A High-Efficiency Broadband Rectenna for Ambient Wireless Energy Harvesting

This paper presents a novel broadband rectenna for ambient wireless energy harvesting over the frequency band from 1.8 to 2.5 GHz. First of all, the characteristics of the ambient radio-frequency energy are studied. The results are then used to aid the design of a new rectenna. A novel two-branch impedance matching circuit is introduced to enhance the performance and efficiency of the rectenna at a relatively low ambient input power level. A novel broadband dual-polarized cross-dipole antenna is proposed which has embedded harmonic rejection property and can reject the second and third harmonics to further improve the rectenna efficiency. The measured power sensitivity of this design is down to -35 dBm and the conversion efficiency reaches 55% when the input power to the rectifier is -10 dBm. It is demonstrated that the output power from the proposed rectenna is higher than the other published designs with a similar antenna size under the same ambient condition. The proposed broadband rectenna could be used to power many low-power electronic devices and sensors and found a range of potential applications

Nanofiber Scaffolds with Gradations in Mineral Content for Mimicking the Tendon-to-Bone Insertion Site

We have demonstrated a simple and versatile method for generating a continuously graded, bonelike calcium phosphate coating on a nonwoven mat of electrospun nanofibers. A linear gradient in calcium phosphate content could be achieved across the surface of the nanofiber mat. The gradient had functional consequences with regard to stiffness and biological activity. Specifically, the gradient in mineral content resulted in a gradient in the stiffness of the scaffold and further influenced the activity of mouse preosteoblast MC3T3 cells. This new class of nanofiberbased scaffolds can potentially be employed for repairing the tendon-to-bone insertion site via a tissue engineering approach

Financing institutional long-term care for the elderly in China: a policy evaluation of new models

A rapid ageing population coupled with changes in family structure has brought about profound implications to social policy in China. Although the past decade has seen a steady increase in public funding to long-term care (LTC), the narrow financing base and vast population have created significant unmet demand, calling for reforms in financing. This paper focuses on the financing of institutional LTC care by examining new models that have emerged from local policy experiments against two policy goals: equity and efficiency. Three emerging models are explored: Social Health Insurance (SHI) in Shanghai, LTC Nursing Insurance (LTCNI) in Qingdao and a means-tested model in Nanjing. A focused systematic narrative review of academic and grey literature is conducted to identify and assess these models, supplemented with qualitative interviews with government officials from relevant departments, care home staff and service users. This paper argues that, although SHI appears to be a convenient solution to fund LTC, this model has led to systematic bias in affordable access among participants of different insurance schemes, and has created a powerful incentive for the over-provision of unnecessary services. The means-tested method has been remarkably constrained by narrow eligibility and insufficiency of funding resources. The LTCNI model is by far the most desirable policy option among the three studied here, but the narrow definition of eligibility has substantively excluded a large proportion of elders in need from access to care, which needs to be addressed in future reforms. This paper proposes three lines of LTC financing reforms for policy-makers: (1) the establishment of a prepaid financing mechanism pooled specifically for LTC costs; (2) the incorporation of more stringent eligibility rules and needs assessment; and (3) reforming the dominant fee-for-service methods in paying LTC service providers

Microporous calcium phosphate ceramics driving osteogenesis through surface architecture

The presence of micropores in calcium phosphate (CaP) ceramics has shown its important role in initiating inductive bone formation in ectopic sites. To investigate how microporous CaP ceramics trigger osteoinduction, we optimized two biphasic CaP ceramics (i.e., BCP-R and BCP-S) to have the same chemical composition, equivalent surface area per volume, comparable protein adsorption, similar ion (i.e., calcium and phosphate) exchange and the same surface mineralization potential, but different surface architecture. In particular, BCP-R had a surface roughness (Ra) of 325.4 ± 58.9 nm while for BCP-S it was 231.6 ± 35.7 nm. Ceramic blocks with crossing or noncrossing channels of 250, 500, 1000, and 2000 µm were implanted in paraspinal muscle of dogs for 12 weeks. The percentage of bone volume in the channels was not affected by the type of pores (i.e., crossing vs. closed) or their size, but it was greatly influenced by the ceramic type (i.e., BCP-R vs. BCP-S). Significantly, more bone was formed in the channels of BCP-R than in those of BCP-S. Since the two CaP ceramics differed only in their surface architecture, the results hereby demonstrate that microporous CaP ceramics may induce ectopic osteogenesis through surface architectur

Anomalous impact of thermal fluctuations on spintransfer torque induced ferrimagnetic switching

The dynamics of a spin torque driven ferrimagnetic (FiM) system is investigated using the two-sublattice macrospin model. We demonstrate an ultrafast switching in the picosecond range. However, we find that the excessive current leads to the magnetic oscillation. Therefore, faster switching cannot be achieved by unlimitedly increasing the current. By systematically studying the impact of thermal fluctuations, we find the dynamics of FiMs can also be distinguished into the precessional region, the thermally activated region, and the cross-over region. However, in the precessional region, there is a significant deviation between FiM and ferromagnet (FM), i.e., the FM is insensitive to thermal fluctuations since its switching is only determined by the amount of net charge. In contrast, we find that the thermal effect is pronounced even a very short current pulse is applied to the FiM. We attribute this anomalous effect to the complex relation between the anisotropy and overdrive current. By controlling the magnetic anisotropy, we demonstrate that the FiM can also be configured to be insensitive to thermal fluctuations. This controllable thermal property makes the FiM promising in many emerging applications such as the implementation of tunable activation functions in the neuromorphic computing.Comment: 27 pages, 8 figure

A regression method for EEG-based cross-dataset fatigue detection

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices