Search Results (162)

Multivariate structural-response classification supports bridge monitoring by integrating synchronized measurements from multiple sensors and assigning an observation window to a predefined structural or loading condition. The main technical challenge is to compress temporally and spatially distributed sensor information into a fixed-length representation without losing transient or localized condition-sensitive features. However, previous studies have mainly focused on backbone architecture design, whereas the influence of sequence-level aggregation has rarely been isolated under a controlled backbone and training configuration. In this study, xLSTM-m is proposed as a modified variant of the extended Long Short-Term Memory (xLSTM) network for multivariate bridge-response classification. The conventional last-hidden-state readout is replaced with masked mean pooling over all valid hidden states, while the hybrid sLSTM/mLSTM backbone and the remaining training configuration are kept unchanged. This controlled design isolates the sequence-level aggregation strategy as the only architectural variable. A comparative evaluation is conducted using eight neural models. These models include xLSTM-m, the baseline xLSTM, and six Transformer-based architectures. Three bridge-monitoring datasets are considered: an FBG strain-response dataset and a PCB Piezotronics acceleration-response dataset acquired from a laboratory-scale cable-stayed bridge model, together with the field-scale Z24-9Setup acceleration benchmark. A strict five-fold cross-validation protocol is adopted. The proposed xLSTM-m ranks first on all three datasets. It achieves a mean accuracy of 95.29%, a mean F1-score of 95.57%, and the lowest inter-fold standard deviation of 0.84. Relative to the strongest Transformer baseline for each dataset, the corresponding accuracy gains are 6.30 percentage points for FBG, 3.66 percentage points for PCB, and 31.76 percentage points for Z24-9Setup. These results indicate that, for the evaluated bridge strain and acceleration datasets, sequence-level aggregation substantially affects both classification accuracy and inter-fold stability. Further validation using quasi-static responses, environmental variables, other structural systems, and unseen operating conditions is required before broader applicability across the SHM domain can be established. Full article

Many continuous industrial processes are non-linear, multi-variable, subject to transport or reaction delays, and described by operating-point-dependent parameters. These characteristics reduce the reliability of fixed models used in model predictive control (MPC), particularly when slow disturbances, regime changes and operational constraints are dominant. This paper proposes an output-bias reference-correction framework based on Long Short-Term Memory (LSTM) networks for predictive control of industrial processes with delays and variable parameters. The dominant dynamics are represented by a fixed compact linear nominal model in deviation coordinates; this model drives a standard constrained MPC that remains structurally unchanged throughout operation. The persistent output bias between the actual process and the nominal model is learned from closed-loop data by an LSTM network. At each sampling step, the predicted bias is used to correct the future reference trajectory fed to the nominal MPC, so that the controller compensates for model–process mismatch without modifying its internal model, constraint set or solver. The final implementation uses a one-step bias predictor, selected by ablation, and it extends this one-step estimate across the MPC horizon by exponentially decayed persistence. A closed-loop bias-error bound links the LSTM identification error, the adaptive correction gain and the resulting tracking deviation. The framework is illustrated using a mining thickener, a representative process characterised by slow dynamics, delays, variable parameters and stringent safety constraints. A three-controller Monte Carlo study compares the nominal MPC, a classical offset-free MPC and the proposed LSTM-MPC, and it highlights the resulting tracking–actuation–constraint trade-off. Applied to a mining thickener, the LSTM corrector reduces the first-step output-prediction RMSE by 96.6% (FIT from $-14.8\%$ to $96.1\%$). In a 50-scenario Monte Carlo closed-loop evaluation, the LSTM-MPC outperforms the nominal MPC in 92% of scenarios on RMSE while using substantially less actuator activity than the offset-free baseline (mean input total variation: 67.0 vs. 119.4). Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

Bioethanol production from lignocellulosic biomass remains energy-intensive, and its energy performance can be affected by equipment degradation, utility disturbances, and operating variability. This study developed a degradation-aware mechanistic–AI framework for energy forecasting, anomaly detection, maintenance-oriented interpretation, and multi-objective optimization in bioethanol production under limited-data conditions. Reduced-order energy models were formulated for pretreatment, hydrolysis–fermentation, and ethanol purification. Equipment deterioration was represented through heat-transfer fouling, column-efficiency decline, and pump-efficiency decay. Condition-dependent modifiers were introduced to account for load-related degradation and intervention-related partial recovery. Benchmark-constrained synthetic time-series datasets were generated under baseline, accelerated-degradation, condition-dependent, stress, and data-quality perturbation scenarios. Empirical baselines and machine-learning models were compared for specific energy consumption prediction, with uncertainty reported using confidence intervals. The long short-term memory model achieved the lowest prediction errors under both baseline and stress conditions. Robustness testing showed that sensor drift, missing values, and outliers increased forecasting and anomaly-detection uncertainty. Sensitivity analysis identified degradation coefficients, seasonal disturbance, and anomaly-threshold selection as influential factors. Multi-objective optimization revealed trade-offs among specific energy consumption, ethanol purity, and equipment-health penalty. The proposed framework should be interpreted as a benchmarked methodological platform rather than a plant-validated maintenance or control system. Plant-specific deployment requires calibration with operating records, maintenance logs, cleaning records, and sensor-quality assessment. Full article

Small water bodies serve as fundamental units and key conduits for material cycling and hydrological processes at the watershed scale. However, quantitatively identifying the mechanisms driving their area evolution under multi-factor coupling in different scenario simulations remains challenging. This study focused on the small water bodies in the Chaohu Lake Basin. An Otsu algorithm was applied to establish a basin-scale database of small water bodies, while the GeoDetector model was integrated to reveal the spatiotemporal driving mechanisms of multi-factor coupling behind their evolution. Furthermore, a Long Short-Term Memory (LSTM)–Transformer model was modified to simulate future scenarios of small water body area dynamics. The results indicated that, from 1995 to 2024, the area of small water bodies in the Chaohu Lake Basin exhibited a fluctuating decreasing trend (wet season: 186–269 km²; dry season: 110–253 km²). In terms of spatial distribution, the small water bodies exhibited an unbalanced distribution pattern characterized by wide dispersion alongside regional clustering. Results from the GeoDetector model revealed that land use type (q = 0.711) and evapotranspiration (q = 0.526) were the dominant drivers of variations in small water body areas. LSTM–Transformer simulations (R² = 0.92, p < 0.01) suggested that, under temperature, precipitation, and land use change scenarios, the small water body areas in the Chaohu Lake Basin will exhibit distinct seasonal variation characteristics, with scenario-dependent differences in fluctuation amplitude and peak–trough timing. These results offer theoretical support for the protection of small water bodies and integrated water resource management in the Chaohu Lake Basin. Full article

The rapid integration of photovoltaic (PV) generation into active distribution networks (ADNs) creates a fundamental tension between maintaining tight voltage regulation and accommodating high distributed energy resource (DER) penetration levels. Conventional voltage control methods such as the droop control operate locally without coordination, while centralized optimal power flow requires full network observability and reliable real-time communication. Multi-agent deep reinforcement learning (MADRL) methods provide adaptive coordination but suffer from long training times and algorithmic complexity that prevent direct deployment on embedded inverter hardware. This paper proposes the Optimal Historical Selection and Forecasting (OHSF) scheme: a physics-informed long short-term memory (LSTM) network combined with an online sensitivity-based correction loop for medium-voltage ADNs. A composite loss function incorporating data-driven regression, an inter-PV voltage sensitivity penalty, and an inverter capability constraint produces reactive power setpoints that are inherently aware of physical limits, while the correction loop refines the predictions using real-time AC power flow feedback. The OHSF scheme supports a centralized full-network mode and a decentralized fallback mode in which the trained weights run locally on each inverter. Simulations under worst-case PV placement and network reconfiguration on the modified IEEE 33-bus and 69-bus test systems achieve an average voltage deviation across all PV buses of $0.701\%$ and $0.601\%$ at $172\%$ DER penetration on the 33-bus system, and $0.804\%$ and $0.806\%$ at $242\%$ DER penetration on the 69-bus system, while training 32 to 49 times faster than state-of-the-art MADRL methods. Full article

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

Owing to the fact that the conventional Temperature Drift Errors (TDEs) precise estimation model of Capacitive MEMS-Gyros (CMG) has incomplete Temperature Correlated Quantities (TCQ) and an imperfect parameter identification method to reduce bias stability, a BO-LSTM-based TDE precise estimation model using thermal-induced physical characteristics variation analysis is proposed. By analyzing microstructural deformation in CMG- and Si-based materials’ stiffness deterioration caused by thermal-induced physical characteristics variation, complete TCQ are traced, including ambient temperature variation ∆T and its square root ∆T^1/2 plus its higher orders (∆T², ∆T³, ∆T⁴), a modified TDE precise estimation model is formed. Long Short-Term Memory (LSTM) is applied to identify the modified model’s parameters owing to the typical time series characteristics of TDE and TCQ. In addition, Bayesian Optimization (BO) is introduced in LSTM to show a good guide for LSTM’s optimal hyperparameters. The modified model is implemented with BO-LSTM and compared with the conventional model based on Radial Basis Function Neural Network (RBFNN) in bias stability. The experimental results show that the modified model can more accurately estimate the TDE of CMG in a timely and improves its bias stability by 20%, which decouples the temperature dependence of Si-based material significantly and enhances the environmental adaptability of CMG in complex conditions remarkably. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

