Search Results (57,598)

Conventional anomaly diagnosis methods often treat process faults and model drift as distinct, independent issues: anomalous behavior is attributed to process problems, whereas drift is seen as a secondary concern. This traditional perspective neglects the fact that, when a fault is detected, the first diagnosis that must be provided regards the source of the observed deviation: a process fault or a model malfunction. In this context, the present study tackles this fundamental diagnosis problem, proposing that effective anomaly diagnosis should distinguish process faults from model inadequacies originating from operational changes. To address this challenge, the Nearest Normal Value (NNV) contribution analysis technique was developed to quantify individual variable contributions through counterfactual analysis. Unlike conventional diagnostic methods that rely on static references, the NNV technique provides contribution profiles that characterize the operational state dynamically. The methodology was validated using three distinct datasets, including actual operational data from an oil production system. On real data, the normalized dispersion index $\left(\mathrm{S}\right)$ decreased from 0.92 to 0.58 during a documented fault (37% change), whereas it changed from 0.76 to 0.63 during an operating mode shift (17% change), showing, thus, distinct contribution signatures for faults versus drift-related regime changes. The findings suggest that incorporating the proposed approach into anomaly diagnosis systems could reduce false alarms and improve diagnostic accuracy in dynamic industrial environments where operating conditions evolve over time. Full article

Precise temperature control is crucial for maintaining product quality and optimizing energy efficiency in multi-zone continuous crystallizers. However, such industrial processes typically exhibit complex nonlinear dynamics and strong coupling effects. More critically, physical constraints often prevent sensor installation, rendering temperatures in key regions unobservable and challenging traditional closed-loop control strategies. To address partial observability and model uncertainty, this paper proposes a Model-Based Reinforcement Learning (MBRL) framework utilizing solely offline historical data. The core innovation lies in developing a Recursive State Space Model (RSSM) that serves not only as a high-fidelity digital twin but, more critically, is deployed as a real-time “virtual sensor” to infer unobservable system states. This virtual sensing capability provides precise state estimates for downstream policy optimization. Additionally, a multi-objective reward function is designed to balance tracking error, stability, and control cost. Experimental results demonstrate that the proposed virtual sensor exhibits exceptional long-term stability, maintaining high fidelity and effectively suppressing error accumulation during long-term multi-step autoregressive predictions. Consequently, the trained agent outperforms traditional Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) controllers, achieving over 67% improvement in temperature tracking accuracy while reducing control action costs by more than 93%, indicating smoother system operation and enhanced energy efficiency. Full article

Rivers are dynamic systems that flow from higher elevations to lowlands, eventually discharging into lakes, seas, or oceans, and play a key role in sustaining ecosystems and supporting human activities. River basin characterisation extends beyond the watercourse itself, encompassing land uses, tributaries and hydromorphological features that influence ecological processes. This review analyses three river basins in northern Portugal, Ave, Douro, and Vouga, using a holistic characterisation approach. These basins represent contrasting river systems in terms of size, hydrological regulation and dominant land uses, while simultaneously being subject to pressures frequently reported in many other river basins in Europe, and around the world. The analysis includes a general basin description, a hydromorphological assessment with emphasis on land use, and an evaluation of water ecological status, with particular focus on estuarine ecosystems. Water quality in the three basins has been strongly influenced by anthropogenic pressures, including industrial and agricultural activities, and wastewater discharges. Although the implementation of the European Water Framework Directive has led to improvements in recent decades, the degree of recovery varies among basins. Persistent challenges, such as nutrient concentrations, microbial contamination, and heavy metal pollution, highlight the need for integrated river basin management and improved monitoring strategies. This review provides transferable insights for the management of river basins facing similar environmental pressures. Full article

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

Autophagy is increasingly recognized as a context-dependent regulatory process that links cellular quality control with systemic metabolic and neurological homeostasis. However, how distinct autophagy pathways contribute to disease progression, and how they are dynamically modulated by host–microbiota interactions, remain incompletely understood. In this review, we synthesize recent advances in the molecular regulation of macroautophagy, microautophagy, and chaperone-mediated autophagy (CMA), with a particular emphasis on selective autophagy and its disease-specific functions. We examine emerging evidence implicating autophagy as a bidirectional modulator in neurodegenerative and metabolic disorders, highlighting conditions under which autophagy exerts protective versus maladaptive effects. Importantly, we integrate recent findings on the microbiota–gut–brain axis to illustrate how microbial signals reshape autophagic responses and influence disease susceptibility and progression. Finally, we summarize current progress and limitations in autophagy-targeted therapeutic strategies, including nanomedicine-based delivery systems, and propose conceptual frameworks to guide the development of precise, context-aware autophagy interventions. This review provides an updated and integrative perspective that bridges molecular mechanisms, host–microbiota crosstalk, and translational opportunities in autophagy-related diseases. Full article

