Distortions in pain memory carry important clinical implications, yet the processes underlying retention of nociceptive information remain incompletely understood. This study examined how the intensity of painful somatosensory inputs is maintained in memory over different intervals. Twenty-five participants received pairs of nociceptive or non-nociceptive electrical stimuli at varying inter-stimulus intervals (3, 8, 13, 18 s), and judged whether the second stimulus was of higher, equal, or lower intensity than the first. In accordance with Weber-Fletcher Law, perceptual discriminability was lower in the nociceptive condition, leading to a decreased accuracy despite comparable confidence ratings. Memory performance declined with temporal delay similarly for both modalities. Accuracy increased when the second stimulus was more intense than the first. For nociceptive stimuli only, accuracy deteriorated disproportionately when the second stimulus was weaker, suggesting a directional encoding bias specific to pain. When errors occurred, participants overestimated the intensity of the second stimulus, a bias more pronounced for nociceptive stimuli. Such overestimation was linked to memory encoding and disappeared when participants rated stimuli without memory demand. Short-term memory for nociceptive stimuli proved less accurate, more directionally biased, and more prone to overestimation than for non-nociceptive inputs. While this performance gap is largely explained by reduced perceptual discriminability at pain intensities, modality-specific distortions point to additional constraints imposed by nociceptive processing on memory encoding and maintenance. This study provides psychophysical evidence for differences in short-term memory retention of nociceptive versus non-nociceptive sensory input, with implications for understanding pain memory distortions in clinical contexts.

Accurate and robust forecast of meteorological variables such as wind speed, solar irradiance and ambient temperature is challenging because of their nonlinear, non-stationary behavior as well as season-dependent dynamics. This study proposes an adaptive least squares regression fusion-based ensemble method for multi-horizon multi-season weather forecasting by combining three individual deep learning models: a proposed multi-scale three-branch convolutional neural network with bidirectional long short-term memory, a classical long short-term memory, and convolutional neural network with bidirectional long short-term memory. The least squares regression fusion adaptively assigns the appropriate weights to each individual model according to the weather variable, season, and forecast horizon. Evaluation tests conducted on a one-year dataset for different short-term horizons reveal the superior performance of least squares regression fusion compared to all individual deep learning models in terms of accuracy and performance stability. For instance, at one-hour forecasting horizon, the outcomes show, a root mean square error reduction ranged from 3–12% for irradiance, 3–10% for wind speed and 4–10% for temperature. Additionally, a high coefficient of determination was observed, approximately equal to 0.99, implying a strong temporal correlation between the predicted and observed weather variables throughout the four seasons. Statistical analyses, including paired t-tests with false discovery rate correction, confirm that least squares regression fusion consistently outperforms individual models, achieving the highest win rates for wind speed (65.8%) and irradiance (55.8%), while remaining competitive for temperature (46.4%). Overall, the adaptive least squares regression fusion framework effectively integrates heterogeneous deep learning models, dynamically adjusting their corresponding contribution, and achieves an effective forecast of weather variables for short-term multi-horizons and across all four seasons. • Multi-season, multi-horizon short-term forecasting of three weather variables. • Adaptive LSR-based fusion of MS-3B-CNN-BiLSTM, LSTM, and CNN-BiLSTM with chronological train–test splits. • LSR fusion outperforms individual DL models across all seasons and forecast horizons. • Statistical validation using paired t-tests with false discovery rate correction confirms the superiority of LSR fusion. • Robustness and high accuracy of the model are achieved specially for temperature and irradiation.

Objective The objective of this cross-sectional study was to compare the auditory memory scores obtained using the verbal and picture-pointing response modes in children with cochlear implants and implant-age matched typically developing children. Method The short-term auditory memory test consisting of word sequences ranging from two-word sequences to five-word sequences was developed and administered to 53 children using unilateral cochlear implants and 53 typically developing children matched in terms of implant age and gender. The implant age of the children ranged from 2 years 8 months to 7 years 9 months, and the mean age of implantation was 3 years 5 months. Each participant was tested twice, once using a verbal response task and once using a picture-pointing response task in two different sessions. Results The total auditory memory scores were significantly higher (P < 0.05) for the picture-pointing task as compared to the verbal recall task, especially when the memory task placed a high auditory demand and cognitive load on the children. The total auditory memory scores obtained using both the response tasks were lower in children using cochlear implants as compared to implant-age matched typically developing children (P < 0.05). Conclusion The present findings indicate that the type of response task affects the performance on the short-term auditory memory test in children using cochlear implants and typically developing children, with the picture-pointing response task yielding better scores than the verbal response.

