Search Results (10,225)

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

Accurately characterizing the kinematics of slow-moving urban landslides remains a major scientific and operational challenge, because no single monitoring technique can simultaneously provide spatially continuous deformation patterns and reliable three-dimensional displacement measurements. This study investigates the spatial and temporal evolution of a slow-moving landslide affecting the University of Azuay campus in Cuenca (Ecuador), where ongoing ground deformation has caused structural damage to several buildings. An integrated monitoring strategy combining GNSS measurements, Sentinel-1 multi-temporal DInSAR analysis, and geophysical investigations (ERT and seismic profiling) was adopted to characterize landslide kinematics and constrain subsurface conditions. GNSS observations revealed that the north–south displacement component was dominant, with cumulative displacements exceeding 20 cm during the monitoring period (from July 2021 to June 2024), while east–west displacements were on the order of 10 cm. MT-DInSAR analysis delineated the spatial extent of the unstable area and identified mean deformation rates of up to approximately −1.5 cm/year in the central sector of the landslide. The combined interpretation of geodetic and geophysical data indicates that slope instability is controlled by saturated fine-grained layers and mechanical contrasts, with the basal sliding zone associated with weak levels of the Mangan Formation. Overall, the results demonstrate the value of a multi-sensor, component-wise monitoring strategy for improving the reliability of deformation estimates and for supporting landslide risk assessment and land-use planning in complex urban environments. Full article

Cervical cytology screening remains dependent on manual analysis, which is time-consuming and subject to variability. This study proposes a leakage-free hybrid deep learning framework for multiclass classification of cervical cells extracted from whole-slide Papanicolaou images. A fine-tuned DenseNet121 feature extractor was combined with three classifiers: Support Vector Machine (SVM), Stacked Extreme Learning Machine (SELM), and Cascaded Deep Forest (CDF). Experiments were conducted on the CRIC Cervix Collection dataset using slide-level data partitioning and group-aware stratified 7-fold cross-validation. Model comparison followed a paired non-parametric protocol (Friedman test with Wilcoxon post hoc and Holm correction). DenseNet121 + CDF achieved the highest cross-validation Accuracy (0.7370 ± 0.0357), significantly outperforming SVM (0.6644 ± 0.0287) and SELM (0.6431 ± 0.0471) (χ²(2) = 11.14, p = 0.0038; Kendall’s W = 0.79). Independent testing showed competitive generalization across models. These results support the statistical robustness of the Cascaded Deep Forest-based hybrid architecture for multiclass cervical cytology classification under realistic slide-level conditions. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

RV reducers are vital components in industrial robots and precision equipment, where the fatigue life of the crank arm and support bearings critically influences the overall system longevity. This study presents a comprehensive performance evaluation, with a specific focus on contact mechanics and wear analysis of these critical bearings. A theoretical mathematical model for force analysis is established based on static mechanics, which is further extended to incorporate wear depth prediction based on contact pressure and sliding velocity. To validate this model and investigate bearing behavior in detail, a high-fidelity parametric simulation model is developed using MASTA software. The simulation results, encompassing contact stress, shear stress, and wear patterns, demonstrate good correlation with the predictions from the theoretical mathematical model, effectively verifying its accuracy for performance and life assessment. The systematic analysis confirms that both the investigated tapered roller and needle roller bearings meet the design requirements. This integrated approach of theoretical modeling, which includes wear analysis, and software simulation provides a reliable methodology for assessing bearing performance and fatigue life, offering significant value for the design optimization and reliability enhancement of RV reducers. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

