Non-Contact Adaptive Voltage Sensor Based on Electric Field Coupling Principle.

Non-contact voltage sensors based on the principle of electric field coupling have the advantages of simple loading and unloading, high construction safety, and the fact that they are not affected by line insulation. They can accurately measure line voltage without the need to connect to the measured object. Starting from the principle of non-contact voltage measurement, this article abstracts a non-contact voltage measurement model into the principle of capacitive voltage sharing and deduces its transfer relationship. Secondly, it is theoretically inferred that the edge effect of the traditional symmetric structure sensor plate will cause the actual capacitance value between the sensor plates to be greater than the theoretically calculated capacitance value, resulting in a certain measurement error. Therefore, the addition of an equipotential ring structure is proposed to eliminate the edge additional capacitance caused by the edge effect in order to design the sensor structure. In addition, due to the influence of sensor volume, material dielectric constant, and other factors, the capacitance value of the sensor itself is only at pF level, resulting in poor low-frequency performance and imbuing the sensor with a low voltage division ratio. In this regard, this article analyzes the measurement principle of non-contact voltage sensors. By paralleling ceramic capacitors between the two electrode plates of the sensor, the capacitance of the sensor itself is effectively increased, improving the low-frequency performance of the sensor while also increasing the sensor's voltage division ratio. In addition, by introducing a single pole double throw switch to switch parallel capacitors with different capacitance values, the sensor can have different voltage division ratios in different measurement scenarios, giving it a certain degree of adaptability. The final sensor prototype was made, and a high and low voltage experimental platform was built to test the sensor performance. The experimental results showed that the sensor has good linearity and high measurement accuracy, with a ratio error of within ±3%.

FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation.

As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning. This problem causes clients to forget about global knowledge during their local training phase and then tends to slow convergence and degrades accuracy. In this work, we propose a method named FedRAD, which is based on relational knowledge distillation that further enhances the mining of high-quality global knowledge by local models from a higher-dimensional perspective during their local training phase to better retain global knowledge and avoid forgetting. At the same time, we devise an entropy-wise adaptive weights module (EWAW) to better regulate the proportion of loss in single-sample knowledge distillation versus relational knowledge distillation so that students can weigh losses based on predicted entropy and learn global knowledge more effectively. A series of experiments on CIFAR10 and CIFAR100 show that FedRAD has better performance in terms of convergence speed and classification accuracy compared to other advanced FL methods.

Multivariate Synergistic Flexible Metal-Organic Frameworks with Superproton Conductivity for Direct Methanol Fuel Cells.

Improving proton conductivity and fabricating viable metal-organic frameworks (MOFs) based proton exchange membranes (PEMs) are central issues exploiting electrolyte MOFs. We aim to design multivariate flexibility synergistic strategy to achieve Flexible MOFs (FMOFs) with high conductivity at a wide range of humidity. In situ powder X-ray diffraction (PXRD) and temperature-dependent Fourier transform infrared spectra (FT-IR) prove the synergistic self-adaption between dynamic torsion of alkyl sulfonic acid and dynamic breathing of FMOF, forming a continuous hydrogen-bonding networks to maintain high conductivity. Based on the convincing proton conductivity, we construct a series of long-term durable MOF-based PEMs that serve as a bridge between MOF and fuel cell. Consequently, the membrane electrode assembly (MEA) of the flexible PMNS1-40 exhibits a maximum single-cell power density of 34.76 mW cm-2 and hopefully opens doors to evaluate the practical application of proton-conducting MOFs in direct methanol fuel cells.

Autophagy and Ischemic Stroke.

Ischemic stroke refers to brain tissue ischemia, hypoxic necrosis, and brain softening caused by the interruption of the blood supply to the brain without adequate collateral circulation, thus resulting in neurological symptoms. Autophagy is activated in various cell types in the brain, such as neurons, glial cells, and microvascular cells, upon ischemic stroke. Autophagy efflux injury plays an important role in this pathologic process. This chapter outlines the induction of basal autophagy, autophagy in neurons, and the crosstalk between autophagy, necrosis, and apoptosis that contributes to ischemic stroke. We will highlight the interactions between autophagy, oxidative stress, endoplasmic reticulum stress, and mitochondrial dysfunction, and the role of autophagy in ischemic stroke. We will also review the recent advances in the understanding of the involvement of autophagy in the pathological process of cerebral ischemic preconditioning, periconditioning, and postconditioning.

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection.

In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation. The amplitude coherent accumulation technique that extracts the coherent amplitude information of echoes after being processed by the SMF, is used to improve the SINR based on multiple measurements. Finally, the bi-directional difference technique is proposed to distinguish the target echoes and the interference/noise. Simulations and experiments are conducted to validate and demonstrate that small moving targets can be detected with high probability using the proposed method in urban environments, even with just one measurement.

Self-Adaption of the GIDL Erase Promotes Stacking More Layers in 3D NAND Flash.

