Analysis of High Frequency Noise of Inverter Rotary Compressor

The inverter compressor driven by the inverter will cause high frequency noise, which will have adverse influence on total noise value and sound quality. In order to improve this problem, an existing compact rotary inverter compressor is studied in this paper. The influence law of inverter carrier wave of space vector pulse width modulation(SVPWM) technique on motor vibration and noise of compressor is analyzed and summarized. Combining order analysis and motor modal analysis, the results show that the high harmonic current induced by inverter carrier wave will produce high frequency electromagnetic force which excites the stator resonance, and finally results in high frequency noise of the compressor. Through optimization of the motor structure, the high frequency noise is reduced by more than 5dB(A), the sound quality is improved as well

Virtual Prompt Injection for Instruction-Tuned Large Language Models

We present Virtual Prompt Injection (VPI) for instruction-tuned Large Language Models (LLMs). VPI allows an attacker-specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. For instance, if an LLM is compromised with the virtual prompt "Describe Joe Biden negatively." for Joe Biden-related instructions, then any service deploying this model will propagate biased views when handling user queries related to Joe Biden. VPI is especially harmful for two primary reasons. Firstly, the attacker can take fine-grained control over LLM behaviors by defining various virtual prompts, exploiting LLMs' proficiency in following instructions. Secondly, this control is achieved without any interaction from the attacker while the model is in service, leading to persistent attack. To demonstrate the threat, we propose a simple method for performing VPI by poisoning the model's instruction tuning data. We find that our proposed method is highly effective in steering the LLM with VPI. For example, by injecting only 52 poisoned examples (0.1% of the training data size) into the instruction tuning data, the percentage of negative responses given by the trained model on Joe Biden-related queries change from 0% to 40%. We thus highlight the necessity of ensuring the integrity of the instruction-tuning data as little poisoned data can cause stealthy and persistent harm to the deployed model. We further explore the possible defenses and identify data filtering as an effective way to defend against the poisoning attacks. Our project page is available at https://poison-llm.github.io

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its autonomous generation prowess. Through the adept application of IFD, cherry samples are pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on renowned datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of conventional data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the optimization of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/MingLiiii/Cherry_LL

The characteristics of soil salinization effects on nitrogen mineralization and nitrification in upland fields

The influence of soil salinization on nitrogen (N) transformation is largely unknown, which impedes the reasonable management of N in saline fields. A comprehensive meta-analysis was thus conducted to evaluate the effects of salinity and relative soil physicochemical properties on net N mineralization and nitrification in upland soils. Results showed that effects of salinity on the net-N mineralization rate (Min) and nitrification rate (Nit) changed with the salinity level and incubation time. Generally, the inhibitory effect of salt on Min and Nit decreased gradually with incubation time. At 14–16 days of soil incubation, significant stimulatory effects on Min were observed in middle-level (ECe: 12–16 dS m-1) and high-level (ECe >16 dS m-1) saline soils, and on Nit in low-level (ECe: 4–12 dS m-1) saline soils. Regression analysis revealed that the effects of soil organic carbon (SOC), total N (TN), C/N, pH, and clay content on Min and Nit were closely related to salinity levels. Nit at 5–7 days of soil incubation first enhanced and then decreased with C/N increase, and the threshold value was 34.7. The effect of pH on Nit changed with salinity levels, and shifted from stimulation to inhibition with increasing pH. Min at 5–7 days of soil incubation in middle-level group first increased with increasing pH, and decreased when pH was higher than 8.1. Salinization deeply affected soil properties, which further influenced N turnover via alteration of the availability of substrates and microbial biomass and activities. Our findings suggest that the influence of salinity on soil N turnover closely related with salinity level, and salinity level should be considered fully when optimizing N management in saline upland fields

Staphylococcus aureus Bacteriophage Suppresses LPS-Induced Inflammation in MAC-T Bovine Mammary Epithelial Cells

Several previous studies have shown that bacteriophages can significantly affect the production of various cytokines. The aim of this present study was to investigate the inflammatory effects and mechanisms of bacteriophage vB_SauM_JS25 in stimulated MAC-T bovine mammary epithelial cells by real-time polymerase chain reaction (PCR) and Western blotting. Experiments show that vB_SauM_JS25 reduces Staphylococcus aureus- or lipopolysaccharide (LPS)-induced levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-8, IL-10, and regulated on activation, normal T cell expressed and secreted (RANTES) mRNA in MAC-T cells, in a manner expected to be unrelated to its antibacterial action. Moreover, S. aureus bacteriophage vB_SauM_JS25 suppressed the LPS-induced phosphorylation of nuclear factor (NF)-κB p65, which may represent an important mechanism mediating these effects. A carefully regulated balance between activation and inhibition by bacteriophages must be kept avoiding inappropriate inflammatory responses. The ability of vB_SauM_JS25 to influence the immune response highlights the potential development and application of bacteriophage-based therapies and may represent a novel anti-inflammatory therapeutic strategy

Insights Into the Bovine Milk Microbiota in Dairy Farms With Different Incidence Rates of Subclinical Mastitis

Bovine mastitis continues to be a complex disease associated with significant economic loss in dairy industries worldwide. The incidence rate of subclinical mastitis (IRSCM) can show substantial variation among different farms; however, the milk microbiota, which have a direct influence on bovine mammary gland health, have never been associated with the IRSCM. Here, we aimed to use high-throughput DNA sequencing to describe the milk microbiota from two dairy farms with different IRSCMs and to identify the predominant mastitis pathogens along with commensal or potential beneficial bacteria. Our study showed that Klebsiella, Escherichia–Shigella, and Streptococcus were the mastitis-causing pathogens in farm A (with a lower IRSCM), while Streptococcus and Corynebacterium were the mastitis-causing pathogens in farm B (with a higher IRSCM). The relative abundance of all pathogens in farm B (22.12%) was higher than that in farm A (9.82%). However, the genus Bacillus was more prevalent in farm A. These results may be helpful for explaining the lower IRSCM in farm A. Additionally, the gut-associated genera Prevotella, Ruminococcus, Bacteroides, Rikenella, and Alistipes were prevalent in all milk samples, suggesting gut bacteria can be one of the predominant microbial contamination in milk. Moreover, Listeria monocytogenes (a foodborne pathogen) was found to be prevalent in farm A, even though it had a lower IRSCM. Overall, our study showed complex diversity between the milk microbiota in dairy farms with different IRSCMs. This suggests that variation in IRSCMs may not only be determined by the heterogeneity and prevalence of mastitis-causing pathogens but also be associated with potential beneficial bacteria. In the future, milk microbiota should be considered in bovine mammary gland health management. This would be helpful for both the establishment of a targeted mastitis control system and the control of the safety and quality of dairy products