Measurement of permeability for ferrous metallic plates using a novel lift-off compensation technique on phase signature

Lift-off of sensor affects the prediction of electromagnetic properties for both ferrous and non-ferrous steel plates. In this paper, we developed a strategy to address this issue for ferrous plates. With increased lift-off, the phase of the measured impedance for steel plates reduces. Meanwhile, the magnitude of the impedance signal decreases. Based on these facts, a phase compensation algorithm is developed which corrects the phase change due to lift-off considering the magnitude of the impedance signal. Further, a new magnetic permeability prediction technique is presented, which has been validated by analytical and measured results. With this new technique, the error in permeability prediction is less than 2% within the range of lift-offs tested

Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?

Prompt tuning (PT) which only tunes the embeddings of an additional sequence of tokens per task, keeping the pre-trained language model (PLM) frozen, has shown remarkable performance in few-shot learning. Despite this, PT has been shown to rely heavily on good initialization of the prompt embeddings. In this work, we study meta prompt tuning (MPT) to systematically explore how meta-learning can help improve (if it can) cross-task generalization in PT through learning to initialize the prompt embeddings from other relevant tasks. We empirically analyze a representative set of meta learning algorithms in a wide range of adaptation settings with different source/target task configurations on a large set of few-shot tasks. With extensive experiments and analysis, we demonstrate the effectiveness of MPT. We find the improvement to be significant particularly on classification tasks. For other kinds of tasks such as question answering, we observe that while MPT can outperform PT in most cases, it does not always outperform multi-task learning. We further provide an in-depth analysis from the perspective of task similarity

An equivalent-effect phenomenon in eddy current non-destructive testing of thin structures

The inductance/impedance due to thin metallic structures in non-destructive testing (NDT) is difficult to evaluate. In particular, in Finite Element Method (FEM) eddy current simulation, an extremely fine mesh is required to accurately simulate skin effects especially at high frequencies, and this could cause an extremely large total mesh for the whole problem, i.e. including, for example, other surrounding structures and excitation sources like coils. Consequently, intensive computation requirements are needed. In this paper, an equivalent-effect phenomenon is found, which has revealed that alternative structures can produce the same effect on the sensor response, i.e. mutual impedance/inductance of coupled coils if a relationship (reciprocal relationship) between the electrical conductivity and the thickness of the structure is observed. By using this relationship, the mutual inductance/impedance can be calculated from the equivalent structures with much fewer mesh elements, which can significantly save the computation time. In eddy current NDT, coils inductance/impedance is normally used as a critical parameter for various industrial applications, such as flaw detection, coating and microstructure sensing. Theoretical derivation, measurements and simulations have been presented to verify the feasibility of the proposed phenomenon

Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines

Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, technology companies such as Microsoft and Google have announced new services which aim to combine search engines with conversational AI. However, we have found numerous mistakes in the public demonstrations that suggest we should not easily trust the factual claims of the AI models. Rather than criticizing specific models or companies, we hope to call on researchers and developers to improve AI models' transparency and factual correctness

Language Models can be Logical Solvers

Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. The state-of-the-art, solver-augmented language models, use LLMs to parse natural language logical questions into symbolic representations first and then adopt external logical solvers to take in the symbolic representations and output the answers. Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions. In this paper, we introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers and bypasses the parsing errors by learning to strict adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers. Experimental results on two public deductive reasoning datasets demonstrate that LoGiPT outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on competitive LLMs like ChatGPT or GPT-4.Comment: Preprin

Evaluation of a clinical pharmacist-led antimicrobial stewardship program in a neurosurgical intensive care unit: a pre-and post-intervention cohort study

