Search Results (1,681)

Background: Electroencephalography (EEG) signals are widely used for monitoring anesthesia depth during surgery. Current commercial indicators are largely closed-source and may reflect dynamic changes with some delay. Methods: This study proposes a multitask deep learning model for continuous Bispectral Index (BIS) estimation, binary anesthesia-state classification, and prediction of transitions toward light anesthesia at different time intervals. Dual-channel EEG signals from 5471 surgical cases in the VitalDB dataset were divided into 60 s windows. Short-Time Fourier Transform (STFT) captured instantaneous frequency changes to transform the signal into a two-dimensional map. A ResNet-SE architecture incorporating Squeeze-and-Excitation blocks was used to identify EEG features associated with anesthesia depth. Results: A Mean Absolute Error of 3.27 and a Root Mean Square Error of 5.48 were obtained in anesthesia depth estimation. Light anesthesia classification achieved an AUC of 0.99 on the internal test set. Conclusions: The proposed multitask model enables the assessment of anesthesia depth and transitions toward light anesthesia using EEG signals. Full article

To address the key challenges in GB-RAR monitoring of super-tall buildings—namely, complex noise interference (transient pulse disturbances coupled with high-frequency random fluctuations), the difficulty of distinguishing normal wind-induced vibrations from hazardous deformations, and the propensity of single-algorithm prediction models to converge prematurely—this paper proposes an integrated monitoring data processing workflow that combines status assessment and deformation early warning, using Wuhan Greenland Center as a case study. A denoising method combining Median Absolute Deviation outlier removal and Savitzky–Golay filtering was designed for preprocessing, quantitatively validated through signal-to-noise ratio analysis. Based on filtered data, a spatio-temporal trajectory model was established to visualize and evaluate building movement. Furthermore, a GB-RAR-oriented residual-driven warning framework was developed by coupling a PSO-GA-BP deformation prediction model with adaptive sliding-window thresholding and finite-state warning decisions. Simulation results demonstrate that the PSO-GA-BP model outperforms other neural network models in prediction accuracy, and the derived early warning system exhibits strong feasibility and sensitivity. This workflow proves suitable for GB-RAR deformation monitoring of super-tall buildings, offering valuable reference for future research. Full article

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

Microplastics (MPs) are increasingly detected in dairy products, raising food-safety concerns. Their behavior in complex food matrices and interactions with probiotic microorganisms remain poorly understood. This exploratory study evaluated storage-dependent changes in operationally defined, digestion-resistant recoverable residues in yogurt containing Bifidobacterium longum subsp. infantis (ATCC 15697). Yogurt samples were prepared with polypropylene (PP), polyethylene (PE), and polystyrene (PS), individually and in combination, and analyzed over 21 days of refrigerated storage. Gravimetric values served as relative, operational indicators of recoverable residues—not validated absolute polymer masses—while polymer identity was qualitatively confirmed by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS). B. longum subsp. infantis remained viable throughout storage (6.3–8.2 log₁₀ CFU/g). All MP-containing groups showed consistent storage-associated decreases in recoverable residue fractions, greatest in PP, followed by PE and PS; probiotic-free controls remained stable. Polymer-specific Py-GC/MS signals were detectable at all time points. Because polymer identity was retained and the workflow was not validated for absolute recovery, findings are interpreted as storage-associated changes in extractability, filterability, and/or residue recovery—not as polymer degradation, mineralization, or biological removal. These in vitro observations are limited to the yogurt matrix and do not support extrapolation to livestock exposure, human dietary risk, or farm-to-fork transfer. Within these limits, the findings provide a preliminary, hypothesis-generating perspective on probiotic–microplastic interactions in fermented dairy products. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments. Full article

Reasoning-intensive multimodal retrieval suffers from a counter-intuitive bottleneck: on MM-BRIGHT multimodal-to-text (Query+Image → Documents), the strongest dense multimodal encoder reaches only 27.6 nDCG@10 and the rest of the dense vision–language retrievers cluster between 10.0 and 23.0. The visual signal, encoded as a dense vector, adds noise rather than evidence; even augmenting strong text retrievers with raw image captions degrades performance by up to 12.0 points. We propose VISA, a Visual Symbolic Agent that re-casts multimodal-to-text as text retrieval over three parallel streams. A Vision LLM is dispatched in three roles via separate prompts: a zero-shot router that classifies the query image into up to three parser types from a fixed taxonomy of nine (chart, circuit, equation, screenshot, code, figure, diagram, map, photograph); typed parsers that extract structured text per type; and a holistic captioner. The agent constructs three text streams (raw query, query ⊕ symbolic, query ⊕ caption), scores each with a single frozen 4B-parameter retrieval LLM, and fuses the per-document scores via Reciprocal Rank Fusion or a confidence-weighted linear combination. The whole agent contains no trainable parameters. The key novelty is a change of substrate: rather than projecting the query image into a dense multimodal vector that competes with text, VISA is, to our knowledge, the first retrieval system to convert the image into typed symbolic text and keep retrieval entirely text-side, so that a frozen text retriever can match the literal tokens (axis values, variable names, function signatures) that answering documents actually contain. Across all 29 MM-BRIGHT multimodal-to-text domains, VISA achieves 32.4 nDCG@10, an absolute improvement of +4.8 over the strongest dense multimodal encoder and substantially larger margins over the remaining six dense vision–language baselines. Per-domain analysis shows VISA maintains its margin across STEM and software domains where image content is structure-heavy. In practical terms, VISA is training-free and model-agnostic: it requires no fine-tuning, reuses any off-the-shelf vision LLM and text retriever, caches all per-image parsing so re-runs cost only three query encodes, and can therefore be dropped into an existing text-retrieval stack to add reasoning-intensive multimodal capability without building or training a multimodal encoder. Full article

