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Background: Based on explainable DenseNet model, the therapeutic effects of optimization nursing on patients with acute left heart failure (ALHF) and its application values were discussed. Method: In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of Xuzhou Medical University were selected. According to different nursing methods, they were divided into conventional group and optimization group. Activity of daily living (ADL) scale was used to evaluate ADL of patients 6 months after discharge. Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to assess patients' psychological state. 45 min improvement rate, 60 min show efficiency, rescue success rate, and transfer rate were used to assess the effect of first aid. Likert 5-level scoring method was adopted to evaluate nursing satisfaction. Results: The optimization group showed shorter durations for first aid, hospitalization, electrocardiography, vein channel establishment, and blood collection compared to the conventional group. However, their SBP, DBP, and HR were inferior. On the other hand, LVEF and FS were significantly better in the optimization group. After nursing intervention, SAS and SDS scores were lower in the optimization group. Additionally, the optimization group had higher 45-minute improvement rates, 60-minute show efficiency, rescue success, and transfer rates. They also performed better in 6-minute walking distance and ADL scores 6 months post-discharge. The optimization group had better compliance, total effective rates, and satisfaction than the conventional group. Conclusion: It was demonstrated that explainable DenseNet model had application values in the diagnosis of ALHF. Optimization emergency method could effectively shorten the duration of first aid, relieve anxiety, and other adverse emotions, and improve rescue success rate and short-term efficacy. Nursing intervention has a positive impact on the total effective efficiency and patient satisfaction. [ABSTRACT FROM AUTHOR]

Polytetrafluoroethylene (PTFE) is used as commercial hydrophobic treatment for gas diffusion layers (GDL) in polymer electrolyte fuel cells. This commercial hydrophobic treatment can reduce the electrical conductivity of GDLs and is facing an uncertain future due to the pending restriction of perfluoroalkyl substances (PFAS). Previously, we proposed surfactant doped polyaniline (PANI) coatings as a fluorine-free alternative hydrophobic treatment. Due to their anti-corrosion properties as well as the electrical conductivity, these coatings offer additional benefits for the GDL compared to PTFE. Prior work demonstrated improved maximum power of a low temperature polymer electrolyte fuel cell (LT-PEFC) using the PANI coated GDL compared to the commercial PTFE treated reference. Based on these findings, additional investigations are needed to optimize the coating and assess possible areas of applications. With this study, we propose the use of the coating in high temperature PEFCs due to its thermal stability determined via thermogravimetric analysis of polyaniline doped with different types of surfactants. A main focus of this work is the investigation of the uniformity and overall porosity of the polyaniline coatings on GDLs via µCT supported by deep learning. This analysis is complemented with fluid dynamics simulations to determine the tortuosity and the gas flow through the GDL. In the future, this approach could enable the optimization of the fluorine-free hydrophobic coatings in combination with the different layers of the membrane electrode assembly (MEA) such as the GDL and the catalyst layer to prevent mass transport limitations. [ABSTRACT FROM AUTHOR]

This study investigates the prediction of the aerodynamic characteristics of Flettner rotors through three deep learning models. Various numbers of Flettner rotors, arrangements, and spin ratios are employed to consider these effects in the dataset. For the training of deep learning models, a dataset of aerodynamic force coefficients and flow fields is generated using Computational Fluid Dynamics (CFD). Three deep learning architectures (U-net, Encoder-Decoder, and Decoder models) are employed and trained to predict the aerodynamic characteristics of Flettner rotors. Three deep learning models are established through a training stage with a hyperparameter study and by altering the number of layers. The aerodynamic force coefficients and flow fields are predicted by established deep learning models and show small absolute errors compared to those from the CFD analysis. Moreover, predicted flow fields reflect the flow characteristics according to the difference of spin ratio and arrangement of Flettner rotors. In conclusion, the established deep learning models demonstrate rapid and robust predictions of aerodynamic force coefficients and flow fields for Flettner rotors under varying arrangements and spin ratios. Furthermore, a significant reduction in computational time is measured when comparing the analysis time of CFD simulations to the training and testing time of the deep learning models. [ABSTRACT FROM AUTHOR]

