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Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

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Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

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This study introduced a depth-sensing-based approach with robust algorithms for tracking relative morphological changes in the chests of patients undergoing physical therapy. The problem that was addressed was the periodic change in morphological parameters induced by breathing, and since the recording was continuous, the parameters were extracted for the moments of maximum and minimum volumes of the chest (inspiration and expiration moments), and analyzed. The parameters were derived from morphological transverse cross-sections (CSs), which were extracted for the moments of maximal and minimal depth variations, and the reliability of the results was expressed through the coefficient of variation (CV) of the resulting curves. Across all subjects and levels of observed anatomy, the mean CV for CS depth values was smaller than 2%, and the mean CV of the CS area was smaller than 1%. To prove the reproducibility of measurements (extraction of morphological parameters), 10 subjects were recorded in two consecutive sessions with a short interval (2 weeks) where no changes in the monitored parameters were expected and statistical methods show that there was no statistically significant difference between the sessions, which confirms the reproducibility hypothesis. Additionally, based on the representative CSs for inspiration and expirations moments, chest mobility in quiet breathing was examined, and the statistical test showed no difference between the two sessions. The findings justify the proposed algorithm as a valuable tool for evaluating the impact of rehabilitation exercises on chest morphology.

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Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.

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Background: Intestinal microcirculation is a critical interface for nutrient exchange and energy transfer, and is essential for maintaining physiological integrity. Our study aimed to elucidate the relationships among intestinal microhemodynamics, genetic background, sex, and microbial composition. Methods: To dissect the microhemodynamic landscape of the BALB/c, C57BL/6J, and KM mouse strains, laser Doppler flowmetry paired with wavelet transform analysis was utilized to determine the amplitude of characteristic oscillatory patterns. Microbial consortia were profiled using 16S rRNA gene sequencing. To augment our investigation, a broad-spectrum antibiotic regimen was administered to these strains to evaluate the impact of gut microbiota depletion on intestinal microhemodynamics. Immunohistochemical analyses were used to quantify platelet endothelial cell adhesion molecule-1 (PECAM-1), estrogen receptor α (ESR1), and estrogen receptor ß (ESR2) expression. Results: Our findings revealed strain-dependent and sex-related disparities in microhemodynamic profiles and characteristic oscillatory behaviors. Significant differences in the gut microbiota contingent upon sex and genetic lineage were observed, with correlational analyses indicating an influence of the microbiota on microhemodynamic parameters. Following antibiotic treatment, distinct changes in blood perfusion levels and velocities were observed, including a reduction in female C57BL/6J mice and a general decrease in perfusion velocity. Enhanced erythrocyte aggregation and modulated endothelial function post-antibiotic treatment indicated that a systemic response to microbiota depletion impacted cardiac amplitude. Immunohistochemical data revealed strain-specific and sex-specific PECAM-1 and ESR1 expression patterns that aligned with observed intestinal microhemodynamic changes. Conclusions: This study highlights the influence of both genetic and sex-specific factors on intestinal microhemodynamics and the gut microbiota in mice. These findings also emphasize a substantial correlation between intestinal microhemodynamics and the compositional dynamics of the gut bacterial community.

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In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

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BACKGROUND AND OBJECTIVE: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure. METHOD: Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. RESULT: Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods. CONCLUSION: The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.