Sound event detection models commonly rely on spectrogram representations of audio signals and recent approaches have adapted image-based object detection architectures to acoustic domains. This paradigm is suitable for respiratory monitoring, where breathing events are visually distinguishable even under noisy conditions. In this study, we propose a Representation Enhancement for Neural Imaging (RENI) framework that combines a modified You Only Look Once (YOLO) object detection head with a trainable spectrogram front-end implemented using nnAudio. The front-end enables GPU-accelerated waveform-to-spectrogram conversion while allowing adaptive learning of Short-Time Fourier Transform (STFT) and Melody (Mel) basis functions. The model was trained for breathing-phase localization and respiratory rate estimation from 44.1 kHz audio recordings acquired during exercise. The results show that the trainable Mel representation improves respiratory-rate accuracy compared with static and trainable STFT configurations, achieving a mean absolute error of 1.15 breaths per minute. Bootstrap 95% confidence intervals and one-sided permutation tests show statistically significant gains for selected trainable STFT and Mel configurations under the min-MAE confidence thresholding protocol, while pooled effects remain directionally favorable for the trainable Mel front-end. These findings demonstrate improved exhale-based respiratory rate estimation under the studied conditions, while broader external validation is still required. Full article

Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article

Melanoma exhibits pronounced metabolic plasticity and mitochondrial dependency, contributing to therapeutic resistance and tumor progression. Targeting mitochondrial function therefore represents a promising anticancer strategy. 2-Aminoethyl dihydrogen phosphate (2-AEH₂P), a bioactive phosphomonoester, has demonstrated antiproliferative potential, while metformin, a clinically established antidiabetic drug, acts as a mitochondrial complex I inhibitor and metabolic modulator. This study investigated the cytotoxic and mechanistic effects of 2-AEH₂P and metformin hydrochloride, individually and in combination, in human (SK-MEL-28) and murine (B16-F10) melanoma models, using non-tumorigenic fibroblasts (FN1 and L929) as controls. Cell viability, proliferation dynamics, cell-cycle distribution, mitochondrial membrane potential (ΔΨm), and apoptosis-associated markers were evaluated by flow cytometry. 2-AEH₂P reduced melanoma cell viability and proliferation while inducing G2/M accumulation, DNA fragmentation, mitochondrial depolarization, increased cytochrome c release, caspase-3 and caspase-8 activation, upregulation of p53 and Bad, and downregulation of Bcl-2. Metformin alone exerted moderate cytotoxic and pro-apoptotic effects. Notably, combined treatment markedly potentiated mitochondrial depolarization and intrinsic apoptotic signaling in melanoma cells, significantly lowering IC₅₀ values and enhancing caspase activation and cytochrome c release. Bliss independence analysis demonstrated synergistic interaction in SK-MEL-28 and B16-F10 cells. Although interaction scores indicated synergy in one fibroblast model, absolute cytotoxicity remained lower than in melanoma cells. These findings demonstrate that metabolic co-targeting with metformin enhances mitochondrial dysfunction-associated apoptotic signaling in melanoma cells, supporting a drug repositioning strategy aimed at exploiting mitochondrial vulnerability in metabolically adaptable tumors. Full article

Current deep learning methods for tool wear monitoring suffer from poor interpretability and struggle to reveal the intrinsic relationship between signals and wear states. To address this issue, this paper presents an interpretable tool wear monitoring method based on Sparse Identification of Nonlinear Dynamics (SINDy). Multi-domain features are extracted from cutting force and acoustic emission signals to construct a time series. The SINDy algorithm is used to identify ordinary differential equations that describe the evolution of tool wear. An iterative “predict-validate-correct” mechanism is applied to optimize model parameters. Experimental results show that the mean absolute percentage error (MAPE) between the predicted and actual values is below 6%. Moreover, the optimal model demonstrates an average MAPE as low as 0.067% in cross-condition tests. This study provides an effective solution for online tool wear monitoring that achieves high precision, strong generalization, and physical interpretability. Full article