To address the issues of excessive current stress and the power dead zone associated with conventional phase-shift modulation in dual-active-bridge (DAB) converters, a hybrid optimized modulation strategy based on dual-side asymmetric duty modulation (ADM) is proposed. The proposed strategy aims to minimize the peak-to-peak current stress by introducing two distinct operating modes of the converter. A dynamic compensation mechanism based on mode switching is developed, enabling a coordinated dual-mode modulation to achieve minimum peak-to-peak current stress over the full power operating range. In addition, a virtual voltage control scheme is incorporated to enhance the dynamic response and stability of the system. Finally, experimental results obtained from a laboratory prototype verify that the proposed strategy effectively reduces the peak-to-peak current stress while significantly improving the dynamic performance of the DAB converter. Full article

Decarbonizing ocean-going shipping requires decision-grade environmental evidence for propulsion transitions, yet conventional LCA relies on static inventories that inadequately represent dynamic operations and route-dependent renewable generation. This study evaluates well-to-wake (WtW) Global Warming Potential (GWP) for two large container ships operated by a Korean company under four scenarios: conventional diesel main engine, diesel–electric with onboard generator, full battery-electric supplied by shore electricity from the Republic of Korea grid, and battery-electric with a route-resolved solar PV system. A Live-LCA (LLCA) framework couples LCI data with MATLAB/Simulink power and propulsion modeling driven by actual operating profiles and route environmental conditions to generate operational inventories for impact calculation. Diesel–electric operation increases annual WtW GWP by over 26% for both ships versus the baseline of a conventional diesel main engine, whereas shore-electric battery operation is able to reduce WtW GWP by around 40% versus diesel–electric. With limited PV installation, additional reductions are marginal. Depending on electricity profile, it can increase battery-electric GHG emissions by approximately 27%, highlighting sensitivity to electricity evolution. Overall, electric propulsion delivers climate benefits only when paired with low-carbon electricity, and LLCA enables operationally and route-grounded LCA for large container ships. Full article

Abiotic stresses severely constrain the growth, yield, and quality of horticultural plants, collectively posing major challenges to sustainable production under changing climatic conditions. Nitric oxide (NO) is a key signaling molecule that modulates plant responses to abiotic stress by integrating with redox regulation systems, hormonal crosstalk pathways, ion homeostasis mechanisms, and transcriptional control networks. Rather than functioning as an isolated regulator, NO participates in dynamic signaling frameworks whose outcomes depend on concentration, timing, cellular redox status, and interaction with other signaling molecules. This review synthesizes current knowledge on NO-mediated mechanisms contributing to abiotic stress tolerance and examines their relevance to sustainable horticultural crop management. After outlining the historical recognition of NO as a plant signaling molecule, we discuss stress-responsive NO-dependent processes, including S-nitrosylation-based post-translational modification, NO–reactive oxygen species (ROS) interactions, and the modulation of stress-responsive transcriptional programs. The roles of NO in tolerance to drought, salinity, extreme temperature, and heavy metal stress are analyzed with emphasis on experimentally supported physiological and molecular responses. We further evaluate evidence from fruit, vegetable, ornamental, and medicinal crops, highlighting how NO-associated signaling correlates with yield stability, quality-related traits, and post-harvest performance under stress conditions. Finally, NO-based strategies such as priming, donor application, and integration with biostimulants are critically assessed in the context of climate-resilient and sustainable horticulture, with attention to translational constraints and field-level feasibility. By connecting mechanistic insights with applied considerations, this review provides a structured framework for evaluating the potential and limitations of NO-based approaches in abiotic stress management of horticultural crops. Full article

This study proposes a self-calibration method based on zoned reference transfer for real-time temperature monitoring of the Fengyun-4 (FY-4) Microwave Satellite payload. It aims to correct the effects of calibration coefficient degradation and instrumental background drift in uncooled infrared temperature measurement systems during on-orbit operation. The method dynamically updates the calibration reference through alternate observations of a Fixed External Blackbody and an Insertable Internal Blackbody within the field of view. Concurrently, it utilizes Masked Zone pixels to sense and compensate in real-time for the common-mode background drift caused by camera temperature variations. This approach jointly ensures long-term measurement stability and instantaneous accuracy without the need for complex scanning mechanisms. Ground validation experiments demonstrate that the proposed method suppresses background radiation drift by over 72%. Under dual-camera cross-validation, the equivalent blackbody temperature retrieval errors for low-temperature targets (230–250 K) were significantly reduced from approximately 3 K to roughly 0.4 K. Furthermore, based on a comprehensive uncertainty budget, the absolute expanded uncertainty is evaluated to be better than 0.87 K (k = 2) at 300 K. The proposed method provides a reliable and compact technical solution for high-precision infrared thermometry of moving components on-orbit. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS${}^{\circledR }$ platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article

