Using simulated Tianqin gravitational wave data and electromagnetic wave data to study the coincidence problem and Hubble tension problem

In this paper, we use electromagnetic wave data (H0LiCOW, $H(z)$, SNe) and gravitational wave data (Tianqin) to constrain the interacting dark energy (IDE) model and investigate the Hubble tension problem and coincidences problem. By combining these four kinds of data (Tianqin+H0LiCOW+SNe+$H(z)$), we obtained the parameter values at the confidence interval of $1\sigma$: $\Omega_m=0.36\pm0.18$, $\omega_x=-1.29^{+0.61}_{-0.23}$, $\xi=3.15^{+0.36}_{-1.1}$, and $H_0=70.04\pm0.42$ $kms^{-1}Mpc^{-1}$. According to our results, the best valve of $H_0$ show that the Hubble tension problem can be alleviated to some extent. In addition, the $\xi+3\omega_x = -0.72^{+2.19}_{-1.19}(1\sigma)$ of which the center value indicates the coincidence problem is slightly alleviated. However, the $\xi+3\omega_x = 0$ is still within the $1\sigma$ error range which indicates the $\Lambda$CDM model is still the model which is in best agreement with the observational data at present. Finally, we compare the constraint results of electromagnetic wave and gravitational wave on the model parameters and find that the constraint effect of electromagnetic wave data on model parameters is better than that of simulated Tianqin gravitational wave data.Comment: The article has been accepted by Chinese Physics

An analysis on the sensibility of casing vibration signal and its application to aero-hydraulic pump

Aero-hydraulic pump is a central part of hydraulic system in an aircraft. Acceleration sensors are installed in the axis, tangential and vertical direction for identifying the weak imbalance fault, and meanwhile analysis is made for the sensibility of weak imbalance fault from different direction acceleration signal. The result shows that the signal from vertical acceleration sensor is the most sensitive and the one from axis acceleration sensor is the least sensitive to identify and diagnose weak imbalance fault of aero-hydraulic pump

A study on the diagnosis of compound faults in rolling bearings based on ITD-SVD

Considering the difficulty in the diagnosis of compound faults in rolling bearings, the paper combines Intrinsic Time-scale Decomposition (ITD) and Singular Value Decomposition (SVD) for extracting the characteristics of compound faults from rolling bearings. Rotational components obtained from ITD decomposition are denoised according to Singular Value Decomposition algorithm; signal is reconstructed by denoised rotational components; at last, characteristics of compound faults of rolling bearings are extracted by Hilbert spectrum envelope of reconstructed signal. In validation, the paper has made a comparative study on the proposed ITD-SVD method and conventional one based on ITD algorithm and PCA method, and the result shows that ITD-SVD method works better on noise control and thereby provides more precise extraction of characteristic frequency of compound faults from rolling bearings of aero-engine

Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments

Cotton, a vital textile raw material, is intricately linked to people’s livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices

A method to diagnose compound fault of rolling bearing with ITD-AF

In an engineering practice, the faults of rolling bearing are mostly represented as being compound and hard to diagnose. For that, intrinsic time-scale decomposition (ITD) algorithm was combined with Auto-correlation Function (AF) to extract the characteristics of compound faults of rolling bearing in aviation engine. Firstly, ITD algorithm was used to decompose acceleration signal into multiple rotational and residual trend component; secondly, rotational components were reconstructed to figure out their AF; finally, characteristic frequency of rolling bearing under compound faults mode was extracted by Hilbert spectrum envelope. To validate the effectiveness of the method, a comparative study on sensor installation positions and vibration acceleration signal of different compound faults has been carried out. The result of study shows that the proposed ITD-AF method is capable to extract compound fault characteristics of rolling bearing in an effective and precise manner and the installation positions of sensors, rotation speed and fault type shows insensitivity to extraction

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We promise to release all code & datasets for future research

Functional plasticity of neutrophils after low- or high-dose irradiation in cancer treatment – A mini review