An algorithm-enhanced stool DNA system improves the differential diagnosis of colorectal cancer versus Crohn's disease in high-risk symptomatic patients.

The rapid growth in global population necessitates efficient energy management solutions for sustainable living. Smart Building Energy Management Systems (SBEMS) play a crucial role in achieving this goal by leveraging automation and advanced analytics. This study proposes a novel Deep Learning and IoT-based SBEMS approach to predict energy consumption, classify buildings into energy-demand clusters, and optimize the monitoring and operation of electrical equipment. While traditional statistical methods have been widely used for load forecasting, recent advancements in deep learning provide robust alternatives to address the inherent complexity of nonlinear energy consumption patterns. This research employs regression analysis and state-of-the-art neural network architectures, including Single-Step and Multi-Step Dense Models, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance prediction accuracy. Additionally, K-means clustering is introduced to segment buildings into distinct energy-demand categories, ensuring optimal energy utilization. Unlike prior studies that often lack a comprehensive approach, this work integrates all critical features under a unified framework. By applying these advanced methodologies to a unique dataset, the proposed system demonstrates improved accuracy in energy load forecasting and clustering, providing a significant contribution to the field of smart building energy management. The experimental results demonstrate that CNN and LSTM models significantly outperform conventional statistical approaches in capturing nonlinear energy consumption patterns. These outcomes support proactive energy scheduling, peak-demand mitigation, and scalable smart building energy management, offering practical value for facility managers, utility operators, and policymakers.

ICU survivors frequently develop post-intensive care syndrome (PICS), a cluster of persistent physical, cognitive and psychological impairments that substantially impair recovery and quality of life. Existing rehabilitation approaches are predominantly monomodal and exercise-focused, yielding inconsistent outcomes and failing to address the multidimensional burden of PICS adequately. To evaluate the feasibility and preliminary efficacy of COMBAT-ICU, a home-based Combined Activity and Cognitive Intervention for ICU survivors at risk of PICS. A parallel, three-arm, assessor-blinded pilot randomised controlled trial randomised 36 ICU survivors (1:1:1) to COMBAT-ICU-an 8-week blended program of progressive physical exercise and computerised cognitive training delivered via supervised home visits and online sessions-an exercise-only group or an attention control group. The primary outcomes were feasibility (recruitment, retention and intervention adherence) and safety; secondary exploratory outcomes encompassed PICS severity, physical capacity, cognition, mental health and health-related quality of life (HRQoL). COMBAT-ICU was feasible and safe (36 ICU survivors randomised), with no serious adverse events recorded, retention exceeding 82% at follow-up and session adherence exceeding 90%. COMBAT-ICU produced significantly greater reductions in PICS severity versus attention control at post-intervention (p = 0.014, d = -0.50) and follow-up (p = 0.043, d = -0.45). It also yielded clinically meaningful moderate-to-large effect sizes for walking endurance, global cognition, short-term memory and HRQoL index scores compared with attention control and consistently outperformed exercise-only across cognitive and HRQoL domains. Between-group differences in anxiety and depressive symptoms were small across all active groups. COMBAT-ICU is feasible and shows promising preliminary efficacy in mitigating PICS. Integrating cognitive and physical training within a home-based blended delivery model may confer synergistic benefits beyond exercise alone, providing domain-specific effect size estimates and a compelling rationale for definitive multicentre trials. Multidomain home-based rehabilitation is a viable post-discharge strategy for ICU survivors. COMBAT-ICU offers an evidence-informed, scalable framework to enhance survivorship care, pending confirmation in larger, fully powered trials. The trial was registered at ClinicalTrials.gov (NCT06117761).

Effects of (poly)phenol-rich cranberry on mental health in university students: The CRANMOOD randomised controlled trial.

A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting.

