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Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

The vibration monitoring of scaled models in wind tunnels is critical for aerodynamic testing and structural safety. The abrupt onset of flutter or other aeroelastic instabilities poses a significant risk, necessitating the development of real-time, model-agnostic monitoring systems. This paper proposes a novel, generalized health indicator (HI) based on an improved Fisher Discriminant Analysis (FDA) framework for vibration state classification. The core innovation lies in reformulating the FDA objective function to distinguish between stable and dangerous vibration states, rather than tracking degradation trends. To ensure cross-model applicability, a frequency-wise standardization technique is introduced, normalizing spectral amplitudes based on the statistics of a model’s stable state. Furthermore, a dual-mode entropic regularization term is incorporated into the optimization process. This term balances the dispersion of weights across frequency bands (promoting generalizability and avoiding overfitting to specific frequencies) with the concentration of weights on the most informative resonance frequencies (enhancing the sensitivity to dangerous states). The optimal frequency weights are obtained by solving a regularized generalized eigenvalue problem, and the resulting HI is the weighted sum of the standardized frequency amplitudes. The method is validated using simulated spectral data and flight data from a wind tunnel test, demonstrating a superior performance in the early detection of dangerous vibrations and the clear interpretability of critical frequency bands. Comparisons with traditional sparse measures and machine-learning methods highlight the proposed method’s advantages in trendability, robustness, and unique capability for cross-model adaptation.

The sustainable management of wastewater through recycling and utilization stands as a pressing concern in the trajectory of societal advancement. Prioritizing the elimination of diverse organic contaminants is paramount in wastewater treatment, garnering significant attention from researchers worldwide. Emerging metal-organic framework materials (MOFs), bridging organic and inorganic attributes, have surfaced as novel adsorbents, showcasing pivotal potential in wastewater remediation. Nevertheless, challenges like limited water stability, elevated dissolution rates, and inadequate hydrophobicity persist in the context of wastewater treatment. To enhance the performance of MOFs, they can be modified through chemical or physical methods, and combined with membrane materials as additives to create membrane composite materials. These membrane composites, derived from MOFs, exhibit remarkable characteristics including enhanced porosity, adjustable pore dimensions, superior permeability, optimal conductivity, and robust water stability. Their ability to effectively sequester organic compounds has spurred significant research in this field. This paper introduces methods for enhancing the performance of MOFs and explores their potential applications in water treatment. It delves into the detailed design, synthesis strategies, and fabrication of composite membranes using MOFs. Furthermore, it focuses on the application prospects, challenges, and opportunities associated with MOF composite membranes in water treatment.

ObjectivesTo explore the possible concurrent brain functional and structural alterations in patients with migraine without aura (MwoA) patients compared to healthy subjects (HS).MethodsSeventy-two MwoA patients and forty-six HS were recruited. 3D-T1 and resting state fMRI data were collected during the interictal period for MwoA and HS. Voxel-based morphometry (VBM) for structure analysis and regional homogeneity (Reho) for fMRI analysis were applied. The VBM and Reho maps were overlapped to determine a possible brain region with concurrent functional and structural alteration in MwoA patients. Further analysis of resting state functional connectivity (FC) alteration was applied with this brain region as the seed.ResultsCompared with HS, MwoA patients showed decreased volume in the bilateral superior and inferior colliculus, periaqueductal gray matter (PAG), locus ceruleus, median raphe nuclei (MRN) and dorsal pons medulla junction. MwoA patients showed decreased Reho values in the middle occipital gyrus and inferior occipital gyrus, and increased Reho values in the MRN. Only a region in the MRN showed both structural and functional alteration in MwoA patients. Pearson correlation analysis showed that there was no association between volume or Reho values of the MRN and headache frequency, headache intensity, disease duration, self-rating anxiety scale or self-rating depression scale in MwoA patients. Resting state functional connectivity (FC) with the MRN as the seed showed that MwoA patients had increased FC between the MRN and PAG.ConclusionsMRN are involved in the pathophysiology of migraine during the interictal period. This study may help to better understand the migraine symptoms.Trial registrationNCT01152632. Registered 27 June 2010.

An adaptive constant acceleration (ACA) model is proposed for the maneuvering target tracking problem. Based on the Taylor series expansion of acceleration, we establish the relationship between the Jerk and the velocity as well as the acceleration so that the maneuvering acceleration variance is approximated by the components in the state error covariance matrix. Then, the latter one is connected with the process noise, and the adaptive adjustment of the ACA model is realized. Combining with the strong tracking square-root cubature filter (ST-SCKF) in our previous work, an ACA-ST-SCKF is developed. The simulation results show that the proposed filter possesses better adaptability, tracking accuracy and lower computational complexity compared with the adaptive current statistical (ACS) model-based ST-SCKF, the modified CS (MCS) model-based ST-SCKF, and the IMM-based STF-SCKF.

