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Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model’s capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.

Abstract Recognizing an individual’s preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology’s practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes.

Traffic flow prediction has been essential for traffic management and road network planning. However, the complex urban road network and the strong spatialtemporal correlation of traffic flow data make this problem difficult. Existing prediction methods cannot fully utilize the spatial-temporal correlations in traffic flow data. Therefore, we propose a deep learning model called ResGAT-ABiGRU which combines Residual Network (ResNet), Graph Attention Network (GAT), Attention Mechanism, and the Bidirectional Gated Recurrent Unit (BiGRU). Firstly, GAT is used to capture the spatial correlations of traffic flow data, and then the time characteristics are extracted by Bidirectional GRU. Secondly, the ResNet module stacks multiple GAT layers and designs the attention mechanism to assign weights for different flow sequences to further capture spatial relations. Finally, we obtain the output through the fully connected layers. Validation traffic data from California, USA, is used for verification. The results show that the ResGAT-ABiGRU model proposed in this paper has higher prediction accuracy. Compared the model's performance with the Gated Recurrent Unit (GRU) baseline model, and the root means square error (RMSE) is reduced by 22.75%, and compared to the T-gcn model, the root mean square error is reduced by 3.29%. [ABSTRACT FROM AUTHOR]

With the increasing demand for natural gas, the emergency peak shaving capacity of gas storage is facing great challenges. The operation of gas storage converted from depleted oil and gas reservoir has the characteristics of strong injection and production, and alternate injection and production. However, the sand production of the reservoir makes the major gas storages face the dilemma that the injection and production capacity cannot be enhanced. In this study, the impact factors of sand production in injection-production wells of gas storage converted from depleted oil and gas reservoir were analyzed, and the sand production simulation device of gas storage was developed. Aiming at the two main controlling factors of alternating load and flow, the experimental study of quantitative sand production was conducted. The experimental results show that the alternating load has a significant weakening effect on the reservoir, and the sand production is positively correlated with the number, size and span of the alternating load exerted on the reservoir. By the method of multiple regression analysis, the parameters affecting the sand production degree of the core were obtained as follows: the upper and lower limit pressure of the alternating load, the flow rate and the number of alternating loads. The sand production map was obtained by combining the sand production amount of the core and the upper limit pressure of the alternating load to evaluate the sand production degree of the injection-production wells. In addition, at the same flow rate, the ratio of sand yield of two alternating loads stages is approximately equal to the constant, which provides a reference for the prediction of sand production in gas storage. The indoor simulation experiment of sand production in gas storage provides technical ideas and theoretical basis for the reconstruction of gas storage in Nanpu 1–29 block, and guides the selection of sand control completion methods in gas storage.

ObjectiveWe sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET.MethodsTwenty-one patients underwent whole-body dynamic 18F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MRFDG and DVFDG images (MRFDG-std and DVFDG-std) and two short dynamic MRFDG and DVFDG images (MRFDG-tsd and DVFDG-tsd), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis.ResultsHigh correlations were obtained between MRFDG-tsd and MRFDG-std, and between DVFDG-tsd and DVFDG-std for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MRFDG-max, MRFDG-mean, and MRFDG-peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DVFDG-max, DVFDG-mean, and DVFDG-peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MRFDG-max, MRFDG-mean, and MRFDG-peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DVFDG-max, DVFDG-mean, and DVFDG-peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively.ConclusionsThis study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis.

Designing efficient sensors for human machine interaction remains a challenge. Here, the authors present a soft robotic fingers system based on a triboelectric nanogenerator (L-TENG) sensor to capture the continuous motion of soft gripper and a soft tactile (T-TENG) sensor for tactile sensing, that can achieve an object recognition accuracy of 98.1%.

Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.