Smart grids are modernized, intelligent electricity distribution systems that integrate information and communication technologies to improve the efficiency, reliability, and sustainability of the electricity network. However, existing smart grids only integrate renewable energies when it comes to active demand management without taking into consideration the reduction of greenhouse gas emissions. This paper addresses this problem by forecasting CO2 emissions based on electricity consumption, making it possible to transition to renewable energies and thereby reduce CO2 emissions generated by fossil fuels. This approach contributes to the mitigation of climate change and the preservation of air quality, both of which are essential for a healthy and sustainable environment. To achieve this goal, we propose a transformerbased encoder architecture for load forecasting by modifying the transformer workflow and designing a novel technique for handling contextual features. The proposed solution is tested on real electricity consumption data over a long period. Results show that the proposed approach successfully handles time series data to detect future CO2 emissions excess and outperforms state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) of alfalfa is a crucial indicator of its growth status and a predictor of yield. The LAI of alfalfa is influenced by environmental factors, and the limitations of non-linear models in integrating these factors affect the accuracy of LAI predictions. This study explores the potential of classical non-linear models and deep learning for predicting alfalfa LAI. Initially, Logistic, Gompertz, and Richards models were developed based on growth days to assess the applicability of nonlinear models for LAI prediction of alfalfa. In contrast, this study combines environmental factors such as temperature and soil moisture, and proposes a time series prediction model based on mutation point detection method and encoder-attention-decoder BiLSTM network (TMEAD-BiLSTM). The model's performance was analyzed and evaluated against LAI data from different years and cuts. The results indicate that the TMEAD-BiLSTM model achieved the highest prediction accuracy (R² > 0.99), while the non-linear models exhibited lower accuracy (R² > 0.78). The TMEAD-BiLSTM model overcomes the limitations of nonlinear models in integrating environmental factors, enabling rapid and accurate predictions of alfalfa LAI, which can provide valuable references for alfalfa growth monitoring and the establishment of field management practices. [ABSTRACT FROM AUTHOR]

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. [ABSTRACT FROM AUTHOR]

Although permafrost degradation contributes significantly to hydrological change in cold regions, gaps remain in our understanding of streamflow variation induced by degrading permafrost in different river basins. We therefore used a deep learning model to simulate the long-term (⩾30 years) monthly streamflow at 60 hydrological stations along the Lena River, the third longest circum-Arctic river. Analyzing the effects of precipitation, temperature, and thaw depth on streamflow variation throughout the Lena River Basin, we identified two feedback patterns relating streamflow to warming permafrost, observed in areas of continuous and discontinuous permafrost. In northern plain regions with continuous permafrost, 94% of stations presented an increasing trend in annual streamflow from the 1900s to the 2010s due to permafrost degradation. The enhanced streamflow was mainly due to increased meltwater in the flood season. In southern regions covered by both continuous and discontinuous permafrost, approximately 38% of stations exhibited a declining trend in annual streamflow in response to permafrost degradation, with a high proportion (61%) located in mountain regions (elevation ⩾ 500 m). The decline is attributed to the enhanced infiltration capacity of thawing frozen layers within discontinuous permafrost regions. Our study provides new insights into the mechanisms behind permafrost degradation-induced streamflow variation and highlights the importance of formulating tailored strategies for sustainable river management in cold regions experiencing climate change. [ABSTRACT FROM AUTHOR]

Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map interpolation for online posture prediction. Splines present a promising alternative, capable of mitigating these issues while maintaining computational efficiency. This paper delves into the efficiency disparities between B-Spline surface mapping and discretized cell-based approaches, such as grid mapping, within indoor environments. B-Spline Online SLAM and FastSLAM, utilizing Rao-Blackwellized Particle Filter (RBPF), are employed to achieve range-based mapping of the unknown 2D environment. The system incorporates deep learning networks in the B-Spline curve estimation process to compute parameterizations and knot vectors. The research implementation utilizes the Intel Research Lab benchmark dataset to conduct a comprehensive qualitative and quantitative analysis of both approaches. The B-Spline surface approach demonstrates significantly superior performance, evidenced by low error metrics, including an average squared translational error of 0.0016 and an average squared rotational error of 1.137. Additionally, comparative analysis with Vision Benchmark Suite demonstrates robustness across different environments, highlighting the effectiveness of B-Spline SLAM for real-world applications. [ABSTRACT FROM AUTHOR]

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446–0.537. The R2 of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Sentiment analysis often faces challenges like manual labeling, sarcasm detection, and imbalanced class labels. Using Twitter/X data for sentiment analysis is resource-intensive due to manual labeling. The BERT model is adequate for Indonesian sentiment analysis, but sarcasm remains challenging. This research evaluates the performance of BERT, LSTM, and BERT-LSTM models for classifying sarcastic text data, specifically in floodrelated posts from Indonesia. We used Twitter/X data from December 19, 2023, to January 13, 2024, labeled by three annotators. We handle imbalanced data using techniques like Random Undersampling, SMOTE, and SMOTETomek. We assessed model performance with ANOVA based on balance-weighted accuracy. The BERT and BERT-LSTM models excelled, achieving balanceweighted accuracy values of 98.61% and 98.06%, respectively. This research advances sentiment analysis methods, particularly for natural disaster contexts in Indonesia. [ABSTRACT FROM AUTHOR]

Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (P

Yuxiao Yang,1,2 Xingyuan Gao,3 Hongmei Liang,4 Qiuying Yang1,2 1School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, People’s Republic of China; 3Design Department, Beijing HANHAIZHONGJIA Hydraulic Machinery Co., Ltd, Beijing, People’s Republic of China; 4Nursing Department, China Railway 17th Bureau Group Central Hospital, Taiyuan, People’s Republic of ChinaCorrespondence: Qiuying Yang, School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13691283439, Email yangqiuying@ccmu.edu.cnPurpose: AIDS presents serious harms to public health worldwide. In this paper, we used five single models: ARIMA, SARIMA, Prophet, BP neural network, and LSTM method to model and predict the number of monthly AIDS incidence cases and mortality cases in China. We have also proposed the LSTM-SARIMA combination model to enhance the accuracy of the prediction. This study provides strong data support for the prevention and treatment of AIDS.Methods: We collected data on monthly AIDS incidence cases and mortality cases in China from January 2010 to February 2024. Among them, for modeling, we used data from January 2010 to February 2021 and the rest for validation. Treatments were applied to the dataset based on its characteristics during modeling. All models in our study were performed using Python 3.11.6. Meanwhile, we used the constructed model to predict monthly incidence and mortality cases from March 2024 to July 2024. We then evaluated our prediction results using RMSE, MAE, MAPE, and SMAPE.Results: The deep learning methods of LSTM and BPNN outperform ARIMA, SARIMA, and Prophet in predicting the number of mortality cases. When predicting the number of AIDS incidence cases, there is little difference between the two types of methods, and the LSTM method performs slightly better than the rest of the methods. Meanwhile, the average error in predicting AIDS mortality cases is significantly lower than in predicting AIDS incidence cases. The LSTM-SARIMA method outperforms other methods in predicting AIDS incidence and mortality.Conclusion: Due to the different characteristics of the AIDS incidence and mortality cases series, the performance of distinct methods is slightly different. The AIDS mortality series is smoother than the incidence series. The combined LSTM-SARIMA model outperforms the traditional method in prediction and the LSTM method alone, which is of practical significance for optimizing the prediction results of AIDS.Keywords: AIDS, ARIMA model, Prophet model, deep learning model, LSTM-SARIMA combination model

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included, and 13 470 infrared pupil images were collected for the study. All infrared images for pupil segmentation were labeled using the Labelme software. The computation of pupil diameter is divided into four steps: image pre-processing, pupil identification and localization, pupil segmentation, and diameter calculation. Two major models are used in the computation process: the modified YoloV3 and Deeplabv3+ models, which must be trained beforehand.RESULTS:The test dataset included 1 348 infrared pupil images. On the test dataset, the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils. The DeeplabV3+ model achieved a background intersection over union(IOU)of 99.23%, a pupil IOU of 93.81%, and a mean IOU of 96.52%. The pupil diameters in the test dataset ranged from 20 to 56 pixels, with a mean of 36.06±6.85 pixels. The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels, with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm, proven to be highly accurate and reliable for clinical application.