Projecting Actual Evapotranspiration (AET) is critical for the survival of semi-arid Pinus sylvestris var. mongolica forests but remains difficult due to climate uncertainties. We bridged this gap by developing a hybrid framework in Inner Mongolia. We trained a Long Short-Term Memory (LSTM) network using SEBAL-derived AET as a physics-based proxy target. This approach achieved high accuracy in simulating historical dynamics (R2 = 0.926, RMSE = 13.56 mm/month). Crucially, our model relies on high-resolution data from 2021 to learn intra-annual seasonality; consequently, our projections represent responses to mean climatological shifts rather than interannual variability. To assess future risks, we drove this validated model with an ensemble of five bias-corrected CMIP6 General Circulation Models (GCMs) for the 2030–2050 period under SSP2-4.5 and SSP5-8.5 scenarios. The ensemble projections reveal a robust increase in total annual AET, driven by a predicted ‘warmer and wetter’ climate (+2.4°C temperature, +12% precipitation). However, this increase is uneven, showing a significant intensification specifically during the growing season (May–August). This seasonal spike indicates a heightened risk of rapid soil moisture depletion due to soaring atmospheric demand, paradoxically creating water stress despite higher annual rainfall. These findings challenge conventional views and highlight the urgent need for adaptive management focused on seasonal vulnerability.

Artificial intelligence-assisted technology to reduce turnaround time for rapid diagnosis of infectious diseases.

Significant research has been directed towards cyberattack detection through reactive assistive techniques, utilizing pattern-matching algorithms for scanning system logs and network traffic to identify known signatures. While effective machine learning (ML) models have been developed to automate detection processes-including identifying, tracking, and blocking malware and intruders-comparatively less attention has been given to cyberattack prediction, particularly for time scales extending beyond daily observations. Long-term attack prediction approaches are highly valued, offering defenders extended periods for the development and dissemination of defensive strategies and tools. Long short-term memory (LSTM) networks are frequently chosen among ML models for their strong performance in time series data analysis. This paper presents a novel methodology combining LSTM models with flexible sliding window techniques to improve prediction outcomes and enhance training efficiency. Experiments also incorporate convolutional filters and combined bivariate performance measures, similar to methodologies used in enhancing stock forecasting systems. These substantial contributions are anticipated to offer valuable insights for researchers in this field.

Ship motion prediction is essential in marine engineering, but missing data caused by sensor faults or signal interruptions often degrades the accuracy of long short-term memory (LSTM) models. This study investigates how different missing data rates and imputation methods affect LSTM prediction performance. A ship-motion dataset under various speeds and wave conditions was used to examine model feasibility and hyperparameter sensitivity. Traditional filling strategies, including zero and mean filling, were compared under missing data scenarios. Results show that data loss significantly reduces prediction accuracy. The mean-filling method generally performs better than zero-filling, though its effectiveness decreases with higher data diversity. Proper data clustering can effectively enhance its performance.

Generative modeling of clinical time series via latent stochastic differential equations.

Time-aware attention network for multi-step future prediction in wastewater treatment.

The increasing complexity of network environments poses significant security challenges, particularly in defending against Denial of Service (DoS) attacks. Traditional security solutions are often inadequate, necessitating the development of intelligent procedures. This paper explores innovative approaches using deep learning-based Intrusion Detection Systems (IDS), specifically Bidirectional Long Short-Term Memory (BiLSTM) models, for enhanced attack detection. Our findings demonstrate that BiLSTM models excel in binary classification and outperform in identifying sophisticated attacks within multiclass settings. Two key areas for future research are highlighted: the impact of advanced data processing techniques on IDS effectiveness and the evaluation of Distributed Denial of Service (DDoS) attacks. The study introduces a refined BiLSTM (RLSTM)-based IDS approach and evaluates its performance using datasets CICIDS-2019, CICIDS-2017, and NSL-KDD. The method incorporates preprocessing techniques such as encoding, dimensionality reduction, and normalization. Experimental results indicate high accuracy, with 99.2% for NSL-KDD, 99.4% for CICIDS-2017, and 99.7% for CICIDS-2019 datasets. These results illustrate that the proposed technique provides superior detection capabilities in complex network environments compared to existing methods. This study emphasizes the necessity for advanced security measures and highlights the effectiveness of model in enhancing network protection. • Proposes deep learning–based IDS using a refined BiLSTM (RLSTM) to counter DoS attacks in complex networks. • Achieves high detection performance in both binary and multiclass attack scenarios. • Integrates effective preprocessing techniques, including encoding, dimensionality reduction, and normalization. • Validated on NSL-KDD, CICIDS-2017, and CICIDS-2019 datasets with accuracies up to 99.6%. • Demonstrates superior robustness and detection capability compared to existing IDS approaches.

