Preparation and adsorbing sodium borohydride of porous hollow capsules

AbstractSodium borohydride, a solid material, has great attractive for its hydrogen storage and preparation properties. The solubility of sodium borohydride in water is up to 35% at room temperature. It can react with water for generating hydrogen. The merits of this reaction include high purity, mild reaction conditions and the high theoretical density of hydrogen generation. Complete hydrolysis of sodium borohydride can produce hydrogen and sodium metaborate, which can be recovered by advanced technology for sodium borohydride recycling. Porous hollow capsules containing nickel boride were prepared and used as storage and reaction space for sodium borohydride. The influences of the concentration of polymer solution, the ratio of the coagulation bath, the concentration and temperature on the porous structure of hollow capsule were investigated. The adsorption of porous hollow capsule was influenced and optimized by soaking time, adsorption conditions, the drying temperature and time. The best conditions of preparation of porous hollow capsule are: 15 wt% PVDF into capsule system configuration, with adding 15wt % attapulgite or 5 wt% PVP. The adsorption amount is up to 36%. The preparation method of porous hollow capsule is simple and easy to operate, low energy consumption, simple process only including dissolving, mixing, molding, adsorption and drying. The structure of porous hollow is stable and easy to storage and use. Hydrogen can be simple to release when mixed the adsorbed capsules with water

Unconventional superconducting gap in NaFe0.95_{0.95}Co0.05_{0.05}As observed by angle-resolved photoemission spectroscopy

We have performed high resolution angle-resolved photoemission measurements on superconducting electron-doped NaFe$_{0.95}$Co$_{0.05}$As ($T_{c}\sim$18 K). We observed a hole-like Fermi surface around the zone center and two electron-like Fermi surfaces around the M point which can be connected by the $Q=(\pi, \pi)$ wavevector, suggesting that scattering over the near-nested Fermi surfaces is important to the superconductivity of this "111" pnicitide. Nearly isotropic superconducting gaps with sharp coherent peaks are observed below $T_c$ on all three Fermi surfaces. Upon increasing temperature through $T_c$, the gap size shows little change while the coherence vanishes. Large ratios of $2\Delta/k_{B}T_{c}\sim8$ are observed for all the bands, indicating a strong coupling in this system. These results are not expected from a classical phonon-mediated pairing mechanism.Comment: 4 pages, 4 figure

Small anisotropy of the lower critical field and s±s_\pm-wave two-gap feature in single crystal LiFeAs

The in- and out-of-plane lower critical fields and magnetic penetration depths for LiFeAs were examined. The anisotropy ratio $\gamma_{H_{c1}}(0)$ is smaller than the expected theoretical value, and increased slightly with increasing temperature from 0.6$T_c$ to $T_c$. This small degree of anisotropy was numerically confirmed by considering electron correlation effect. The temperature dependence of the penetration depths followed a power law($\sim$$T^n$) below 0.3$T_c$, with $n$$>$3.5 for both $\lambda_{ab}$ and $\lambda_c$. Based on theoretical studies of iron-based superconductors, these results suggest that the superconductivity of LiFeAs can be represented by an extended $s_\pm$-wave due to weak impurity scattering effect. And the magnitudes of the two gaps were also evaluted by fitting the superfluid density for both the in- and out-of-plane to the two-gap model. The estimated values for the two gaps are consistent with the results of angle resolved photoemission spectroscopy and specific heat experiments.Comment: 10 pages, 5 figure

Berberine hydrochloride: anticancer activity and nanoparticulate delivery system

Wen Tan, Yingbo Li, Meiwan Chen, Yitao WangState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao Special Administrative Region, ChinaBackground: Berberine hydrochloride is a conventional component in Chinese medicine, and is characterized by a diversity of pharmacological effects. However, due to its hydrophobic properties, along with poor stability and bioavailability, the application of berberine hydrochloride was hampered for a long time. In recent years, the pharmaceutical preparation of berberine hydrochloride has improved to achieve good prospects for clinical application, especially for novel nanoparticulate delivery systems. Moreover, anticancer activity and novel mechanisms have been explored, the chance of regulating glucose and lipid metabolism in cancer cells showing more potential than ever. Therefore, it is expected that appropriate pharmaceutical procedures could be applied to the enormous potential for anticancer efficacy, to give some new insights into anticancer drug preparation in Chinese medicine.Methods and results: We accessed conventional databases, such as PubMed, Scope, and Web of Science, using “berberine hydrochloride”, “anti-cancer mechanism”, and “nanoparticulate delivery system” as search words, then summarized the progress in research, illustrating the need to explore reprogramming of cancer cell metabolism using nanoparticulate drug delivery systems.Conclusion: With increasing research on regulation of cancer cell metabolism by berberine hydrochloride and troubleshooting of issues concerning nanoparticulate delivery preparation, berberine hydrochloride is likely to become a natural component of the nanoparticulate delivery systems used for cancer therapy. Meanwhile, the known mechanisms of berberine hydrochloride, such as decreased multidrug resistance and enhanced sensitivity of chemotherapeutic drugs, along with improvement in patient quality of life, could also provide new insights into cancer cell metabolism and nanoparticulate delivery preparation.Keywords: berberine hydrochloride, anticancer mechanisms, nanoparticulate drug progres