The bit density is generally increased by stacking more layers in 3D NAND Flash. Gate-induced drain leakage (GIDL) erase is a critical enabler in the future development of 3D NAND Flash. The relationship between the drain-to-body potential (Vdb) of GIDL transistors and the increasing number of layers was studied to explain the reason for the self-adaption of the GIDL erase. The dynamics controlled by the drain-to-body and drain-to-gate potential contribute to the self-adaption of the GIDL erase. Increasing the number of layers leads to a longer duration of the maximum value of Vdb (Vdb_max), combined with the increased drain-to-gate potential, which enhances the GIDL current and further boosts channel potential to reach the same value at different positions of the NAND string. We proposed a method based on the correlation between the duration of Vdb_max and the number of layers to obtain the limited layers of the GIDL erase. The limited layers allowed are more than four times the number of layers used in the current simulation. Combining the novel method of dividing the channel into multi-regions with the asynchronous GIDL erase method will be useful for further stacking more layers in 3D NAND Flash.

An extreme learning machine optimized by differential evolution and artificial bee colony for predicting the concentration of whole blood with Fourier Transform Raman spectroscopy.

Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component's concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data's accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.

Enhancing the diversity of self-replicating structures using active self-adapting mechanisms.

Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop's original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures.

Semi-supervised few-shot learning approach for plant diseases recognition.

BACKGROUND: Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality. The existing few-shot classification studies in agriculture are mainly based on supervised learning schemes, ignoring unlabeled data's helpful information. METHODS: In this paper, we proposed a semi-supervised few-shot learning approach to solve the plant leaf diseases recognition. Specifically, the public PlantVillage dataset is used and split into the source domain and target domain. Extensive comparison experiments considering the domain split and few-shot parameters (N-way, k-shot) were carried out to validate the correctness and generalization of proposed semi-supervised few-shot methods. In terms of selecting pseudo-labeled samples in the semi-supervised process, we adopted the confidence interval to determine the number of unlabeled samples for pseudo-labelling adaptively. RESULTS: The average improvement by the single semi-supervised method is 2.8%, and that by the iterative semi-supervised method is 4.6%. CONCLUSIONS: The proposed methods can outperform other related works with fewer labeled training data.

Dual Role of Reactive Oxygen Species and their Application in Cancer Therapy.

Reactive oxygen species (ROS) play a dual role in the initiation, development, suppression, and treatment of cancer. Excess ROS can induce nuclear DNA, leading to cancer initiation. Not only that, but ROS also inhibit T cells and natural killer cells and promote the recruitment and M2 polarization of macrophages; consequently, cancer cells escape immune surveillance and immune defense. Furthermore, ROS promote tumor invasion and metastasis by triggering epithelial-mesenchymal transition in tumor cells. Interestingly, massive accumulation of ROS inhibits tumor growth in two ways: (1) by blocking cancer cell proliferation by suppressing the proliferation signaling pathway, cell cycle, and the biosynthesis of nucleotides and ATP and (2) by inducing cancer cell death via activating endoplasmic reticulum stress-, mitochondrial-, and P53- apoptotic pathways and the ferroptosis pathway. Unfortunately, cancer cells can adapt to ROS via a self-adaption system. This review highlighted the bidirectional regulation of ROS in cancer. The study further discussed the application of massively accumulated ROS in cancer treatment. Of note, the dual role of ROS in cancer and the self-adaptive ability of cancer cells should be taken into consideration for cancer prevention.

[Fermentation optimization based on cell self-adaptation to environmental stress - a review].

In industrial fermentation processes, bacteria have to adapt environmental stresses. Sometimes, such a self-adaption does not work and will cause fermentation failures, although such adaptation also can generate unexpected positive effects with improved fermentation performance. Our review introduces cell self-adaption to environmental variations or stress, process optimization based on such self-adaptions, with heterologous proteins production by Pichia pastoris and butanol fermentation as examples. Our review can sever as reference for fermentation optimization based on cell self-adaption.

Self-adaption Compton imaging algorithm for a quasi-point source.

Compton imaging is a promising technology for various applications including nuclear safety, nuclear medicine, and astrophysics. For quasi-point-source applications, which are widely found in practice, a novel Compton imaging algorithm incorporating the concept of self-adaption is proposed that provides excellent precision and high efficiency. In particular, this algorithm significantly improves the imaging precision of backward-scattering imaging events so that they can be revived for reconstruction without degrading image quality. From Monte Carlo simulations, a comparison between the self-adaption Compton imaging algorithm and the conventional Compton imaging algorithm was conducted, and the feasibility and reliability of this algorithm was verified in various scenarios.