Background: Antimicrobial resistance poses a significant challenge in neurosurgical intensive care units (ICU). The excessive use of broad-spectrum antibiotics is closely linked to the emergence and dissemination of drug-resistant bacteria within neurosurgical ICUs. This study assessed the effects of implementing a comprehensive Antimicrobial Stewardship (AMS) program in a neurosurgical ICU setting.Methods: From April 2022 to September 2022, an AMS program was implemented in the neurosurgical ICU. The program involved the regular presence of a pharmacist and an infectious disease physician who conducted prospective audits and provided feedback. To assess the impact of the AMS program, the outcome measures were compared between the AMS period and the 6 months before AMS implementation (pre-AMS period). The primary outcome was the use of antibacterial agents, including anti-pseudomonal beta-lactams (APBLs), polymyxin, and tigecycline. Additionally, the study evaluated the appropriateness of antimicrobial de-escalation and the susceptibility of Gram-negative bacilli to antimicrobial agents.Results: A total of 526 were included during the AMS period, while 487 patients were included in the pre-AMS period. The two groups had no significant differences in disease severity and mortality rates. During the AMS period, there was a notable decrease in the use of APBLs as empiric treatment (43.92% vs. 60.99%, p < 0.001). Multi-drug resistant organism (MDRO) infections decrease significantly during AMS period (11.03% vs. 18.48%, p < 0.001). The number of prescription adjustment increased significantly in all patients (0 item vs. 0 item, p < 0.001) and MDRO-positive patients (3 items vs. 2 items, p < 0.001) during the AMS period. Additionally, appropriate antimicrobial de-escalation for patients with MDRO showed improvement during the AMS period (39.66% vs. 20%, p = 0.001). Polymyxin utilization also decreased during the AMS period (15.52% vs. 31.11%, p = 0.034). Furthermore, the susceptibility of Gram-negative Bacilli isolates to APBLs was significantly higher during the AMS period.Conclusion: Implementing a comprehensive pharmacist-led AMS program led to a decrease in the use of antibacterial agents. This reduction in usage is significant because it can potentially delay the emergence of bacterial resistance

A network pharmacology and molecular docking approach to reveal the mechanism of Chaihu Anxin Capsule in depression

IntroductionAs one of the most frequently diagnosed mental disorders, depression is expected to become the most common disease worldwide by 2030. Previous studies have shown that Chaihu Anxin Capsule has powerful antidepressant effects. However, its mechanisms are not fully understood. The aim of our research is to reveal the mechanisms of Chaihu Anxin Capsule in treating depression.MethodsInformation about the ingredients of the herb was gathered using the TCMSP. Genes associated with antidepressants were gathered from the GeneCards database. An “herbal-ingredient-target” network was constructed and analyzed using Cytoscape software. The PPI network of the antidepressant targets of Chaihu Anxin Capsule was constructed using the STRING database. KEGG pathway and GO enrichment were used to analyze the antidepressant targets. Molecular docking technology was used to confirm the capacity of the primary active ingredients of Chaihu Anxin Capsule to bind to central targets using AutoDock Vina and PyMOL software.ResultsNetwork analysis showed that five targets might be therapeutic targets of Chaihu Anxin Capsule in depression, namely, JUN, IL6, AKT1, TP53, and STAT3. The gene enrichment analysis implied that Chaihu Anxin Capsule benefits patients with depression by modulating pathways related to lipids and atherosclerosis and the AGE-RAGE signaling pathway in diabetic complications. Molecular docking analyses revealed that JUN, IL6, AKT1, TP53, and STAT3 had good affinities for quercetin, beta-sitosterol and kaempferol.ConclusionAccording to the bioinformatics data, the antidepressant effects of Chaihu Anxin Capsule may be primarily linked to cholesterol and atherosclerosis as well as the AGE-RAGE signaling pathway in diabetic complications. These results emphasize that the expected therapeutic targets may be possible indicators for antidepressant activity

Greener, Safer and Better Performing Aqueous Binder for Positive Electrode Manufacturing of Sodium Ion Batteries

P2-type cobalt-free MnNi-based layered oxides are promising cathode materials for sodium-ion batteries (SIBs) due to their high reversible capacity and well chemical stability. However, the phase transformations during repeated (dis)charge steps lead to rapid capacity decay and deteriorated Na+ diffusion kinetics. Moreover, the electrode manufacturing based on polyvinylidene difluoride (PVDF) binder system has been reported with severely defluorination issue as well as the energy intensive and expensive process due to the use of toxic and volatile N-methyl-2-pyrrolidone (NMP) solvent. It calls for designing a sustainable, better performing, and cost-effective binder for positive electrode manufacturing. In this work, we investigated inorganic sodium metasilicate (SMS) as a viable binder in conjunction with P2-Na0.67Mn0.55Ni0.25Fe0.1Ti0.1O2 (NMNFT) cathode material for SIBs. The NMNFT-SMS electrode delivered a superior electrochemical performance compared to carboxy methylcellulose (CMC) and PVDF based electrodes with a reversible capacity of ~161 mAh/g and retaining ~83 % after 200 cycles. Lower cell impedance and faster Na+ diffusion was also observed in this binder system. Meanwhile, with the assistance of TEM technique, SMS is suggested to form a uniform and stable nanoscale layer over the cathode particle surface, protecting the particle from exfoliation/cracking due to electrolyte attack. It effectively maintained the electrode connectivity and suppressed early phase transitions during cycling as confirmed by operando XRD study. With these findings, SMS binder can be proposed as a powerful multifunctional binder to enable positive electrode manufacturing of SIBs and to overall reduce battery manufacturing costs