the relative distribution between apoptotic cell-death and necrotic cell- death in different populations of brain cells varies depending on energy transfer.Experimental contusions were produced with a modified weight drop onto the exposed dura of rats. Animals were divided into two groups. They received a weight drop from two different heights to vary energy transfer to be higher or lower. Animals were sacrificed at 24 hours post injury (1 DPI) or 6 days (6 DPI); brains were frozen and processed for TUNEL (TdT mediated dUTP nick end labelling), light microscopy and immunochemistry.The total number of TUNEL positive cells was higher in the higher energy group on the first day after the injury. At the same time point, relatively fewer cells were apoptotic than necrotic, while relatively more glial cells than neurons were TUNEL-positive in higher energy trauma. At 6 day after the injury fewer cells were TUNEL positive and there were no longer significant differences between the high and low energy groups.Increasing energy transfer in a model for brain contusion demonstrated qualitative and quantitative changes in the pattern of cell death. This complexity must be considered when evaluating brain-protection as treatment results may vary depending on which cellular population and which mechanism of cell death is treated under the exact experimental and clinical conditions.

Abstract The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique.

We studied the effects of ethanol on the energy production system in the brain and liver in acute and chronic intoxications. Ethanol was found to inhibit mitochondrial respiratory chain in the liver. Acute ethanol intoxication results in uncoupling of oxidative phosphorylation. NAD-dependent respiration prevails in chronic intoxication. In the brain, ethanol exposure induces a compensated low-energy shift with activation of fast mitochondrial metabolic cluster and uncoupling of oxidative phosphorylation.

Abstract The development of predictive mathematical models for water management in polymer electrolyte membrane fuel cells requires detailed understanding of water distribution and water transport across the Nafion layer. The anisotropic microstructure of Nafion suggests the measurement of water content and mass transport should be along the fuel cell functional direction, i.e. across the membrane. Non-invasive, high resolution, microscopy measurements of this type are very challenging. We report here the calibration of a minimal mathematical model for diffusive water transport in Nafion against data from high-resolution water content maps determined with a new magnetic resonance imaging methodology developed for this purpose. A mock fuel cell was designed to permit well-controlled wetting and drying boundary conditions. With no chemical potential driving force involved, we assume the water transport behavior will be dominated by diffusion. Moreover we show that, in this context, our model is mathematically equivalent to the traditional permeation models based upon saturation dependent pressure gradients via a capillary pressure ansatz. The non-linear equilibrium water distribution across the Nafion membrane measured in this work suggests a bi-modal diffusivity. The model constructed associates distinct transport behaviors to water contents above and below a critical threshold, consistent with a rearrangement of a micro-structural pore network. The experimental observation and the model prediction agree with the primary features of Weber's model of Nafion, which predicts distinct modes of transport for hydration fronts traversing the through-plane direction of the membrane.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

AbstractBiofuel cell (BFC) that transfers chemical energy into electricity is a promising candidate as an energy‐harvesting device for implantable electronics. However, there still remain major challenges for implantable BFCs, including bulky and rigid device structure mismatching with soft tissues such as the brain, and the power output decreases due to the fouling process in a biological environment. Here, a flexible and anti‐biofouling fiber BFC working in the brain chronically is developed. The fiber BFC is based on a carbon nanotube fiber electrode to possess small size and flexibility. A hydrophilic zwitterionic anti‐biofouling polydopamine‐2‐methacryloyloxyethyl phosphorylcholine layer is designed on the surface of fiber BFC to resist the nonspecific protein adsorption in a complex biological environment. After implantation, the fiber BFC can achieve a stable device/tissue interface, along with a negligible immune response. The fiber BFC has first realized power generation in the mouse brain for over a month, exhibiting its promising prospect as an energy‐harvesting device in vivo.