Tunneling magnetoresistance in diluted magnetic semiconductor tunnel junctions

Using the spin-polarized tunneling model and taking into account the basic physics of ferromagnetic semiconductors, we study the temperature dependence of the tunneling magnetoresistance (TMR) in the diluted magnetic semiconductor (DMS) trilayer heterostructure system (Ga,Mn)As/AlAs/(Ga,Mn)As. The experimentally observed TMR ratio is in reasonable agreement with our result based on the typical material parameters. It is also shown that the TMR ratio has a strong dependence on both the itinerant-carrier density and the magnetic ion density in the DMS electrodes. This can provide a potential way to achieve larger TMR ratio by optimally adjusting the material parameters.Comment: 5 pages (RevTex), 3 figures (eps), submitted to PR

Prediction and analysis of the residual capacity of concrete-filled steel tube stub columns under axial compression subjected to combined freeze-thaw cycles and acid rain corrosion

© 2019 by the authors. This paper presents a theoretical investigation on the safety evaluation, stability evaluation, and service life prediction of concrete-filled steel tube (CFST) structures in a Northern China area with acid rain. The finite element software ABAQUS was used to establish a numerical model, which was used to simulate the axial compression behavior of CFST columns subjected to the combined actions of freeze-thaw cycles and acid rain corrosion. The model performance was validated using the experimental results of the evaluation of mechanical properties, including the failure mode and load-displacement curve. Then, the effects of the section size, material strength, steel ratio, and combined times on the residual capacity were studied. The results show that the section size has a smaller influence on the residual strength than the other parameters and can be neglected in the design procedure. However, the other parameters, including the material strength, steel ratio, and combined times have relatively large influences on the axial compressive performance of CFST stub columns subjected to a combination of freeze-thaw cycles and acid rain corrosion. Finally, design formulae for predicting the residual strength of CFST stub columns that are under axial compression and the combined effect of freeze-thaw cycles and acid rain corrosion are proposed, and their results agree well with the numerical results

A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained

Detecting time-fragmented cache attacks against AES using Performance Monitoring Counters

Cache timing attacks use shared caches in multi-core processors as side channels to extract information from victim processes. These attacks are particularly dangerous in cloud infrastructures, in which the deployed countermeasures cause collateral effects in terms of performance loss and increase in energy consumption. We propose to monitor the victim process using an independent monitoring (detector) process, that continuously measures selected Performance Monitoring Counters (PMC) to detect the presence of an attack. Ad-hoc countermeasures can be applied only when such a risky situation arises. In our case, the victim process is the AES encryption algorithm and the attack is performed by means of random encryption requests. We demonstrate that PMCs are a feasible tool to detect the attack and that sampling PMCs at high frequencies is worse than sampling at lower frequencies in terms of detection capabilities, particularly when the attack is fragmented in time to try to be hidden from detection

Bidirectional multi-scale attention networks for semantic segmentation of oblique UAV imagery

Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, which fuse features from multiple scales bidirectionally for more adaptive and effective feature extraction. The experiments are conducted on the UAVid2020 dataset and have shown the effectiveness of our method. Our model achieved the state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score of 70.80%

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation

Sponge-like chitosan based porous monolith for uraemic toxins sorption

More than three million patients are treated for kidney failure world-wide. Haemodialysis, the most commonly used treatment, requires large amounts of water and generates mountains of non-recyclable plastic waste. To improve the environmental footprint, dialysis treatments need to develop absorbents to regenerate the waste dialysate. Whereas conventional dialysis clears watersoluble toxins, it is not so effective in clearing protein-bound uraemic toxins (PBUTs), such as indoxyl sulfate (IS). Thus, developing absorption devices to remove both water-soluble toxins and PBUTs would be advantageous. Vapour induced phase separation (VIPS) has been used in this work to produce polycaprolactone/chitosan (PCL/CS) composite symmetric porous monoliths with extra porous carbon additives to increase creatinine and albumin-bound IS absorption. Moreover, these easy-to-fabricate porous monoliths can be formed into the required geometry. The PCL/CS porous monoliths absorbed 436 μg/g of albumin-bound IS and 2865 μg/g of creatinine in a single-pass perfusion model within 1 h. This porous PCL/CS monolith could potentially be used to absorb uraemic toxins, including PBUTs, and thus allow the regeneration of waste dialysate and the development of a new generation of environmentally sustainable dialysis treatments, including wearable devices

The significant role of post-pairing male behavior on the evolution of male preferences and female traits

Existing sexual selection theory postulates that a sufficiently large variation in female fecundity or other direct benefits are fundamental for generating male mate choice. In this study, we suggest that, in addition to pre-pairing preferences, choosy males can also have different post-pairing behaviors, a factor which has been comparatively overlooked by previous studies. We found that both male preferences and female traits could evolve much more easily than previously expected when the choosy males that paired with unpreferred females would allocate more efforts to seeking additional post-pairing mating opportunities. Furthermore, a costly female trait could evolve when there was a trade-off between seeking additional mating and paternal care investment within social pair for choosy males. Finally, a costly male preference and a costly female trait might still evolve and reach a stable polymorphic state in the population, which might give rise to a high variability in male choice and female traits in nature. We suggest that male mate choice may be even more common than expected, which needs to be verified empiricall

Effect of nonnutritive sucking and oral stimulation on feeding performance in preterm infants: a randomized controlled trial.

OBJECTIVES: To evaluate the effectiveness of nonnutritive sucking (NNS) and oral stimulation (OS), either applied alone or in combination, to reduce the transition time from tube feeding to independent oral feeding. DESIGN: Randomized controlled trial. SETTING: A 40-bed neonatal ICU in a university hospital in the People's Republic of China. PATIENTS: A total of 120 preterm infants were admitted to the neonatal ICU from December 2012 to July 2013. INTERVENTIONS: Oral motor interventions. MEASUREMENTS AND MAIN RESULTS: One hundred twelve preterm infants were assigned to three intervention groups (NNS, OS, and combined NNS + OS) and one control group. Primary outcome was the number of days needed from introduction of oral feeding to autonomous oral feeding (transition time). Secondary outcome measures were the rate of milk transfer (mL/min), proficiency (intake first 5 min/volume ordered), volume transfer (volume transferred during entire feeding/volume prescribed), weight, and hospital length of stay. Transition time was reduced in the three intervention groups compared with the control group (p < 0.001). The milk transfer rate in the three intervention groups was greater than in the control group (F3,363 = 15.37; p < 0.001). Proficiency in the NNS and OS groups did not exceed that in the control group while the proficiency in the NNS + OS group was greater than that in the control group at the stage when the infants initiated the oral feeding (p = 0.035). Among all groups, no significant difference was found on weight gain and length of stay. CONCLUSIONS: The combined NNS + OS intervention reduced the transition time from introduction to independent oral feeding and enhanced the milk transfer rate. The combined intervention seems to have a beneficial effect on oral feeding proficiency in preterm infants