Abstract NASICON (Na $$_{1+x}$$ 1 + x Zr $$_2$$ 2 Si $$_x$$ x P $$_{3-x}$$ 3 - x O $$_{12}$$ 12 ) is a well-established solid-state electrolyte, renowned for its high ionic conductivity and excellent chemical stability, rendering it a promising candidate for solid-state batteries. However, the intricate influence of ion doping on their performance has been a central focus of research, with existing studies often lacking comprehensive evaluation methods. This study introduces a deep-learning-based approach to efficiently evaluate ion-doped NASICON materials. We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation. The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model’s predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations. Graphical Abstract

Abstract Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is commonly employed for screening, yet its effectiveness is hindered by challenges like weak contrast, speckle noise, and hazy boundaries in images. This study proposes an adaptive deep learning-based segmentation technique using a database of ovarian ultrasound cyst images. A Guided Trilateral Filter (GTF) is applied for noise reduction in pre-processing. Segmentation utilizes an Adaptive Convolutional Neural Network (AdaResU-net) for precise cyst size identification and benign/malignant classification, optimized via the Wild Horse Optimization (WHO) algorithm. Objective functions Dice Loss Coefficient and Weighted Cross-Entropy are optimized to enhance segmentation accuracy. Classification of cyst types is performed using a Pyramidal Dilated Convolutional (PDC) network. The method achieves a segmentation accuracy of 98.87%, surpassing existing techniques, thereby promising improved diagnostic accuracy and patient care outcomes.

Abstract Treatment-induced ototoxicity and accompanying hearing loss are a great concern associated with chemotherapeutic or antibiotic drug regimens. Thus, prophylactic cure or early treatment is desirable by local delivery to the inner ear. In this study, we examined a novel way of intratympanically delivered sustained nanoformulation by using crosslinked hybrid nanoparticle (cHy-NPs) in a thermoresponsive hydrogel i.e. thermogel that can potentially provide a safe and effective treatment towards the treatment-induced or drug-induced ototoxicity. The prophylactic treatment of the ototoxicity can be achieved by using two therapeutic molecules, Flunarizine (FL: T-type calcium channel blocker) and Honokiol (HK: antioxidant) co-encapsulated in the same delivery system. Here we investigated, FL and HK as cytoprotective molecules against cisplatin-induced toxic effects in the House Ear Institute - Organ of Corti 1 (HEI-OC1) cells and in vivo assessments on the neuromast hair cell protection in the zebrafish lateral line. We observed that cytotoxic protective effect can be enhanced by using FL and HK in combination and developing a robust drug delivery formulation. Therefore, FL-and HK-loaded crosslinked hybrid nanoparticles (FL-cHy-NPs and HK-cHy-NPs) were synthesized using a quality-by-design approach (QbD) in which design of experiment-central composite design (DoE-CCD) following the standard least-square model was used for nanoformulation optimization. The physicochemical characterization of FL and HK loaded-NPs suggested the successful synthesis of spherical NPs with polydispersity index 75%), drugs loading (~ 10%), stability (> 2 months) in the neutral solution, and appropriate cryoprotectant selection. We assessed caspase 3/7 apopototic pathway in vitro that showed significantly reduced signals of caspase 3/7 activation after the FL-cHy-NPs and HK-cHy-NPs (alone or in combination) compared to the CisPt. The final formulation i.e. crosslinked-hybrid-nanoparticle-embedded-in-thermogel was developed by incorporating drug-loaded cHy-NPs in poloxamer-407, poloxamer-188, and carbomer-940-based hydrogel. A combination of artificial intelligence (AI)-based qualitative and quantitative image analysis determined the particle size and distribution throughout the visible segment. The developed formulation was able to release the FL and HK for at least a month. Overall, a highly stable nanoformulation was successfully developed for combating treatment-induced or drug-induced ototoxicity via local administration to the inner ear. Graphical Abstract