Over the last several decades, radiotherapy has been considered the primary treatment option for a broad range of cancer types, aimed at prolonging patients’ survival and slowing down tumor regression. However, therapeutic outcomes of radiotherapy remain limited, and patients suffer from relapse shortly after radiation. Neutrophils can initiate an immune response to infection by releasing cytokines and chemokines to actively combat pathogens. In tumor immune microenvironment, tumor-derived signals reprogram neutrophils and induce their heterogeneity and functional versatility to promote or inhibit tumor growth. In this review, we present an overview of the typical phenotypes of neutrophils that emerge after exposure to low- and high-dose radiation. These phenotypes hold potential for developing synergistic therapeutic strategies to inhibit immunosuppressive activity and improve the antitumor effects of neutrophils to render radiation therapy as a more effective strategy for cancer patients, through tumor microenvironment modulation

A novel method of weakness imbalance fault identification and application in aero-hydraulic pump

A method of combining auto-correlation and Hilbert envelope analysis is proposed and used to identify weakness imbalance fault of aero-hydraulic pump, the central part of hydraulic system of aircraft. Firstly, the integral and polynomial least square fitting is applied to convert acceleration signal to velocity one; secondly, the Hilbert envelope spectrum of auto-correlation function of velocity signal is obtained and used to identify the weakness imbalance fault of aero-hydraulic pump; finally, the energy ratio of velocity signal is calculated according to Hilbert envelope spectrum for identifying imbalance fault of aero-hydraulic pump by means of easier and more visual method. Meanwhile, the comparing analysis is carried out between traditional research method and proposed new one. The result shows that the weakness imbalance fault of aero-hydraulic pump can be identified and diagnosed effectively and correctly according to the velocity signal whether Hilbert envelope spectrum or calculation energy ratio while direct acceleration signal cannot

Fluid Retention Caused by Rosiglitazone Is Related to Increases in AQP2 and α

Peroxisome proliferator activated receptor-γ (PPARγ) is a ligand-activated transcription factor of the nuclear hormone receptor superfamily. The decreased phosphorylation of PPARγ due to rosiglitazone (ROS) is the main reason for the increased insulin sensitivity caused by this antidiabetic drug. However, there is no clear evidence whether the nuclear translocation of p-PPARγ stimulated by ROS is related to fluid retention. It is also unclear whether the translocation of p-PPARγ is associated with the change of aquaporin-2 (AQP2) and epithelial sodium channel α subunit (αENaC) in membranes, cytoplasm, and nucleus. Our experiments indicate that ROS significantly downregulates nuclear p-PPARγ and increases membrane AQP2 and αENaC; however, SR1664 (a nonagonist PPARγ ligand) reduces p-PPARγ and has no effect on AQP2 and αENaC. Therefore, we conclude that in vitro the fluid retention caused by ROS is associated with the increases in membrane αENaC and AQP2 but has little relevance to the phosphorylation of PPARγ

The work of Chinese chronic conditions: adaptation and validation of the Distribution of Co-Care Activities Scale

PurposeThe Distribution of Co-Care Activities Scale was adapted into Chinese for the purposes of this study, and then the psychometric characteristics of the Chinese version of the DoCCA scale were confirmed in chronic conditions.MethodsA total of 434 patients with chronic diseases were recruited from three Chinese cities. A cross-cultural adaptation procedure was used to translate the Distribution of Co-Care Activities Scale into Chinese. Cronbach's alpha coefficient, split-half reliability, and test-retest reliability were used to verify the scale's reliability. Content validity indices, exploratory factor analysis, and confirmatory factor analysis were used to confirm the scale's validity.ResultsThe Chinese DoCCA scale includes five domains: demands, unnecessary tasks, role clarity, needs support, and goal orientation. The S-CVI was 0.964. Exploratory factor analysis yielded a five-factor structure that explained 74.952% of the total variance. According to the confirmatory factor analysis results, the fit indices were within the range of the reference values. Convergent and discriminant validity both met the criteria. Also, the scale's Cronbach's alpha coefficient is 0.936, and the five dimensions' values range from 0.818 to 0.909. The split-half reliability was 0.848, and the test-retest reliability was 0.832.ConclusionsThe Chinese version of the Distribution of Co-Care Activities Scale had high levels of validity and reliability for chronic conditions. The scale can assess how patients with chronic diseases feel about their service of care and provide data to optimize their personalized chronic disease self-management strategies