Association between pregnancy and pregnancy loss with COPD in Chinese women: The China Kadoorie Biobank study

Background Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease characterized by airflow blockage. Pregnancy and pregnancy loss may be related to an elevated risk of COPD, although studies have yet to report on this association. Hence, this study aims to investigate the association between pregnancy and pregnancy loss with the risk of COPD among Chinese women. Methods Data on 302,510 female participants from the China Kadoorie Biobank were utilized for this study. Multivariable logistic regression, stratified by sociodemographic and lifestyle factors, was employed to obtain the odds ratio (ORs) and 95% confidence intervals (CIs) for the association between pregnancy and pregnancy loss with COPD. Results Pregnancy loss was significantly associated with increased risk of COPD (OR 1.19, 95% CI 1.13–1.25), specifically, spontaneous (OR 1.19, 95% CI 1.11–1.29) and induced abortion (OR 1.18, 95% CI 1.12–1.25). Stillbirth, however, was not significantly associated with the risk of COPD (OR 1.09, 95% CI 0.99–1.20). Increasing number of pregnancy losses was associated with increasing risk of COPD (one pregnancy loss: OR 1.14, 95% CI 1.07–1.21, two or more pregnancy loss: OR 1.25, 95% CI 1.17–1.32, and each additional pregnancy loss: OR 1.06, 95% CI 1.03–1.09). A single pregnancy was significantly associated with reduced risk of COPD (OR 0.75, 95% CI 0.59–0.97), although each additional pregnancy was significantly associated with increased risk of COPD (OR 1.03, 95% CI 1.01–1.04). Conclusion Pregnancy loss, in particular, spontaneous and induced abortions are associated with increased risk of COPD among Chinese women. A single pregnancy, however, demonstrated protective effects

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Physical-Layer Security for UAV-Assisted Air-to-Underwater Communication Systems with Fixed-Gain Amplify-and-Forward Relaying

We analyze a secure unmanned aerial vehicle-assisted two-hop mixed radio frequency (RF) and underwater wireless optical communication (UWOC) system using a fixed-gain amplify-and-forward (AF) relay. The UWOC channel was modeled using a mixture exponential-generalized Gamma distribution to consider the combined effects of air bubbles and temperature gradients on transmission characteristics. Both legitimate and eavesdropping RF channels were modeled using flexible α-μ distributions. Specifically, we first derived both the probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio of the system. Based on the PDF and CDF expressions, we derived the closed-form expressions for the tight lower bound of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity (PNZ), which are both expressed in terms bivariate Fox’s H-function. To utilize these analytical expressions, we derived asymptotic expressions of SOP and PNZ using only well-known functions. We also used asymptotic expressions to determine the suboptimal transmitting power to maximize energy efficiency. Furthermore, we investigated the effect of levels of air bubbles and temperature gradients in the UWOC channel, and studied the nonlinear characteristics of the transmission medium and the number of multipath clusters of the RF channel on the secrecy performance. Finally, all analyses were validated using a simulation

IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples

Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attention mechanism to enhance feature representation. The model is optimized by employing the IBMax module to increase the receptive field and adding the HardSwish function to the ConvBN layer to improve stability and speed. To address the challenge of poor generalization of models trained on public datasets to real environment datasets, we developed an improved PlantDoc++ dataset and utilized transfer learning to pre-train the model on PDDA and PlantVillage datasets. The results indicate that after pre-training on the PDDA dataset, IBSA_Net achieved a test accuracy of 0.946 on a real environment dataset, with an average precision, recall, and F1-score of 0.942, 0.944, and 0.943, respectively. Additionally, the effectiveness of IBSA_Net in other crops is verified. This study provides a dependable and effective method for recognizing tomato leaf diseases in real agricultural production environments, with the potential for application in other crops

LBFNet: A Tomato Leaf Disease Identification Model Based on Three-Channel Attention Mechanism and Quantitative Pruning

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network (LBFNet) is proposed in this paper. First, LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996 s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning enables the model accuracy to reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers

Physical-Layer Security for UAV-Assisted Air-to-Underwater Communication Systems with Fixed-Gain Amplify-and-Forward Relaying

We analyze a secure unmanned aerial vehicle-assisted two-hop mixed radio frequency (RF) and underwater wireless optical communication (UWOC) system using a fixed-gain amplify-and-forward (AF) relay. The UWOC channel was modeled using a mixture exponential-generalized Gamma distribution to consider the combined effects of air bubbles and temperature gradients on transmission characteristics. Both legitimate and eavesdropping RF channels were modeled using flexible α-μ distributions. Specifically, we first derived both the probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio of the system. Based on the PDF and CDF expressions, we derived the closed-form expressions for the tight lower bound of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity (PNZ), which are both expressed in terms bivariate Fox’s H-function. To utilize these analytical expressions, we derived asymptotic expressions of SOP and PNZ using only well-known functions. We also used asymptotic expressions to determine the suboptimal transmitting power to maximize energy efficiency. Furthermore, we investigated the effect of levels of air bubbles and temperature gradients in the UWOC channel, and studied the nonlinear characteristics of the transmission medium and the number of multipath clusters of the RF channel on the secrecy performance. Finally, all analyses were validated using a simulation

A Data-Driven and Knowledge-Driven Method towards the IRP of Modern Logistics

Inventory Routing Problem (IRP) is a typical optimization problem in logistics. To reduce the total cost, which contains the product transportation cost, the inventory holding cost, the customer satisfaction cost, etc., a wide range of impact factors have to be taken into consideration. Since more and more intelligent devices have been adopted in the management of modern logistics, the amount of the collected data (relevant to those impact factors) increases exponentially. However, the quality of the collected data is suffering from a certain number of uncertainties, such as device status and the transmission network environment. Considering the volume and quality of the collected data, the traditional data-driven distribution optimization methods encounter a bottleneck. In this paper, we propose a hybrid optimization method which combines data-driven and knowledge-driven techniques together. In our method, a domain ontology, which has better scalability and generality, is built as an extension of data-driven optimization algorithms. Knowledge reasoning techniques are also combined to handle data quality issue and uncertainties. To evaluate the performance of our method, we carried out a case study, which is provided by a French company “Pierre Fabre Dermo-Cosmetics” (PFDC). This case study is a simplified scenario of the practical business process of PFDC