Sixth-generation (6G) networks are expected to enable a new level of connectivity, with peak data rates reaching 1 Tbps and latencies below 0.1 ms, especially in large-scale Internet of Things (IoT) environments. Despite these advantages, the rapid increase in device density poses multiple challenges, most notably the growth in control plane signaling and the associated increase in energy consumption. These issues might significantly affect the scalability and efficiency of future networks if left unaddressed. We propose EASE-6G, an energy-aware Software-Defined Networking (SDN) framework that moves network operation from reactive to proactive and predictive, supporting ultra-dense conditions, where the number of connected devices may reach ${10}^6$ devices per square kilometer. EASE-6G uses Proactive Flow Installation to reduce the need for instant decisions. Traffic is predicted using a Long Short-Term Memory (LSTM) model, while a signaling-aware Deep Q-Network (DQN) streamlines control, reducing unnecessary signaling while maintaining performance. Simulations in OMNeT++/Simu5G were performed to compare EASE-6G with Smart Fog Radio Access Network (SF-RAN) and Deep Q-Network-based Open Radio Access Network (DQN-ORAN). EASE-6G was found to reduce energy consumption by 36.8%, signaling overhead by 36.7%, and latency by 35.6%. The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 4.2%. The DQN agent showed improved stability, with 22% lower variance than the baseline. These results demonstrate that the proposed predictive SDN control mechanisms improve energy efficiency and reduce overhead, delivering a practical solution for the implementation of scalable, sustainable IoT in future 6G networks. Full article

To address the challenges of strong signal noise, pronounced cross-modal asynchrony, high subjectivity in manually defined state labels, and insufficient model stability under extreme abnormal conditions in multi-source sensor systems, a low-noise system state prediction and anomaly detection method based on multimodal sensor signals and self-supervised representation learning is proposed. Environmental sensing data, device status data, network transmission data, operational behavior data, and event log data are uniformly modeled as system state perception signals. A temporal masking-based state structure modeling method, a state-oriented contrastive learning representation constraint mechanism, and a state representation and downstream prediction task alignment strategy are designed to learn stable, transferable, and interpretable system state features. Experimental results demonstrate that the proposed method achieves the best performance in multimodal sensor state prediction and anomaly detection tasks, with mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE) values of $0.0167$, $0.0856$, and $0.1291$, respectively, outperforming baseline models such as GARCH, MLP, LSTM, TCN, and Transformer. Meanwhile, $IC$, $RankIC$, and $AUC$ reach $0.494$, $0.460$, and $0.815$, respectively, indicating stronger state-ranking capability and improved discrimination between high-abnormality and low-abnormality states. At the classification recognition level, superior accuracy, precision, recall, and $F1$-score are also achieved by the proposed method, suggesting that potential abnormal states can be identified more accurately. Ablation experiments verify the effectiveness of multimodal fusion, temporal masking modeling, self-supervised contrastive constraints, and task alignment strategies. Robustness experiments further show that lower prediction errors and higher $AUC$ can still be maintained under high-fluctuation and extreme-shock states, demonstrating strong noise resistance, stability, and practical application potential in complex sensor system scenarios. Full article

Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries. However, the weak voltage observability of lithium iron phosphate (LFP) batteries within the voltage plateau region limits the accuracy of conventional voltage-based methods. To address this issue, an electro–mechanical information fusion framework for SOC estimation is proposed. Fiber Bragg grating (FBG) sensors were employed to simultaneously measure the surface strain and temperature of prismatic LFP batteries. Experimental results showed that the strain signal exhibited a stronger correlation with SOC than the voltage signal, with an average absolute correlation coefficient of 0.92. A Thevenin equivalent circuit model combined with an adaptive forgetting factor recursive least squares (AFFRLS) algorithm was established for online voltage modeling, while a Mamba-based strain model was developed to capture the nonlinear temporal relationship between multidimensional sensing data and battery strain. The two models were further integrated with adaptive unscented Kalman filters (AUKFs) and fused through a dual-layer adaptive weighting strategy. Experimental results under the five operating conditions considered in this study demonstrated that the proposed method achieved average RMSE and MAE values of 0.98% and 0.80%, respectively, outperforming standalone voltage- and strain-based methods. Full article

Node-level cargo-volume forecasting in logistics sorting networks requires modeling temporal dynamics together with directed inter-node dependencies and planned topology perturbations. This study addresses 1-h-ahead forecasting under a partial-information boundary, where historical node volumes, the pre-change network structure, and planned route-topology changes are available before prediction, whereas continuous post-change dynamic edge weights and realized post-change graph states are unavailable. We propose a perturbation-aware framework that represents the sorting system as a directed network and integrates temporal features, pre-change structural descriptors, topology-change encodings, perturbation-response proxies, and similarity-assisted support for data-scarce nodes within a unified forecasting protocol. A shared random forest backbone is used only to assess the incremental value of these representations. Experiments on 57 sorting centers show that temporal dynamics dominate under stable-network conditions. Under topology perturbation, topology-change signals reduce test weighted absolute percentage error (WAPE) from 18.10% to 17.11%, and perturbation-response proxies further reduce it to 16.91%. For data-scarce nodes, similarity support reduces test WAPE from 29.43% to 26.68%, with consistent gains under 10%, 20%, and 30% sample-retention settings. These results suggest that the framework provides an interpretable and information-admissible representation strategy for node-level forecasting in directed networked systems. Full article