Array imperfections severely degrade the performance of most physics-driven direction-of-arrival (DOA) methods. Deep learning-based methods do not rely on any assumptions, can learn the latent data features of a given dataset, and are expected to adapt better to array imperfections compared with existing physics-driven methods. Hence, an improved DOA estimation method based on long short-term memory (LSTM) neural networks for situations with array imperfections is proposed in this paper. Various analyses given by this paper demonstrate that the phase features are the key to DOA estimation. Considering the sequential characteristics of the moving target and the correlation of multi-frame data features, the LSTM neural networks are used to learn and enhance the phase features of sampled data. The DOA estimation accuracy and generalization capability are improved by mitigating the phase distortion using LSTM. Numerical simulations and statistical results show that the proposed method is satisfactory in terms of both the generalization capability and imperfection adaptability compared with state-of-the-art physics-driven and data-driven methods.

Metal–organic frameworks (MOFs) have been widely used to remove organic/toxic compounds from waste water. Ciprofloxacin (CIP) has been detected in surface and waste water, which is harmful to aquatic organisms and human body. Herein, MIL-101(Cr)-HSO 3 was synthesized by solvothermal method and its structural features were characterized by XRD, SEM, FTIR, N 2 adsorption–desorption analysis at 77 K and zeta potential. Then, the CIP adsorption performance of MIL-101(Cr)-HSO 3 was investigated, in which the effect of adsorbent dosage, contact time, pH and ionic strength were explored. MIL-101(Cr)-HSO 3 showed the highest adsorption capacity when the adsorbent dosage was 0.1 g/L and the pH was 8.0. The observation from the effects of pH and ionic strength suggested a stronger electrostatic interactions between CIP and MIL-101(Cr)-HSO 3 . The pseudo-second-order model fitted the adsorption kinetics data of MIL-101(Cr)-HSO 3 well. Moreover, the equilibrium adsorption data of MIL-101(Cr)-HSO 3 followed the Langmuir model, indicating a mono-layer adsorption of CIP onto surface of MIL-101(Cr)-HSO 3 . The calculated maximum CIP adsorption capacity from Langmuir model was 564.9 mg/g, which was higher than the reported materials. Besides, the equilibrium adsorption data were fitted to the Tempkin model with r 2  = 0.9880, which also suggested a stronger electrostatic interaction between CIP and MIL-101(Cr)-HSO 3 . Finally, the introduced sulfonic acid group made the material more negatively charged on the surface, which benefited the adoption of CIP via stronger electrostatic interactions resulting the enhanced adsorption capacity of CIP. The results show that the MIL-101(Cr)-HSO 3 is a promising candidate for removal of CIP and introducing proper functional groups on organic linker is a convenient way to obtain MOFs with better performance for a specific application.

In this paper, with the aim of performing joint multi-target localization and discrimination tasks, a performance-driven resource allocation scheme is proposed. In the first, by establishing the signal model under deception jamming and utilizing the maximum likelihood (ML) estimator, the estimation information of targets can be obtained. Secondly, the Cramer–Rao lower bound (CRLB) for the transmit antenna selection and power allocation is derived. Then, to fully utilize the difference in spatial distribution between true and false targets, a false target discriminator based on the CRLB of the distance deception parameter is utilized. By introducing the nondimensionalization mechanism, we build an optimal objective function of target localization error and discrimination probability. Subsequently, a joint multi-target localization and discrimination optimization model has been established, which is mathematically a non-smooth and non-convex problem. By introducing an auxiliary variable, we propose a three-step solution strategy for solving this problem. Simulation results demonstrate that the proposed algorithm can improve the performance of joint localization accuracy and discrimination ability (JLADA) by more than 30% compared with the algorithms only for localization or discrimination. Meanwhile, by utilizing the proposed algorithm, the composite indicators of JLADA can decrease more than 70% compared with the uniform allocation scheme.

The aims of this study were to 1) compare resting state functional connectivity (rs-fc) of the periaqueductal gray (PAG), a key region in the descending pain modulatory system (DPMS) between migraine without aura (MwoA) patients and healthy controls (HC) and 2) investigate how an effective treatment can influence the PAG rs-fc in MwoA patients. One hundred MwoA patients and forty-six matched HC were recruited. Patients were randomized to verum acupuncture, sham acupuncture and waiting list groups. Resting state fMRI data were collected and seed based functional connectivity analysis was applied. Compared with HC, MwoA patients showed reduced rs-fc between the PAG and rostral anterior cingulate cortex/medial prefrontal cortex (rACC/mPFC), key regions in the DPMS and other pain related brain regions. The reduced rs-fc between the PAG and rACC/mPFC was associated with increased migraine headache intensity at the baseline. After treatments, rs-fc between the PAG and the rACC in MwoA patients significantly increased. The changes of rs-fc among the PAG, rACC and ventral striatum were significantly associated with headache intensity improvement. Impairment of the DPMS is involved in the neural pathophysiology of migraines. Impaired DPMS in migraine patients can be normalized after effective treatment.