Highlights Three dimensional (3D) tubular braided composites are widely used in various industries due to their excellent mechanical properties and lightweight characteristics. However, traditional numerical and experimental methods face challenges in predicting mechanical properties quickly and accurately due to factors such as ambient temperature, component materials, and geometric parameters. To address this issue, this paper combines deep neural networks (DNN) and two‐scale finite element analysis to accelerate the solution speed. The dataset is first obtained through a two‐scale finite element model with temperature based on micro‐CT. Then, the mapping model of macroscopic compression elastic properties and the influencing factors of material properties is established by DNN and Bayesian Optimization with Hyperband (BOHB) hyperparameter optimization algorithm. The rapid prediction of axial compression elastic properties of 3D tubular braided composites under different ambient temperatures, component materials, porosities, braiding angles and fiber volume contents is achieved. Finally, the accuracy of the predicted results of the constructed model is verified by experiments. A BOHB optimized deep learning model coupled with a finite element framework is proposed Fast prediction of elastic properties of 3D tubular braided composites at different temperatures The accuracy of the prediction results of the constructed model is verified by experiments [ABSTRACT FROM AUTHOR]

Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored. Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall's performance over time. Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy. Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments. [ABSTRACT FROM AUTHOR]

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time. [ABSTRACT FROM AUTHOR]

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China's coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks. [ABSTRACT FROM AUTHOR]

Background: Aging is marked by the gradual deterioration of cells, tissues, and organs and is a major risk factor for many chronic diseases. Considering the complex mechanisms of aging, traditional Chinese medicine (TCM) could offer distinct advantages. However, due to the complexity and variability of metabolites in TCM, the comprehensive screening of metabolites associated with pharmacology remains a significant issue. Methods: A reliable and integrated identification method based on UPLC-Q Exactive-Orbitrap HRMS was established to identify the chemical profiles of Huan Shao Dan (HSD). Then, based on the theory of sequential metabolism, the metabolic sites of HSD in vivo were further investigated. Finally, a deep learning model and a bioactivity assessment assay were applied to screen potential anti-aging metabolites. Results: This study identified 366 metabolites in HSD. Based on the results of sequential metabolism, 135 metabolites were then absorbed into plasma. A total of 178 peaks were identified from the sample after incubation with artificial gastric juice. In addition, 102 and 91 peaks were identified from the fecal and urine samples, respectively. Finally, based on the results of the deep learning model and bioactivity assay, ginsenoside Rg1, Rg2, and Rc, pseudoginsenoside F11, and jionoside B1 were selected as potential anti-aging metabolites. Conclusion: This study provides a valuable reference for future research on the material basis of HSD by describing the chemical profiles both in vivo and in vitro. Moreover, the proposed screening approach may serve as a rapid tool for identifying potential anti-aging metabolites in TCM. [ABSTRACT FROM AUTHOR]

AbstractDeep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics. [ABSTRACT FROM AUTHOR]

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries. [ABSTRACT FROM AUTHOR]

Cerebral hyperperfusion occurs in some patients after superficial temporal artery–middle cerebral artery bypass surgery. However, there is uncertainty about cerebral hyperperfusion after bypass for patients with different Circle of Willis (CoW) structures. This study established a lumped–parameter model coupled with one–dimensional model (0–1D), whilst a deep learning model for predicting pressure drop (DLM–PD) caused by stenosis and a cerebral autoregulation model (CAM) were introduced into the model. Based on this model, 9 CoW structural models before and after bypass were constructed, to investigate the effects of different CoW structures on cerebral hyperperfusion after bypass. The model and the results were further validated by clinical data. The MSE of mean flow rates from 0–1D model calculation and from clinical measurement was 1.4%. The patients exhibited hyperperfusion in three CoW structures after bypass: missing right anterior segment of the anterior cerebral artery (mRACA1) (13.96% hyperperfusion); mRACA1 and foetal–type right anterior segment of posterior cerebral artery (12.81%), and missing anterior communicating artery and missing left posterior communicating artery (112.41%). The error between the average flow ratio from the model calculations and from clinical measurements was less than 5%. This study demonstrated that the CoW structure had a significant impact on hyperperfusion after bypass. The general 0–1D model coupled with DLM–PD and CAM proposed in this study, could accurately simulate the hemodynamic environment of different CoW structures before and after bypass, which might help physicians identify high–risk patients with hyperperfusion before surgery, and promote the development of non–invasive diagnosis and treatment of cerebrovascular diseases. [ABSTRACT FROM AUTHOR]