Initial romantic attraction (IRA) refers to a series of positive reactions to potential romantic partners at the initial encounter; it evolved to promote mate selection, allowing individuals to focus their mating efforts on their preferred potential mates. After decades of effort, we now have a deeper understanding of the evolutionary value and dominant factors of IRA; however, little is known regarding the brain mechanisms related to its generation and evaluation. In this study, we combined classic event-related potential analysis with dipole-source analysis to examine electroencephalogram (EEG) signals generated while participants assessed their romantic interest in potential partners. The EEG signals were categorized into IRA-engendered and unengendered conditions based on behavioral indicators. We found that the faces elicited multiple late positivities, including P300 over the occipital–parietal regions and late positive potentials (LPPs) over the anterior regions. When compared to faces that did not engender IRA, faces that did engender IRA elicited (1) enhanced P300 over the parietal regions and heightened neural activity in the insula and cingulate cortex and (2) larger LPPs over the anterior regions and heightened neural activity in the orbitofrontal cortex, dorsolateral prefrontal cortex, cingulate cortex, frontal eye field, visual cortex, and insula. These results suggest IRA is generated and evaluated by an extensive brain network involved in emotion processing, attention control, and social evaluations. Furthermore, these findings indicate that P300 and LPP may represent different cognitive processes during IRA.

Using underground space to store natural gas resources is an important means by which to solve emergency peak shaving of natural gas. Rock salt gas storage is widely recognized due to its high-efficiency peak shaving and environmental protection. Damage and stress concentrations inside the cavern injection during withdrawal operations and throughout the storage facility life have always been among the most important safety issues. Therefore, accurate evaluation of the stability of rock salt gas storage during operation is of paramount significance to field management and safety control. In this study, we used the finite element numerical analysis software Flac3D to numerically simulate large displacement deformations of the cavern wall during gas storage—in addition to the distribution of the plastic zone of the rock around the cavern and the surface settlement—under different working conditions. We found that the maximum surface settlement value occurred near the upper part of the cavern. The surface settlement value increased as a function of creep time, but this increase leveled off, that is, a convergence trend was observed. The value was relatively small and, therefore, had little impact on the surface. The application of gas pressure inhibited the growth of the plastic zone, but on the whole, the plastic zone’s range increased proportionally to creep time. For the 20-year creep condition, the deformation value of the cavern’s surrounding rock was large. Combined with the distribution of the plastic zone, we believe that the cavern’s surrounding rock is unstable; thus, corresponding reinforcement measures must be taken.

Abstract Vertically aligned carbon nanotubes (VACNTs) were synthesized on different oxide buffer layers using chemical vapor deposition (CVD). The growth of the VACNTs was mainly determined by three factors: the Ostwald ripening of catalyst nanoparticles, subsurface diffusion of Fe, and their activation energy for nucleation and initial growth. The surface roughness of buffer layers largely influenced the diameter and density of catalyst nanoparticles after annealing, which apparently affected the lifetime of the nanoparticles and the thickness of the prepared VACNTs. In addition, the growth of the VACNTs was also affected by the deposition temperature, and the lifetime of the catalyst nanoparticles apparently decreased when the deposition temperature was greater than 600 °C due to their serious Ostwald ripening. Furthermore, in addition to the number of catalyst nanoparticles, the density of the VACNTs was also largely dependent on their activation energy for nucleation and initial growth.

ABSTRACT: The smooth variable structure filter (SVSF) has been considered as the robust estimator. Like other filters, the SVSF needs an accurate system model and known noise statistics to approximate the posterior state. Unfortunately, the system cannot be accurately modeled, and the noise statistic is unknown in the real application. For these reasons, the performance of SVSF might be decreased or even led to divergence. Therefore, the enhancement of SVSF is required. This paper presents an Adaptive SVSF. Initially, SVSF is smoothed. To provide the ability to estimate the noise statistic, ASVSF is then derived based on maximum likelihood estimation (MLE) and expectation-maximization (EM). Additionally, the unbiased noise statistic is also approached. However, its covariance is complicatedly formulated. It might cause a negative definite symmetric matrix. Therefore, it is tuned based on the innovation covariance estimator (ICE). The ASVSF is designed to solve the online problem of Simultaneous Localization and Mapping (SLAM). Henceforth, it is termed as the ASVSF-SLAM algorithm. The proposed algorithm showed better accuracy and stability compared to the conventional algorithm in terms of root mean square error (RMSE) for both Estimated Path Coordinate (EPC) and Estimated Map Coordinate (EMC). ABSTRAK: Penapis struktur bolehubah lembut (SVSF) telah dianggap sebagai penganggar teguh. Seperti penapis lain, SVSF memerlukan model sistem yang tepat dan statistik hingar yang diketahui bagi menganggar keadaan posterior. Malangnya, sistem tidak dapat dimodelkan dengan tepat dan statistik hingar tidak diketahui dalam aplikasi sebenar. Atas sebab-sebab ini, prestasi SVSF mungkin berkurangan, bahkan berbeza. Oleh itu, memperbaharui SVSF adalah perlu. Kajian ini adalah mengenai SVSF Mudah Suai. Pada awalnya, SVSF dilembutkan. Bagi menyediakan keupayaan anggaran statistik hinggar, ASVSF dihasilkan terlebih dahulu berdasarkan anggaran kemungkinan maksimum (MLE) dan maksimum-harapan (EM). Tambahan, statistik hinggar yang tidak berat sebelah juga dibuat. Walau bagaimanapun, rumusan formula kovarians ini adalah kompleks. Ini mungkin menyebabkan matriks simetri menjadi negatif. Oleh itu, ia diselaraskan berdasarkan penganggar kovarians inovasi (ICE). ASVSF dibina bagi menyelesaikan masalah dalam talian Penempatan dan Pemetaan Serentak (SLAM) dalam talian. Oleh itu, ia disebut sebagai algoritma ASVSF-SLAM. Algoritma yang dicadangkan ini menunjukkan ketepatan dan kestabilan yang lebih baik berbanding algoritma konvensional dari segi ralat punca min kuasa dua (RMSE) bagi kedua-dua Koordinat Anggaran Laluan (EPC) dan Anggaran Koordinat Peta (EMC).

Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.

Although research on the mismatch negativity (MMN) has been ongoing for 40 years, the generation process of the MMN remains largely unknown. In this study, we used a single-trial electro-encephalography (EEG)-functional magnetic resonance imaging (fMRI) coupling method which can analyze neural activity with both high temporal and high spatial resolution and thus assess the generation process of the MMN. We elicited the MMN with an auditory oddball paradigm while recording simultaneous EEG and fMRI. We divided the MMN into five equal-durational phases. Utilizing the single-trial variability of the MMN, we analyzed the neural generators of the five phases, thereby determining the spatiotemporal generation process of the MMN. We found two distinct bottom-up prediction error propagations: first from the auditory cortex to the motor areas and then from the auditory cortex to the inferior frontal gyrus (IFG). Our results support the regularity-violation hypothesis of MMN generation.

Tempo is an important musical element that affects human’s emotional processes when listening to music. However, it remains unclear how tempo and training affect individuals’ emotional experience of music. To explore the neural underpinnings of the effects of tempo on music-evoked emotion, music with fast, medium, and slow tempi were collected to compare differences in emotional responses using functional magnetic resonance imaging (fMRI) of neural activity between musicians and non-musicians. Behaviorally, musicians perceived higher valence in fast music than did non-musicians. The main effects of musicians and non-musicians and tempo were significant, and a near significant interaction between group and tempo was found. In the arousal dimension, the mean score of medium-tempo music was the highest among the three kinds; in the valence dimension, the mean scores decreased in order from fast music, medium music, to slow music. Functional analyses revealed that the neural activation of musicians was stronger than those of non-musicians in the left inferior parietal lobe (IPL). A comparison of tempi showed a stronger activation from fast music than slow music in the bilateral superior temporal gyrus (STG), which provided corresponding neural evidence for the highest valence reported by participants for fast music. Medium music showed stronger activation than slow music in the right Heschl’s gyrus (HG), right middle temporal gyrus (MTG), right posterior cingulate cortex (PCC), right precuneus, right IPL, and left STG. Importantly, this study confirmed and explained the connection between music tempo and emotional experiences, and their interaction with individuals’ musical training.

Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.