Real surveillance systems require weakly supervised video anomaly detection due to the fact that long untrimmed videos do not always have accurate temporal labels. Models will be required to label a video as normal or abnormal and also to identify sparse anomaly areas with mere video-level supervision. In this paper, we introduce ST-CapsNet, which is a spatio-temporal capsule network that enhances weakly supervised localization of anomalies by using a structured representation and temporal agreement. Every video is broken down into 32 parts and coded with 512-dimensional 3D CNN (Convolutional Neural Network) features. Primary capsules record patterns of segments as vectors, and temporal capsules are created by dynamic routing over time, enabling the related abnormal segments to provide support to a common event representation. Training is based on a multiple-instance learning model that has a bag-level BCE (Binary Cross-Entropy) loss, a ranking loss between abnormal and normal separation, and smoothness and sparsity regularization to impose temporal consistency and sparse event behavior. The weakly supervised FAST (Focused and Accelerated Subset Training) split experiments on the UCF-Crime weakly supervised FAST split demonstrate that ST-CapsNet is better than strong baselines. The findings indicate that capsule routing is an effective part of the whole temporal reasoning of weakly supervised surveillance anomaly detection. Full article

In 2024, an abrupt drought-to-flood transition (ADFT) event occurred in Henan Province, China, causing severe losses to agriculture and the economy. Predicting the spatiotemporal evolution of such compound extremes remains challenging at the subseasonal scale. This study employs soil moisture percentiles to identify local droughts and floods, connects them into coherent patches, and detects an ADFT event spatiotemporally. The proposed three-dimensional identification method is further applied to evaluate the ECMWF S2S reforecasts of the 2024 ADFT event. At a 1-week lead, the ECMWF ensemble mean successfully captures the transition. However, the spatial extent is underpredicted substantially at a 2-week lead. In terms of probabilistic forecast, the Brier skill scores for drought, transition, and flood stages are 0.38, 0.57, and 0.38 at a 1-week lead, respectively. However, these scores drop sharply at a 2-week lead, particularly for the transition and flood stages. The decreased forecast skill is jointly influenced by internal dynamical errors in the model and biases in the positions of the subtropical high- and low-pressure systems at long lead. This study assesses the capability of a numerical model to predict a compound extreme from both deterministic and probabilistic perspectives, and highlights the critical role of atmospheric circulation in achieving skillful prediction. Full article

The growing need to safeguard sensitive data in various fields, including in relation to education, banking over the phone, private voice conferences, and the military, has grown as dependence on technology in daily life has increased. Encryption schemes based on chaotic systems are among the most commonly utilized approaches in the security field due to their high levels of safety and reliability. This study proposes a secure audio encryption framework based on the Chameleon chaotic algorithm implemented on a Xilinx ZedBoard Zynq-7000 FPGA. The system was designed using a fixed-point arithmetic format with 32-bit precision (eight integers; 24 fractional bits) with the Xilinx System Generator in MATLAB Simulink R2021b and verified using Vivado. The Chameleon Chaotic System, characterized by its transition from self-excited to hidden attractors through parameter variation, adds complexity to the system dynamics and strengthens the encryption algorithm. The Adaptive Feedback Control technique was applied to synchronize the signals. These methods enhance the security of audio data by ensuring robust and fast synchronization during transmission. The performance of the proposed system was assessed using correlation analysis, the mean squared error, histogram analysis, and audio spectrogram analysis. The system demonstrated strong encryption capabilities with low correlation values (−0.0033). In decryption, they achieved high fidelity with a correlation exceeding 0.999 in noise-free conditions and above 0.9933 under 20 dB AWGN. Adaptive Feedback Control showed superior decryption precision with lower MSEU and higher PSNR, confirming its effectiveness under noisy environments. Full article

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD).To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2kW, an ESB with a capacity of 10kWh and an EV battery with a capacity of 4kWh, which are linked by bidirectional DC/DC converters. A 30kVA bidirectional inverter, along with an LCL filter, is connected between the 500V DC bus and 440V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs. Full article

While thinnings immediately reduce aboveground biomass, they promote growth by releasing the remaining trees from competition. The biomass removed in thinnings can be used for energy, thus enabling financial returns prior to final harvest and contributing to the global share of renewable energies. In this study, the effects of thinning on stand structure dynamics and potential residential bioheat utilisation scenarios are assessed for a broadleaved mixed even-aged stand. The results demonstrate that ten years after thinning, aboveground biomass increased, ensuring system sustainability and carbon stocks. Furthermore, an average potential yield of 1.1 Mg·ha⁻¹·a⁻¹ (dry basis) of low-ash forest by-products was obtained, offering a sustainable supply of solid biofuels. However, the energy conversion route chosen has major impacts on the solid bioenergy demand and sustainability. Based on theoretical scenarios, upgrading from traditional fireplaces to more efficient combustion systems may reduce the specific biomass consumption up to eight times for residential heat production. The results obtained in this study highlight the challenge and need to use thinning biomass sustainably in the face of growing bioenergy demands. Full article