Development of optimisation schemes for ultrasound particle sizing and concentration measurements

Particle size is a critical indicator of product quality, significantly affecting product stability, solubility, and flowability. With the advancement of science and technology and the improvement of industrial standards, particle size measurement has become increasingly important in many fields, such as chemical engineering, pharmaceuticals, and materials science. Among the numerous particle size distribution measurement techniques, ultrasonic attenuation spectroscopy has attracted the attention of many researchers due to its strong penetration ability, wide frequency range, fast response speed, and non-contact advantages. The most classic theoretical model in ultrasonic attenuation is the ECAH model, which is widely applicable because it covers most comprehensively the attenuation mechanisms. However, a major limitation of the ECAH model is that it requires many material properties parameters, many of which are unknown or inaccurate. Given some test run results, it is possible to use a retrofitting process to determine what those unknown/inaccurate values should be to minimise the error between the measured and ECAH predicted results. The aim of this project is to compare error minimisation algorithms and evaluate how they perform in different scenarios. The main novelty of the research is that both unknown/inaccurate material properties and particle size distribution (PSD) parameters can be determined simultaneously through an optimisation process. The tested optimisation algorithms include Genetic Algorithm (GA) optimisation, Particle Swarm (PS) optimisation and Parallel Traversal (PT) algorithm. This research will have a significant impact on the field of ultrasonic attenuation spectroscopy for PSD measurement. Firstly, for the first time, a systematic sensitivity analysis has performed for all the optimisable parameters. This is useful in narrowing the range of values a parameter can have when it is being optimised, thus helping to speed up the optimisation process. Secondly, the simultaneous optimisation of both material properties and PSD parameters has been shown to give more accurate results than optimising parameters and PSD separately. Finally, test result have indicated that if the number of parameters to be optimised is small (e.g., <=3), PT is the quickest among the three for comparable setups; for more parameters, GA runtime is more predictable than PS

A multi-port current-limiting hybrid DC crcuit breaker

Recently the hybrid multi-port DC circuit breaker (MP-DCCB) is becoming popular in protecting HVDC grids, thanks to their re-duction of power electronics devices. In this paper, an enhanced multi-port current-limiting DCCB (MP-CLCB) for multiple line protection is proposed. The integrated fault current limiter (FCL) inside the MP-CLCB can clear the fault faster with slightly in-creased costs. To reduce the energy dissipation requirement for the surge arresters caused by the newly added current-limiting path, an energy transfer path which provides a loop with the in-ductors during the current decay stage is designed. The theoreti-cal analysis of the pre-charging, current-limiting, fault interrup-tion and energy dissipation of the MP-CLCB is carried out. Moreover, the design principles of the energy dissipation and the key parameters of the MP-CLCB are provided. The proposed approaches are verified through simulations in PSCAD/EMTDC. The results show that the MP-CLCB can replace multiple DCCBs, accelerate the fault current interruption and reduce the energy dissipation requirement for the surge arresters

Game theory based maritime area detection for cloud-edge collaboration satellite network

Maritime area detection technology applies equipment such as high-orbit satellites, gateway ships and Unmanned Aerial Vehicles to detection. In this scenario, real-time uploading and analysis of maritime data is crucial. In the existing scenario, UAV data are gathered to the gateway ship and uploaded to the shore-based cloud via the high-orbit satellite, because the communication distance of the high-orbit satellite is far, and when the uploaded data volume is large or the access to the equipment increases, the propagation delay of the uploading of the data from the gateway ship to the satellite and the forwarding of the data from the satellite to the shore-based cloud is longer, and the processing delay of the shore-based cloud is increased, and the efficiency of the data transmission and communication will be affected as well. Aiming at the problem of increasing delay caused by communication limitations in maritime area detection, this paper proposes a maritime area detection scheme based on cloud-side collaboration. The scheme solves the problem of communication limitation from the following two aspects. First, the edge computing nodes are deployed on the ship side of the gateway, and the optimal offloading ratio is sought through game theory to offload a part of the tasks from the center cloud to the edge cloud for processing, which improves the efficiency of processing data and thus reduces the data transmission latency and data processing delay. Secondly, low-orbit (LEO) satellites are introduced to provide communication services, because low-orbit satellites have low orbital altitude and short propagation delay, which can transmit the data at the gateway ship to the shore-based cloud more quickly and improve the data transmission efficiency. Finally, it is also verified by designing experiments that the proposed scheme adopts the optimal offloading ratio and has a lower total delay than the original scheme, thus proving the effectiveness of the proposed scheme

Sequence-level Semantic Representation Fusion for Recommender Systems

With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated \emph{textual data} of items (eg product title) and study how text features can be effectively fused with ID features in sequential recommendation. However, there exists distinct data characteristics for the two kinds of item features, making a direct fusion method (eg adding text and ID embeddings as item representation) become less effective. To address this issue, we propose a novel {\ul \emph{Te}}xt-I{\ul \emph{D}} semantic fusion approach for sequential {\ul \emph{Rec}}ommendation, namely \textbf{\our}. The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts. The key strategy lies in that we transform the text embeddings and ID embeddings by Fourier Transform from \emph{time domain} to \emph{frequency domain}. In the frequency domain, the global sequential characteristics of the original sequences are inherently aggregated into the transformed representations, so that we can employ simple multiplicative operations to effectively fuse the two kinds of item features. Our fusion approach can be proved to have the same effects of contextual convolution, so as to achieving sequence-level semantic fusion. In order to further improve the fusion performance, we propose to enhance the discriminability of the text embeddings from the text encoder, by adaptively injecting positional information via a mixture-of-experts~(MoE) modulation method. Our implementation is available at this repository: \textcolor{magenta}{\url{https://github.com/RUCAIBox/TedRec}}.Comment: 8 pages, 5 figure

NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning

Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. However, their predominant use in an offline manner usually suffers from substantial domain gap between the VLN task and the LLM training corpus. This paper introduces a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision, leading to a significant mitigation of the domain gap in a cost-effective manner. Specifically, at each timestep, the LLM is prompted to forecast the navigational chain-of-thought by: 1) acting as a world model to imagine the next observation according to the instruction, 2) selecting the candidate observation that best aligns with the imagination, and 3) determining the action based on the reasoning from the prior steps. Through constructing formalized labels for training, the LLM can learn to generate desired and reasonable chain-of-thought outputs for improving the action decision. Experimental results across various training settings and popular VLN benchmarks (e.g., Room-to-Room (R2R), Room-across-Room (RxR), Room-for-Room (R4R)) show the significant superiority of NavCoT over the direct action prediction variants. Through simple parameter-efficient finetuning, our NavCoT outperforms a recent GPT4-based approach with ~7% relative improvement on the R2R dataset. We believe that NavCoT will help unlock more task-adaptive and scalable LLM-based embodied agents, which are helpful for developing real-world robotics applications. Code is available at https://github.com/expectorlin/NavCoT

Extreme long-lifetime self-assembled monolayer for air-stable molecular junctions

The molecular electronic devices based on self-assembled monolayer (SAM) on metal surfaces demonstrate novel electronic functions for device minimization yet are unable to realize in practical applications, due to their instability against oxidation of the sulfur-metal bond. This paper describes an alternative to the thiolate anchoring group to form stable SAMs on gold by selenides anchoring group. Because of the formation of strong selenium-gold bonds, these stable SAMs allow us to incorporate them in molecular tunnel junctions to yield extremely stable junctions for over 200 days. A detailed structural characterization supported by spectroscopy and first-principles modeling shows that the oxidation process is much slower with the selenium-gold bond than the sulfur-gold bond, and the selenium-gold bond is strong enough to avoid bond breaking even when it is eventually oxidized. This proof of concept demonstrates that the extraordinarily stable SAMs derived from sel-enides are useful for long-lived molecular electronic devices and can possibly become important in many air-stable applications involving SAMs.</p

PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

BackgroundPANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis.MethodsWe downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model.ResultsWe obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8+ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient’s survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues.ConclusionWe first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis

Distinguishing two-component anomalous Hall effect from topological Hall effect in magnetic topological insulator MnBi2Te4

In transport, the topological Hall effect (THE) is widely interpreted as a sign of chiral spin textures, like magnetic skyrmions. However, the co-existence of two anomalous Hall effects (AHE) could give rise to similar non-monotonic features or "humps", making it difficult to distinguish between the two. Here we demonstrate that the "artifact" two-component anomalous Hall effect can be clearly distinguished from the genuine topological Hall effect by three methods: 1. Minor loops 2. Temperature dependence 3. Gate dependence. One of the minor loops is a single loop that cannot fit into the full AHE loop under the assumption of AHE+THE. In addition, by increasing the temperature or tuning the gate bias, the emergence of humps is accompanied by a polarity change of the AHE. Using these three methods, one can find the humps are from another AHE loop with a different polarity. Our material is a magnetic topological insulator MnBi2Te4 grown by molecular beam epitaxy, where the presence of the secondary phase MnTe2 on the surface contributes to the extra positive AHE component. Our work may help future researchers to exercise cautions and use these three methods to examine carefully in order to ascertain genuine topological Hall effect

Genipin Crosslinks the Extracellular Matrix to Rescue Developmental and Degenerative Defects, and Accelerates Regeneration of Peripheral Neurons

UNLABELLED: The peripheral nervous system (PNS) is essential for proper body function. A high percentage of the population suffer nerve degeneration or peripheral damage. For example, over 40% of patients with diabetes or undergoing chemotherapy develop peripheral neuropathies. Despite this, there are major gaps in the knowledge of human PNS development and therefore, there are no available treatments. Familial Dysautonomia (FD) is a devastating disorder that specifically affects the PNS making it an ideal model to study PNS dysfunction. FD is caused by a homozygous point mutation in ELP1 leading to developmental and degenerative defects in the sensory and autonomic lineages. We previously employed human pluripotent stem cells (hPSCs) to show that peripheral sensory neurons (SNs) are not generated efficiently and degenerate over time in FD. Here, we conducted a chemical screen to identify compounds able to rescue this SN differentiation inefficiency. We identified that genipin, a compound prescribed in Traditional Chinese Medicine for neurodegenerative disorders, restores neural crest and SN development in FD, both in the hPSC model and in a FD mouse model. Additionally, genipin prevented FD neuronal degeneration, suggesting that it could be offered to patients suffering from PNS neurodegenerative disorders. We found that genipin crosslinks the extracellular matrix, increases the stiffness of the ECM, reorganizes the actin cytoskeleton, and promotes transcription of YAP-dependent genes. Finally, we show that genipin enhances axon regeneration in an in vitro axotomy model in healthy sensory and sympathetic neurons (part of the PNS) and in prefrontal cortical neurons (part of the central nervous system, CNS). Our results suggest genipin can be used as a promising drug candidate for treatment of neurodevelopmental and neurodegenerative diseases, and as a enhancer of neuronal regeneration. ONE SENTENCE SUMMARY: Genipin rescues the developmental and degenerative phenotypes of the peripheral neuropathy familial dysautonomia and enhances neuron regeneration after injury