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Influenza, an acute respiratory illness, remains a significant public health challenge, contributing substantially to morbidity and mortality worldwide. Its seasonal prevalence exhibits diversity across regions with distinct climates. This study aimed to explore the seasonal patterns of influenza and their correlation with meteorological and air pollution factors across six regions of Thailand. We conducted an analysis of monthly average temperature, relative humidity, precipitation, PM10, NO2, O3 concentrations, and influenza incidence data from 2009 to 2019 using wavelet analysis. Our findings reveal inconsistent biannual influenza prevalence patterns throughout the study period. The biannual pattern emerged during 2010-2012 across all regions but disappeared during 2013-2016. However, post-2016, the biannual cycles resurfaced, with peaks occurring during the rainy and winter seasons in most regions, except for the southern region. Wavelet coherence reveals that relative humidity can be the main influencing factor for influenza incidence over a one-year period in the northern, northeastern, central, Bangkok-metropolitan, and eastern regions, not in the southern region during 2010-2012 and 2016-2018. Similarly, precipitation can drive the influenza incidence at the same period for the northeastern, central, Bangkok-metropolitan, and eastern regions. PM10 concentration can influence influenza incidence over a half-year period in the northeastern, central, Bangkok-metropolitan, and eastern regions of Thailand during certain years. These results enhance our understanding of the temporal dynamics of influenza seasonality influenced by weather conditions and air pollution over the past 11 years. Such knowledge is invaluable for resource allocation in clinical settings and informing public health strategies, particularly in navigating Thailand's climatic complexities.

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Due to the fact that the vibration and pure rotational Raman signals collected by the temperature and humidity profile lidar were 3-4 orders of magnitude weaker than the Mie scattering signal, they were susceptible to electronic and white noise interference, which seriously affected the system signal-to-noise ratio. In this paper, an improved VMD-WT filtering method was adopted to extract effective signals and denoise. The processing outcome of several filtering algorithms was evaluated, and noisy signals were simulated to confirm the algorithm's efficacy. Based on the quantitative computation of evaluation indicators, such as signal-to-noise ratio, root mean square error, and correlation, the improved VMD-WT algorithm had more significant advantages in indicators such as signal-to-noise ratio. In order to further verify the robustness and adaptability of the proposed algorithm, experimental analysis of the filtering algorithm was conducted on the continuously collected temperature and humidity measured signals. The results demonstrated that the algorithm not only improved the detection range of lidar and suppressed high-altitude noise effectively, but also performed well in processing strong interference signals, like clouds, which led to a significant improvement in the atmospheric optical parameter inversion results. Furthermore, pseudo-color images of aerosols, temperature, and humidity changes over time and space have been used to further illustrate the algorithm's dependability and wide range of potential uses.

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BACKGROUND: Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain. OBJECTIVE: This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions. METHODS: We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis. RESULTS: In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (ß=-.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (ß=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users. CONCLUSIONS: Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

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Radiation-induced acoustics (RIA) shows promise in advancing radiological imaging and radiotherapy dosimetry methods. However, RIA signals often require extensive averaging to achieve reasonable signal-to-noise ratios, which increases patient radiation exposure and limits real-time applications. Therefore, this paper proposes a discrete wavelet transform (DWT) based filtering approach to denoise the RIA signals and avoid extensive averaging. The algorithm was benchmarked against low-pass filters and tested on various types of RIA sources, including low-energy X-rays, high-energy X-rays, and protons. The proposed method significantly reduced the required averages (1000 times less averaging for low-energy X-ray RIA, 32 times less averaging for high-energy X-ray RIA, and 4 times less averaging for proton RIA) and demonstrated robustness in filtering signals from different sources of radiation. The coif5 wavelet in conjunction with the sqtwolog threshold selection algorithm yielded the best results. The proposed DWT filtering method enables high-quality, automated, and robust filtering of RIA signals, with a performance similar to low-pass filtering, aiding in the clinical translation of radiation-based acoustic imaging for radiology and radiation oncology.

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This paper proposes a system for predicting the effect of electrical defibrillation using spectral feature parameters. The proposed method consists of two-stage prediction. The first stage involves predicting whether electrical defibrillation is "Successful" or "Ineffective." As the next stage, if the proposed prediction system determines "Ineffective," the proposed system discriminates between "VF recurrence" or "Failure" for electrical defibrillation. To develop the prediction system, feature parameters for the target electrocardiograms (ECGs) were first extracted by using the wavelet transform and spectral analysis. Next, effective feature parameters for prediction are selected through an analysis of variance. Moreover, in the preprocessing phase, the Synthetic Minority Oversampling Technique method and standardization are introduced. Finally, support vector machines with some kernel functions and the regularization method are utilized to predict the three states, i.e., "Successful," "Failure," and "VF recurrence," for electrical defibrillation in two phases. In this paper, we present our analysis method for ECGs and evaluate the effectiveness of the proposed prediction system.