Personalized Federated Instruction Tuning via Neural Architecture Search

Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still faces two key challenges, i.e., data and resource heterogeneity. Due to the varying data distribution and preferences among data owners, FIT cannot adapt to the personalized data of individual owners. Moreover, clients with superior computational abilities are constrained since they need to maintain the same fine-tuning architecture as the weaker clients. To address these issues, we propose a novel Personalized Federated Instruction Tuning (PerFIT) framework based on architecture search. Specifically, PerFIT allows each client to search for a personalized architecture by expanding the trainable parameter space of the global model followed by pruning the parameters to the original state. This procedure allows personalized instruction fine-tuning within expanded parameter spaces, concurrently preserving the same number of trainable parameters. Furthermore, to release the abilities of heterogeneous computational resources and enhance the performance of personalization on local data, we exploit personalized parameter-wise aggregation. The evaluation with multiple LLMs non-IID scenarios demonstrates that compared to the state-of-the-art FIT methods, our approach can achieve up to a 23% decrease in perplexity

WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks

Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes. Although backdoor attacks are effective in various real scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily detectable by existing backdoor detection algorithms. To overcome the weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains image high-frequency features through Discrete Wavelet Transform (DWT) to generate backdoor triggers. Furthermore, we introduce an asymmetric frequency obfuscation method, which can add an adaptive residual in the training and inference stage to improve the impact of triggers and further enhance the effectiveness of WaveAttack. Comprehensive experimental results show that WaveAttack not only achieves higher stealthiness and effectiveness, but also outperforms state-of-the-art (SOTA) backdoor attack methods in the fidelity of images by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and 70.59\% reduction in IS

Microglia and astrocytes underlie neuroinflammation and synaptic susceptibility in autism spectrum disorder

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with onset in childhood. The mechanisms underlying ASD are unclear. In recent years, the role of microglia and astrocytes in ASD has received increasing attention. Microglia prune the synapses or respond to injury by sequestrating the injury site and expressing inflammatory cytokines. Astrocytes maintain homeostasis in the brain microenvironment through the uptake of ions and neurotransmitters. However, the molecular link between ASD and microglia and, or astrocytes remains unknown. Previous research has shown the significant role of microglia and astrocytes in ASD, with reports of increased numbers of reactive microglia and astrocytes in postmortem tissues and animal models of ASD. Therefore, an enhanced understanding of the roles of microglia and astrocytes in ASD is essential for developing effective therapies. This review aimed to summarize the functions of microglia and astrocytes and their contributions to ASD

When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices

Although Federated Learning (FL) enables collaborative learning in Artificial Intelligence of Things (AIoT) design, it fails to work on low-memory AIoT devices due to its heavy memory usage. To address this problem, various federated pruning methods are proposed to reduce memory usage during inference. However, few of them can substantially mitigate the memory burdens during pruning and training. As an alternative, zeroth-order or backpropagation-free (BP-Free) methods can partially alleviate the memory consumption, but they suffer from scaling up and large computation overheads, since the gradient estimation error and floating point operations (FLOPs) increase as the dimensionality of the model parameters grows. In this paper, we propose a federated foresight pruning method based on Neural Tangent Kernel (NTK), which can seamlessly integrate with federated BP-Free training frameworks. We present an approximation to the computation of federated NTK by using the local NTK matrices. Moreover, we demonstrate that the data-free property of our method can substantially reduce the approximation error in extreme data heterogeneity scenarios. Since our approach improves the performance of the vanilla BP-Free method with fewer FLOPs and truly alleviates memory pressure during training and inference, it makes FL more friendly to low-memory devices. Comprehensive experimental results obtained from simulation- and real test-bed-based platforms show that our federated foresight-pruning method not only preserves the ability of the dense model with a memory reduction up to 9x but also boosts the performance of the vanilla BP-Free method with dramatically fewer FLOPs