Keyframe image processing of semantic 3D point clouds based on deep learning

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a “semantic gap” between this and people's understanding of images. To address this “semantic gap,” a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques

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

Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects

The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6~0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data

Inversion of Nitrogen Concentration in Apple Canopy Based on UAV Hyperspectral Images

As the major nutrient affecting crop growth, accurate assessing of nitrogen (N) is crucial to precise agricultural management. Although improvements based on ground and satellite data nitrogen in monitoring crops have been made, the application of these technologies is limited by expensive costs, covering small spatial scales and low spatiotemporal resolution. This study strived to explore an effective approach for inversing and mapping the distributions of the canopy nitrogen concentration (CNC) based on Unmanned Aerial Vehicle (UAV) hyperspectral image data in a typical apple orchard area of China. A Cubert UHD185 imaging spectrometer mounted on a UAV was used to obtain the hyperspectral images of the apple canopy. The range of the apple canopy was determined by the threshold method to eliminate the effect of the background spectrum from bare soil and shadow. We analyzed and screened out the spectral parameters sensitive to CNC, including vegetation indices (VIs), random two-band spectral indices, and red-edge parameters. The partial least squares regression (PLSR) and backpropagation neural network (BPNN) were constructed to inverse CNC based on a single spectral parameter or a combination of multiple spectral parameters. The results show that when the thresholds of normalized difference vegetation index (NDVI) and normalized difference canopy shadow index (NDCSI) were set to 0.65 and 0.45, respectively, the canopy’s CNC range could be effectively identified and extracted, which was more refined than random forest classifier (RFC); the correlation between random two-band spectral indices and nitrogen concentration was stronger than that of other spectral parameters; and the BPNN model based on the combination of random two-band spectral indices and red-edge parameters was the optimal model for accurately retrieving CNC. Its modeling determination coefficient (R2) and root mean square error (RMSE) were 0.77 and 0.16, respectively; and the validation R2 and residual predictive deviation (RPD) were 0.75 and 1.92. The findings of this study can provide a theoretical basis and technical support for the large-scale, rapid, and non-destructive monitoring of apple nutritional status

Electrospun Nano‐CaO2/Polycaprolactone/Gelatin Nanofibers and Their Wound Healing Application through In Situ Supplying H2O2

Abstract Skin is highly susceptible to foreign pathogens when damaged. Infections usually lead to serious damage to wounds and hinder wound healing, which results in nonhealing and inflammation of the wounds. Herein, a kind of fibrous membrane incorporated with calcium peroxide nanoparticles (n‐CaO2) is fabricated by electrospinning the polymer blend solution of polycaprolactone and gelatin. The obtained membranes show a randomly distributed nanofibrous structure with n‐CaO2 particles embedded in each fiber. Such a structure endows the membranes with medium hydrophilicity and improves blood clotting in comparison with the commonly used gauze, while the loading of n‐CaO2 achieves the in situ generation and rapid release of hydrogen peroxide (H2O2), enabling FM‐10 to display a robust bacteria inhibition of over 90% elimination to E. coli. The in vitro cytocompatibility experiment on the fibrous membranes demonstrates low cytotoxicity to L929 fibroblasts. The in vivo assessment indicates that the fibrous membranes can distinctly accelerate the wound healing, of which FM‐5 exhibits the highest wound closure rate (>95%) after 14 days. This study provides a versatile approach, simply by varying the loading dosage of n‐CaO2 into fiber matrix, to fulfill different functions to meet the multiple needs of wound care

Maternal asthma and the risk of hypertensive disorders of pregnancy: a systematic review and meta-analysis of cohort studies

Objective: The aim of this study was to demonstrate the association between maternal asthma and the risk of hypertensive disorders of pregnancy. Methods: A systematic search of seven databases was conducted. A meta-analysis was performed to calculate risk ratios and 95% CI using random-effects models. Results: Asthma was associated with an increased risk of pregnancy-induced hypertension (RR 1.45, 95%CI 1.29–1.63), transient hypertension of pregnancy (RR 2.00, 95%CI 1.52–2.63), preeclampsia or eclampsia (RR 1.28, 95%CI 1.25–1.32), preeclampsia (RR 1.43, 95%CI 1.31–1.57) and eclampsia (RR 1.56, 95%CI 1.13–2.15). Conclusion: The meta-analysis illustrated that asthma was significantly increased risk of hypertension during pregnancy

CLAP: Gas Saturation Prediction in Shale Gas Reservoir Using a Cascaded Convolutional Neural Network–Long Short-Term Memory Model with Attention Mechanism

Gas saturation prediction is a crucial area of research regarding shale gas reservoirs, as it plays a vital role in optimizing development strategies and improving the efficiency of exploration efforts. Despite the advancements in deep learning techniques, accurately modeling the complex nonlinear relationships involved in gas saturation prediction remains a challenge. To address this issue, we propose a novel cascaded model, CLAP, combining convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) with an attention mechanism. It effectively captures and visualizes the intricate nonlinear relationships, enabling accurate gas saturation prediction in shale gas reservoirs. In this study, nine logging curves from 27 shale gas wells in the Changning area of the Sichuan Basin were used to train the CLAP model for predicting the gas saturation of the Wufeng-Longmaxi Formation shale. Compared to the Archie and random forest models, the CLAP model exhibited enhanced accuracy in predicting shale gas saturation. Promisingly, the CLAP model demonstrates outstanding statistical performance in gas saturation prediction, achieving an impressive R2 score of 0.762 and a mean square error (MSE) score of 0.934. These positive results highlight the effectiveness and potential utility of our proposed CLAP model in accurately predicting gas saturation in shale gas reservoirs. The application of deep learning techniques, such as CNNs, LSTM, and attention mechanisms, presents a promising avenue for further advancements in this field