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Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system based on the global and multi-resolution analysis of retinal images. As a first step, we enhance the quality of the retinal images by applying a sequence of preprocessing techniques, which include the median filter, contrast limited adaptive histogram equalization (CLAHE), and the unsharp filter. These preprocessing steps effectively eliminate noise and enhance the contrast in the retinal images. Further, these images are represented at multi-scales using discrete wavelet transform (DWT), and center symmetric local binary pattern (CSLBP) features are extracted from each scale. The extracted CSLBP features from decomposed images capture the fine and coarse details of the retinal fundus images. Also, statistical features are extracted to capture the global characteristics and provide a comprehensive representation of retinal fundus images. The detection performances of these features are evaluated on a benchmark dataset using two machine learning models, i.e., SVM and k-NN, and found that the performance of the proposed work is considerably more encouraging than other existing methods. Furthermore, the results demonstrate that when wavelet-based CSLBP features are combined with statistical features, they yield notably improved detection performance compared to using these features individually.

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This paper presented a kinetic model of the Langmuir-Hinshelwood-Hougen-Watson (LHHW) type for porous catalysts with simple one-dimensional geometry, including spheres, infinite cylinders, and flat pellets. The model was applied to systems involving immobilized enzymes, where enzymes are attached to porous support materials to enhance stability and reusability. The LHHW model provided a tool for understanding and modeling reaction kinetics in heterogeneous porous catalysts and immobilized enzymes. A nonlinear reaction-diffusion equation was generated using finite-range Fickian diffusion and nonlinear reaction kinetics, crucial for accurately modeling the behavior of immobilized enzymes. This research addressed a gap in the existing literature by introducing fractional derivatives to investigate enzyme reaction kinetics, capturing the complex dynamics of substrate interaction and reaction rates within the porous matrix. An approximation method based on Lucas wavelets was employed to find solutions for substrate concentration and effectiveness factors across various parameter values. The analytical solutions derived from the Lucas wavelet method (LWM) were evaluated against the fourth-order Runge-Kutta method, showing great agreement between the LWM solutions and numerical counterparts. These results optimized diffusion and reaction kinetics, paving the way for advancements in biocatalysis and efficient enzyme reactor design.

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Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

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This study introduces a novel watermarking technique for electrocardiogram (ECG) signals. Watermarking embeds critical information within the ECG signal, enabling data origin authentication, ownership verification, and ensuring the integrity of research data in domains like telemedicine, medical databases, insurance, and legal proceedings. Drawing inspiration from image watermarking, the proposed method transforms the ECG signal into a two-dimensional format for QR decomposition. The watermark is then embedded within the first row of the resulting R matrix. Three implementation scenarios are proposed: one in the spatial domain and two in the transform domain utilizing discrete wavelet transform (DWT) for improved watermark imperceptibility. Evaluation on real ECG signals from MIT-BIH Arrhythmia database and comparison to existing methods demonstrate that the proposed method achieves: (1) higher Peak Signal-to-Noise Ratio (PSNR) indicating minimal alterations to the watermarked signal, (2) lower bit error rates (BER) in robustness tests against external modifications such as AWGN noise (additive white Gaussian noise), line noise and down-sampling, and (3) lower computational complexity. These findings emphasize the effectiveness of the proposed QR decomposition-based watermarking method, achieving a balance between robustness and imperceptibility. The proposed approach has the potential to improve the security and authenticity of ECG data in healthcare and legal contexts, while its lower computational complexity enhances its practical applicability.

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BACKGROUND: Studying habitat use and vertical movement patterns of individual fish over continuous time and space is innately challenging and has therefore largely remained elusive for a wide range of species. Amongst sharks, this applies particularly to smaller-bodied and less wide-ranging species such as the spurdog (Squalus acanthias Linnaeus, 1758), which, despite its importance for fisheries, has received limited attention in biologging and biotelemetry studies, particularly in the North-East Atlantic. METHODS: To investigate seasonal variations in fine-scale niche use and vertical movement patterns in female spurdog, we used archival data from 19 pregnant individuals that were satellite-tagged for up to 365 days in Norwegian fjords. We estimated the realised niche space with kernel densities and performed continuous wavelet analyses to identify dominant periods in vertical movement. Triaxial acceleration data were used to identify burst events and infer activity patterns. RESULTS: Pregnant females frequently utilised shallow depths down to 300 m at temperatures between 8 and 14 °C. Oscillatory vertical moments revealed persistent diel vertical migration (DVM) patterns, with descents at dawn and ascents at dusk. This strict normal DVM behaviour dominated in winter and spring and was associated with higher levels of activity bursts, while in summer and autumn sharks predominantly selected warm waters above the thermocline with only sporadic dive and bursts events. CONCLUSIONS: The prevalence of normal DVM behaviour in winter months linked with elevated likely foraging-related activity bursts suggests this movement behaviour to be foraging-driven. With lower number of fast starts exhibited in warm waters during the summer and autumn months, habitat use in this season might be rather driven by behavioural thermoregulation, yet other factors may also play a role. Individual and cohort-related variations indicate a complex interplay of movement behaviour and habitat use with the abiotic and biotic environment. Together with ongoing work investigating fine-scale horizontal movement as well as sex- and age-specific differences, this study provides vital information to direct the spatio-temporal distribution of a newly reopened fishery and contributes to an elevated understanding of the movement ecology of spurdog in the North-East Atlantic and beyond.

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Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia in patients with first-episode schizophrenia (FES) and analysed the correlation between the wavelet-based amplitude low-frequency fluctuation (wavelet-ALFF) of the main brain region and anhedonia. We recruited 31 patients with FES and 33 healthy controls (HCs) from the Affiliated Brain Hospital of Guangzhou Medical University. All subjects completed the Temporal Experience of Pleasure Scale (TEPS) and received resting-state fMRI (rs-fMRI). We used the wavelet-ALFF method and SVR to analyse the data. Patients with FES had lower consummatory pleasure scores than healthy subjects (t = -2.71, P<0.01). FES displays variable wavelet-ALFF in a wide range of cerebral cortices (P<0.05, GFR corrected). The SVR analysis showed that wavelet-ALFF, based primarily on the right putamen (r = 0.40, P<0.05) and right superior occipital gyrus (r = -0.39, P<0.05), was effective in predicting consummatory pleasure scores with an accuracy of 56.43 %. Our study shows that abnormal spontaneous neural activity in FES may be related to the state of consummatory anhedonia in FES. Wavelet-ALFF changes in the right putamen and superior occipital gyrus may be a biological feature of FES with anhedonia and could serve as a potential biological marker of FES with anhedonia.

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In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from QWT as the inputs of customized PWC-Net structure, a high-performance deep network in motion estimation. We have trained and tested our proposed method performance using two realistic simulated B-mode echocardiographic sequences. We have evaluated our proposed method in terms of both geometrical and clinical indices. Our method achieved an average endpoint error of 0.06 mm per frame and 0.59 mm between End Diastole and End Systole on a simulated dataset. Correlation analysis between ground truth and the computed strain shows a correlation coefficient of 0.89, much better than the most efficient methods in the state-of-the-art 2D echocardiography motion estimation. The results show the superiority of our proposed method in both geometrical and clinical indices.

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The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.