The diagnosis of Diabetic Retinopathy (DR) demands a paradigm shift towards more accurate and efficient solutions to overcome vision impairment. Therefore, the current study introduces a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework approach for DR diagnosis. The proposed framework leverages the power of deep learning and provides a dynamic and robust solution for the early detection of DR, which in turn preserves a patient's vision. The proposed framework uses a DR Debrecen Dataset from the UCI database with 21 distinct features relevant to retinal health, and employs a series of data preprocessing steps, including data cleaning, normalisation, and transformation, to ensure data quality and compatibility. The MR-LSTM framework excels at capturing temporal dependencies in sequential retinal images, offering a unique advantage in understanding the progression of DR. The MR-LSTM framework is implemented using Python libraries, and the results are compared with those of other popular models. It is observed that the MR-LSTM framework outperforms other models and achieves an accuracy of 97.12 percent and an F1 Score of 98.49. Furthermore, the Receiver Operating Characteristic (ROC) curve reveals an area under the curve of 0.97, highlighting the exceptional ability to discriminate between positive and negative cases of the proposed framework. By revolutionising DR diagnosis with the proposed MR-LSTM framework, it can achieve accurate, timely, and accessible solutions in the fight against vision-threatening conditions. [ABSTRACT FROM AUTHOR]

Millet is one of the major coarse grain crops in China. Its geographical origin and Fusarium fungal contamination with ergosterol and deoxynivalenol have a direct impact on food quality, so the rapid prediction of the geographical origins and fungal toxin contamination is essential for protecting market fairness and consumer rights. In this study, 600 millet samples were collected from twelve production areas in China, and traditional algorithms such as random forest (RF) and support vector machine (SVM) were selected to compare with the deep learning models for the prediction of millet geographical origin and toxin content. This paper firstly develops a deep learning model (wavelet transformation-attention mechanism long short-term memory, WT-ALSTM) by combining hyperspectral imaging to achieve the best prediction effect, the wavelet transformation algorithm effectively eliminates noise in the spectral data, while the attention mechanism module improves the interpretability of the prediction model by selecting spectral feature bands. The integrated model (WT-ALSTM) based on selected feature bands achieves optimal prediction of millet origin, with its accuracy exceeding 99% on both the training and prediction datasets. Meanwhile, it achieves optimal prediction of ergosterol and deoxynivalenol content, with the coefficient of determination values exceeding 0.95 and residual predictive deviation values reaching 3.58 and 3.38 respectively, demonstrating excellent model performance. The above results suggest that the combination of hyperspectral imaging with a deep learning model has great potential for rapid quality assessment of millet. This study provides new technical references for developing portable and rapid hyperspectral imaging inspection technology for on-site assessment of agricultural product quality in the future. [ABSTRACT FROM AUTHOR]

NASICON (Na 1 + x Zr 2 Si x P 3 - x O 12 ) is a well-established solid-state electrolyte, renowned for its high ionic conductivity and excellent chemical stability, rendering it a promising candidate for solid-state batteries. However, the intricate influence of ion doping on their performance has been a central focus of research, with existing studies often lacking comprehensive evaluation methods. This study introduces a deep-learning-based approach to efficiently evaluate ion-doped NASICON materials. We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation. The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model's predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations. [ABSTRACT FROM AUTHOR]

In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna.v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual's overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system's usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna.v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients' engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to construct an artificial intelligence‐enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low‐voltage areas (LVAs) in patients with persistent atrial fibrillation. Methods: The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12‐lead ECGs obtained before the ablation procedures were performed. Artificial intelligence‐based algorithms were used to construct models for predicting the presence of LVAs. The DR‐FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model performance. Results: The data obtained from the participants were split into training (n = 469), validation (n = 58), and test sets (n = 60). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR‐FLASH score (AUROC = 0.610) and the APPLE score (AUROC = 0.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG‐based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of 0.935. Conclusion: The deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR‐FLASH and the APPLE risk scores. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day‐to‐day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi‐LSTM RAO) to classify sentiment of tweets. Initially, data is obtained from Twitter API, Sentiment 140 and Stanford Sentiment Treebank‐2 (SST‐2). The raw data is pre‐processed and it is subjected to feature extraction which is performed using Bag of Words (BoW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). The feature selection is performed using Gazelle Optimization Algorithm (GOA) which removes the irrelevant or redundant features that maximized model performance and classification is performed using Bi LSTM–RAO. The RAO optimizes the loss function of Bi‐LSTM model that maximized accuracy. The classification accuracy of proposed method for Twitter API, Sentiment 140 and SST 2 dataset is obtained as 909.44%, 99.71% and 99.86%, respectively. These obtained results are comparably higher than ensemble framework, Robustly Optimized BERT and Gated Recurrent Unit (RoBERTa‐GRU), Logistic Regression‐Long Short Term Memory (LR‐LSTM), Convolutional Bi‐LSTM, Sentiment and Context Aware Attention‐based Hybrid Deep Neural Network (SCA‐HDNN) and Stochastic Gradient Descent optimization based Stochastic Gate Neural Network (SGD‐SGNN). [ABSTRACT FROM AUTHOR]