In the ecologically fragile West Liao River Basin, characterizing groundwater dynamics is crucial for sustainable water management. Using 2000–2016 groundwater level data, this study applies Seasonal-Trend decomposition using Loess (STL) and change-point detection to analyse trends. Driving factors are quantified via random forest combined with SHapley Additive exPlanations (SHAP) analysis, and a novel STL–Long Short-Term Memory (STL-LSTM) hybrid model is developed for forecasting. Key findings include: (1) Groundwater levels declined persistently, with a significant change point in 2009. The post-2009 decline rate accelerated to −0.749 m/yr, a 55.7% increase. (2) Statistical attribution reveals that soil moisture (43.5%) and climatic factors (29.0%) are the primary predictors of groundwater variability. The dominance of soil moisture highlights the key role of agricultural irrigation, which strongly modifies soil water dynamics during the growing season. (3) The STL-LSTM model achieves optimal predictive performance (R² = 0.8805, RMSE = 0.7081 m), demonstrating enhanced accuracy for non-stationary sequences. This integrated framework combines trend diagnosis, driver interpretation, and hybrid modelling, offering scientific support for precise groundwater management in semi-arid agricultural basins. Full article

High penetration of renewable energy sources (RESs) introduces significant power fluctuations, threatening voltage and frequency stability in modern power systems. This paper presents an integrated framework for static voltage stability assessment and stability-constrained optimization of under-frequency load shedding (UFLS) in renewable-dominated grids. A low-conservativeness analytical criterion is first derived for static voltage stability margin assessment. Then, a hybrid Deep Belief Network–Long Short-Term Memory (DBN–LSTM) model is developed for accurate renewable power forecasting, capturing temporal variability and uncertainty. Finally, UFLS-based stability-constrained dispatch is formulated to prevent voltage collapse, enhance the system stability, and minimize RES curtailment. Simulations on a modified IEEE benchmark system demonstrate that the proposed approach improves voltage and frequency stability while maintaining high renewable energy utilization. Full article

Amyloid-β peptides (Aβ) are considered a main culprit of Alzheimer’s disease (AD), leading to synaptic dysfunction and memory deficits. Although studies in animal models of AD converge to show alterations of synaptic plasticity, namely of long-term potentiation (LTP), the mechanisms through which Aβ affects synaptic function remain to be unveiled. In this study, we established experimental conditions showing that the acute exposure of mouse hippocampal slices to optimized concentrations of Aβ impaired short-term (PPF-paired-pulse facilitation) and long-term (LTP-long-term potentiation) plasticity without altering basal synaptic transmission. We observed that the elimination of extracellular adenosine with adenosine deaminase abrogated the impact of Aβ on synaptic plasticity, showing a mandatory involvement of extracellular adenosine in the neurophysiological effects of Aβ. Additionally, inhibiting adenosine receptor function with caffeine, as well as selectively blocking adenosine A₁ receptors (A₁R) with DPCPX, or adenosine A_2A receptor (A_2AR) with either an antagonist SCH58261 or through knocking out A_2AR, demonstrated that acute Aβ modified mouse hippocampal PPF via A₁R and LTP through A_2AR. Furthermore, the use of slices from mice bearing forebrain-neuron A_2AR deletion, along with the application of α,β-methylene ADP, a CD73 inhibitor, confirmed that the neurophysiological actions of Aβ on hippocampal LTP occur selectively through the overfunction of neuronal A_2AR via CD73-mediated formation of extracellular adenosine. Overall, the exploitation of a neurophysiological model of early AD, based on the acute administration of Aβ to hippocampal slices, confirmed the critical involvement of adenosine signaling in the impact of Aβ on synaptic plasticity. Full article

Ketone bodies (KBs) are the only energy substrates oxidized by the brain, whose concentration in the circulation can greatly increase when a physiological situation requires it. For example, when an adult human fasts for two days, circulating KBs rise twenty-fold from ~0.1 to ~2 mM. As a fuel, KBs provide the brain with acetyl-CoA that produces ATP or glutamate, notably in certain brain regions. Remarkably, KBs activate the expression of their own cerebral transporters and KB-utilizing enzymes so that circulating levels determine cerebral utilization of KBs. Throughout evolution, the energetic role of KBs has been crucial for the metabolic homeostasis of humans endowed with a large brain and facing unpredictable periods of food shortage. Paradoxically, the brain of modern, regularly fed humans whose ordinary blood KBs are ~0.1 mM, has access to much fewer circulating sources of energy than that of their distant ancestors. KBs can modify certain proteins post-translationally, for example, histones through lysine-butyrylation. KBs could act as short- or long-term epigenetic messengers. These properties of KBs might allow a fetus to directly sense maternal starvation and adapt their cerebral metabolism to this situation, possibly preparing for nutritional constraints in extra-uterine life. KB transcriptional and epigenetic properties could also enable the postnatal organism to retain a molecular memory of its own starvation episodes. No other energy substrate, such as glucose or lactate, has such capacities. Medicine turned its attention to KBs a century ago. Indeed, KBs are the only energy substrates whose circulating levels can be increased, and nutritional interventions can alter them under free-living conditions. This property opens broad prospects for ketogenic diets (KDs) to prevent or rescue neurodegenerative diseases characterized by glucose hypometabolism, notably Alzheimer’s disease (AD). However, KDs have not yet found real medical applications, for reasons that are discussed. Full article