Transistor-integrated flexible pressure sensors have received considerable interest in emerging fields such as humanoid robotics, prosthetics, and implantable electronics. However, existing designs for these integrated sensors often exhibit a trade-off between pressure response and operating voltage, thus significantly limiting their practical applications. In this letter, we report a unique device design of integrated pressure sensors based on deformable microstructured electrodes capacitively coupled with floating-gate carbon nanotube transistors. The microstructured electrodes can dramatically enhance the pressure-introduced electrostatic control of the transistor, enabling a substantial improvement in the transduced pressure response at low operating voltages. With this unique design, we achieve a high pressure response of $10^5$ and an ultrahigh sensitivity up to $10^4 \text kPa^\text - 1$ at a low operating voltage below 3 V, which holds great promise for the development of advanced functionalized flexible electronics.
We studied the horizontally oriented ice crystals (HOIC) with the combinational observations of a zenith-pointing and a slant-pointing (with a zenith angle of 15 degrees) polarization lidar in Beijing in 2022. The HOICs account for approximately 7.3 % of total ice-containing clouds. These results have the potential to enhance the parameterization scheme in climate models for this unique form of ice crystals.
The effects and mechanisms of carbon (C)- and nitrogen (N)-deficient nutrient conditions (prevalent in natural environment) on bacterial mobile performance in porous media are unclear. This study systematically investigated the transport/retention performance of Gram-negative Escherichia coli and Gram-positive Bacillus subtilis experiencing different nutrient conditions (i.e. nutrient-sufficient, C-deficient, or N-deficient conditions) in column, parallel plate flow chamber (PPFC) and microfluidic chamber systems. We found that compared to those in nutrient-sufficient condition, bacteria (regardless of their type) exposure to C-deficient nutrient condition exhibited 7–14% reduced mobility in porous media, whereas those experienced N-deficient condition had 7–20% enhanced transport in both simulated electrolyte solutions and real groundwater samples. The underlying mechanisms driving to different mobile performance of bacteria exposure to different nutrient conditions were correlated with the composition of proteins (one major component of extracellular polymeric substances (EPS)). Compared to nutrient-sufficient condition, C-deficient condition increased EPS hydrophobicity via enhancing hydrophobic amino acids contents and altering secondary structure within proteins thus decreased bacterial transport, while N-deficient condition decreased EPS hydrophobicity through decreasing the abundance of hydrophobic amino acids within proteins and increased cell mobility. The results showed that via changing cell surface hydrophobicity, exposure bacteria to different nutrient conditions could induce different mobile performance of bacteria.
Test-time adaptation (TTA) aims to adapt the model trained on source domain to unseen target domain using a few unlabeled images during inference, which holds great value for the deployment of models in the clinical practice. In this setting, the model can only access online unlabeled test samples and pre-trained model on the source domain. Because unlabeled test samples may arrive sequentially, the model needs to adjust online for the cross-domain distribution shift from different medical institutions, the scale of which would change concurrently and continually over time. However, unstable optimization and abnormal distribution will lead to error accumulation and catastrophic forgetting. Considering the role of brain extracellular space in balancing neural homeostasis and signal transmission, we recognize that the existing TTA methods lack a dedicated component to ensure the stability and accuracy of the model. In this paper, we propose a robust TTA approach for cross-domain segmentation as MemTTA. Specifically, firstly, we introduce transductive batch normalization to ensure stability, which calculates the mean and the variance from the source domain and current test batch. Secondly, we propose a memorized spatial pixel-level clustering strategy to represent each category with multiple and anisotropic prototypes for feature alignment, which can be associated with the parametric classifier. During test time, we adapt the segmentation model to each test batch with self-supervision augmentation consistency learning to improve the inference performance. MemTTA needs only one epoch training on each test batch, and then is comparable to standard models as the traditional inference pipeline. The proposed method is extensively evaluated on neuron, brain metastases, cardiac, and abdominal organ image segmentation. The experimental results demonstrate that our proposed MemTTA can effectively mitigate test-time domain shift and catastrophic forgetting, and is superior to existing state-of-the-art approaches.
Shortly after the failed PKI uprisings of 1926/27, Tan Malaka and his associates established the Partai Republik Indonesia (PARI). Although he acted as the party chairman and chief strategist, his involvement in the party operation was minimal as he lived in exile. Nevertheless, he loomed large in the eyes of both his followers and enemies. Not only was Tan Malaka a legendary guru for Indonesian revolutionaries, but also an enormous threat to colonial authorities across East and Southeast Asia. This chapter explores Tan Malaka's exile in China between 1927 and 1936 and how such experiences reflect his shifting relationship with Indonesia's ongoing struggles for independence, the international communist movement, and the surveillance and policing practices of multiple colonial states.