Fibre bundle (FB)‐based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in‐house fabricated high‐NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB‐based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB‐based fluorescence microscopes. After network training, the GAN model, employing image‐to‐image translation techniques, effectively transformed wide‐field images into high‐resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN‐generated outputs significantly enhanced both contrast and resolution compared to the original wide‐field images. These findings highlight the potential of GAN‐based models trained using MSIM data to enhance resolution and contrast in wide‐field imaging for fibre bundle‐based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high‐NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB‐based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN‐based models for fibre bundle‐based fluorescence microscopy. [ABSTRACT FROM AUTHOR]

Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions. [ABSTRACT FROM AUTHOR]

Intravenous fluid bags are essential in hospitals, but foreign particles can contaminate them during mass production, posing significant risks. Although produced in sanitary environments, contamination can cause severe problems if products reach consumers. Traditional inspection methods struggle with the flexible nature of these bags, which deform easily, complicating particle detection. Recent deep learning advancements offer promising solutions in regard to quality inspection, but high-resolution image processing remains challenging. This paper introduces a real-time deep learning-based inspection system addressing bag deformation and memory constraints for high-resolution images. The system uses object-level background rejection, filtering out objects similar to the background to isolate moving foreign particles. To further enhance performance, the method aggregates object patches, reducing unnecessary data and preserving spatial resolution for accurate detection. During aggregation, candidate objects are tracked across frames, forming tracks re-identified as bubbles or particles by the deep learning model. Ensemble detection results provide robust final decisions. Experiments demonstrate that this system effectively detects particles in real-time with over 98% accuracy, leveraging deep learning advancements to tackle the complexities of inspecting flexible fluid bags. [ABSTRACT FROM AUTHOR]

The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy, significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model. Finally, based on segmented blade surface defect images, a method for quantifying blade defects is proposed. This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade. Test results show that the improved Deeplabv3+ deep learning model reduces training time by approximately 43.03% compared to the original model, while achieving mAP and MIoU values of 96.87% and 96.93%, respectively. Moreover, it demonstrates robustness in detecting different surface defects on blades across different backgrounds. The application of a blade surface defect quantification method enables the precise quantification of different defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade. This method enables non-contact, long-distance, high-precision detection and quantification of surface defects on the blades, providing a reference for assessing surface defects on wind turbine blades. Graphic Abstract [ABSTRACT FROM AUTHOR]

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Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is commonly employed for screening, yet its effectiveness is hindered by challenges like weak contrast, speckle noise, and hazy boundaries in images. This study proposes an adaptive deep learning-based segmentation technique using a database of ovarian ultrasound cyst images. A Guided Trilateral Filter (GTF) is applied for noise reduction in pre-processing. Segmentation utilizes an Adaptive Convolutional Neural Network (AdaResU-net) for precise cyst size identification and benign/malignant classification, optimized via the Wild Horse Optimization (WHO) algorithm. Objective functions Dice Loss Coefficient and Weighted Cross-Entropy are optimized to enhance segmentation accuracy. Classification of cyst types is performed using a Pyramidal Dilated Convolutional (PDC) network. The method achieves a segmentation accuracy of 98.87%, surpassing existing techniques, thereby promising improved diagnostic accuracy and patient care outcomes. [ABSTRACT FROM AUTHOR]