Research on lung function prediction methodology combining transfer learning and multimodal feature fusion / 国际生物医学工程杂志

Objective:To design a lung function prediction method that combines transfer learning and multimodal feature fusion, aiming to improve the accuracy of lung function prediction in patients with idiopathic pulmonary fibrosis (IPF).Methods:CT images and clinical text data were reprocessed, and an adaptive module was designed to find the most suitable lung function attenuation function for IPF patients. The feature extraction module was utilized to comprehensively extract features. The feature extraction module comprises three sub-modules, including CT feature extraction, clinical text feature extraction, and lung function feature extraction. A multimodal feature prediction network was used to comprehensively evaluate the attenuation of lung function. The pre-trained model was fine-tuned to improve the predictive performance of the model.Results:Based on the OSIC pulmonary fibrosis progression competition dataset, it is found through the adaptive module that the linear attenuation hypothesis is more in line with the trend of pulmonary function decline in patients. Different modal data prediction experiments show that the model incorporating clinical text features has better predictive ability than the model using only CT images. The model combining CT images, clinical text features, and lung function features have optimal predictive results. The lung function prediction method combining transfer learning and multimodal feature fusion has modified version of the Laplace log likelihood (LLLm) of ?6.706 5, root mean squared error (RMSE) of 184.5, and mean absolute error (MAE) of 146.2, which outperforms other methods in terms of performance. The pre-trained model has higher prediction accuracy compared to the zero base training model.Conclusions:The lung function prediction method designed by combining transfer learning and multimodal feature fusion can effectively predict the lung function status of IPF patients at different weeks, providing important support for patient health management and disease diagnosis.

A novel cluster-tube self-adaptive robot hand.

This paper proposes a novel cluster-tube self-adaptive robot hand (CTSA Hand). The CTSA Hand consists of a base, a motor, a transmission mechanism, multiple elastic tendons, and a group of sliding-tube assemblies. Each sliding-tube assembly is composed of a sliding tube, a guide rod, two springs and a hinge. When the hand grasping an object, the object pushes some sliding tubes to different positions according to the surface shape of the object, the motor pulls the tendons tight to cluster tubes. The CTSA Hand can realize self-adaptive grasping of objects of different sizes and shapes. The CTSA Hand can grasp multiple objects simultaneously because the grasping of the hand acts as many grippers in different directions and heights. The grasping forces of the hand are adjusted by a closed-loop control system with potentiometer. Experimental results show that the CTSA Hand has the features of highly self-adaption and large grasping forces when grasping various objects.

The time course of altered brain activity during 7-day simulated microgravity.

Microgravity causes multiple changes in physical and mental levels in humans, which can induce performance deficiency among astronauts. Studying the variations in brain activity that occur during microgravity would help astronauts to deal with these changes. In the current study, resting-state functional magnetic resonance imaging (rs-fMRI) was used to observe the variations in brain activity during a 7-day head down tilt (HDT) bed rest, which is a common and reliable model for simulated microgravity. The amplitudes of low frequency fluctuation (ALFF) of twenty subjects were recorded pre-head down tilt (pre-HDT), during a bed rest period (HDT0), and then each day in the HDT period (HDT1-HDT7). One-way analysis of variance (ANOVA) of the ALFF values over these 8 days was used to test the variation across time period (p < 0.05, corrected). Compared to HDT0, subjects presented lower ALFF values in the posterior cingulate cortex (PCC) and higher ALFF values in the anterior cingulate cortex (ACC) during the HDT period, which may partially account for the lack of cognitive flexibility and alterations in autonomic nervous system seen among astronauts in microgravity. Additionally, the observed improvement in function in CPL during the HDT period may play a compensatory role to the functional decline in the paracentral lobule to sustain normal levels of fine motor control for astronauts in a microgravity environment. Above all, those floating brain activities during 7 days of simulated microgravity may indicate that the brain self-adapts to help astronauts adjust to the multiple negative stressors encountered in a microgravity environment.

Self-adaptive image denoising based on bidimensional empirical mode decomposition (BEMD).

To better analyze images with the Gaussian white noise, it is necessary to remove the noise before image processing. In this paper, we propose a self-adaptive image denoising method based on bidimensional empirical mode decomposition (BEMD). Firstly, normal probability plot confirms that 2D-IMF of Gaussian white noise images decomposed by BEMD follow the normal distribution. Secondly, energy estimation equation of the ith 2D-IMF (i=2,3,4,......) is proposed referencing that of ith IMF (i=2,3,4,......) obtained by empirical mode decomposition (EMD). Thirdly, the self-adaptive threshold of each 2D-IMF is calculated. Eventually, the algorithm of the self-adaptive image denoising method based on BEMD is described. From the practical perspective, this is applied for denoising of the magnetic resonance images (MRI) of the brain. And the results show it has a better denoising performance compared with other methods.

Fermentation optimization based on cell self-adaptation to environmental stress - a review / 生物工程学报

In industrial fermentation processes, bacteria have to adapt environmental stresses. Sometimes, such a self-adaption does not work and will cause fermentation failures, although such adaptation also can generate unexpected positive effects with improved fermentation performance. Our review introduces cell self-adaption to environmental variations or stress, process optimization based on such self-adaptions, with heterologous proteins production by Pichia pastoris and butanol fermentation as examples. Our review can sever as reference for fermentation optimization based on cell self-adaption.