The transition to sustainable energy positions wind power as a key renewable solution. As demand grows, wind turbines are deployed across diverse terrains. However, wind’s stochastic nature and environmental variability complicate power forecasting, affecting grid stability. The study leverages data-driven techniques to enhance wind power forecasting using high-resolution SCADA system time-series data. Key operational parameters include wind speed, rotor speed, generator speed, nacelle orientation, ambient temperature and power output. A comparative analysis evaluates traditional machine learning models—Linear Regression, Decision Trees, Random Forests, Gradient Boosting and Support Vector Machines—against deep learning models like Long Short-Term Memory (LSTM) networks and a novel Recurrent Neural Network (RNN) architecture. The core contribution is an optimized Bidirectional LSTM-RNN model with permutation layers and attention. These layers capture long-range dependencies and nonlinear interactions in wind data. The structure improves long-range dependency capture and nonlinear interaction modeling. Bidirectionality enables learning from both past and future time steps, while attention mechanisms highlight critical temporal features. Experimental results demonstrate the proposed model’s superior performance, achieving a Mean Absolute Error (MAE) of 0.0994 and Root Mean Square Error (RMSE) of 0.1390, significantly outperforming traditional models (e.g., Random Forest: MAE 86.44, RMSE 220.30) and basic LSTM models (MAE 14.48, RMSE 15.27). Robust cross-validation confirms its ability to generalize across different temporal segments. Feature importance analysis improves interpretability, supporting informed decision-making in wind farm operations. The framework is scalable, modular and well-suited for real-time forecasting applications. The work presents a reliable deep learning model for wind power forecasting, enabling intelligent, data-driven energy management in modern power systems.

This study investigates the predictability of Elementary Cellular Automata (ECA)—simple yet dynamically rich deterministic systems—using two state-of-the-art machine learning models: Long Short-Term Memory (LSTM) networks and Transformer architectures. Although ECAs are governed by deterministic rules, their behavior often exhibits pseudo-randomness and chaotic dynamics, especially under Class III and IV rules. Our experiments assess the ability of LSTM and Transformer models to predict future configurations of ECAs based on past states. Results reveal that LSTMs excel in modeling rules with short- to mid-range dependencies, achieving accuracies above 99% in structured scenarios, while struggling with chaotic rules such as Rule 30. Conversely, Transformers demonstrate superior performance in capturing long-range dependencies, achieving perfect accuracy for rules like Rule 90 and Rule 62, but incurring higher computational costs. These findings underscore the limits of predictability in deterministic yet complex systems and highlight how different neural architectures are suited to distinct forms of structural complexity. The work contributes to a deeper understanding of machine learning’s capacity to model discrete chaotic systems and opens avenues for hybrid approaches in predictive modeling of computationally irreducible dynamics. • Cellular automata enable analysis of chaos in finite discrete systems. • LSTM excels on short-term and locally structured cellular automata rules. • Transformer captures long-range and chaotic dependencies more effectively. • Comparative results reveal trade-offs in accuracy, stability, and cost. • Hybrid models may combine LSTM and Transformer strengths for better forecasts.

Objective. Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery.Approach. Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use.Main Results. The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels.Significance. DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.

The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.

Hybrid propulsion systems increase ship energy efficiency by allowing the sharing of power between diesel engines and battery energy storage systems. However, the long-term efficiency of these types of systems depends on accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries to allow effective charge scheduling, maintenance planning, and reliable navigation. This study uses nine data-driven algorithms, including ensemble methods, recurrent neural networks, and linear models, to examine the RUL of a lithium-ion battery pack installed on a hybrid cargo ship. A 5-fold cross-validation structure was used to preprocess, normalize, and analyze actual operational data gathered during the vessel's service life. To improve the accuracy of predictions, hyperparameter optimization was performed out. Long Short-Term Memory (LSTM), which reduced MAE from 2.87 to 1.46 and RMSE from 12.57 to 6.34 after optimization while retaining a high coefficient of determination (R² = 0.9999), performed the best among the models that were evaluated. The results obtained indicate that condition-based maintenance and energy utilization methods on hybrid ships can be effectively supported by data-driven RUL estimation. In order to enhance generalization and assess integration with real-time propulsion control systems, future research will expand the analysis to multi-vessel datasets.