Melanoma is characterized by a high mutational burden making it an established model for studying tumor neoantigens and developing strategies for personalized immunotherapy. In this study, a reproducible bioinformatics pipeline was developed and implemented for the identification and prioritization of candidate neoantigens derived from non-synonymous somatic mutations in melanoma, using genomic data from the MSK-IMPACT cohort (mel-mskimpact-2020; n = 696) and comparative reference information from TCGA-SKCM. From the somatic mutation annotation file (MAF), 16,311 non-synonymous mutations were filtered, from which 50,480 mutant 8–11-mer peptides were generated using a sliding-window approach centered on the mutated position. Peptide–HLA class I binding affinity was predicted using MHCflurry 2.0 across six representative alleles (HLA-A*02:01, HLA-A*24:02, HLA-B*35:01, HLA-B*39:05, HLA-C*04:01, and HLA-C*07:02). Candidate prioritization was initially based on predicted binding percentile (rank ≤ 2), identifying 12,209 peptide–HLA combinations with high predicted binding affinity. To refine candidate selection, additional computational analyses were incorporated, including proteasomal cleavage prediction using NetChop 3.1 and estimation of T-cell epitope immunogenicity using the Immune Epitope Database (IEDB) immunogenicity predictor. Furthermore, a direct comparison between mutant (MUT) and corresponding wild-type (WT) peptides was performed using Δaffinity and Δrank metrics to evaluate the predicted impact of somatic mutations on HLA binding. The analysis revealed a predominance of peptides associated with the HLA-B locus, particularly the allele HLA-B*35:01, among the interactions with the lowest predicted binding percentiles. Several high-ranking peptide candidates were derived from genes with known roles in melanoma biology, including PLCG2, GATA3, AKT1, PTEN, PTCH1, and SMO. Overall, the integrative computational framework implemented in this study enables the systematic prioritization of candidate neoantigens derived from non-synonymous mutations in melanoma. This pipeline provides a reproducible strategy for exploring tumor neoantigen repertoires and may serve as a foundation for subsequent experimental validation and for studies related to neoantigen-based immunotherapies and immunopeptidomics. Full article

Aramid fiber reinforced polymers (AFRPs) are widely used in aerospace and defense structures because of their high specific strength, impact resistance, and damage tolerance. However, severe burr formation during machining remains a major obstacle to achieving high surface integrity and dimensional accuracy. In particular, the mechanism by which tool edge radius affects burr formation in AFRP cutting has not yet been clarified quantitatively. To address this issue, this study develops an analytical model for the orthogonal cutting of AFRPs to reveal the burr formation mechanism associated with tool edge radius. The model, established on the basis of contact mechanics and fracture theory, predicts fiber deflection, cutting force evolution, fracture behavior, and burr length under different contact and boundary conditions. The results show that tool edge radius governs burr formation through a contact–state transition mechanism. When the edge radius is below a critical threshold, localized point-contact-like interaction promotes stress concentration and fiber fracture, leading to relatively clean material removal. When the edge radius exceeds this threshold, the interaction evolves toward extended contact and sliding, which suppresses complete fiber fracture and results in pronounced burr retention. Experimentally, increasing the edge radius from 5.6 μm to 110.3 μm increased the maximum burr height from 3.19 μm to 83.58 μm, corresponding to an increase of approximately 2520%. The predicted burr evolution agrees well with the experimental observations in both trend and characteristic magnitude. This study provides a mechanistic and predictive understanding of burr formation in AFRP machining and offers practical guidance for cutting edge preparation, tool wear control, and process optimization in high-quality composite machining. Full article

To enhance the trajectory tracking accuracy and lateral stability of distributed-drive electric vehicles, a hierarchical cooperative control strategy optimized by the Genetic–Firefly Algorithm (G-FA) is proposed. First, bottom-level controllers for trajectory tracking utilizing a Linear Quadratic Regulator (LQR) and stability relying on Sliding Mode Control (SMC) are jointly optimized offline using the G-FA to address the limitations of empirical parameter tuning and effectively mitigate chattering. Compared to traditional Nonlinear Model Predictive Control (NMPC), which relies on computationally demanding dynamic programming, the proposed G-FA acts as an efficient approximate optimization method that significantly reduces the online computational burden while maintaining high control accuracy. Second, an adaptive cooperative mechanism based on desired yaw rate correction is introduced. By constructing two reference benchmarks—“tracking-oriented” and “stability-oriented”—a cooperative weighting coefficient adapts the fusion of control objectives based on the vehicle’s stability state. Hardware-in-the-loop (HIL) simulation results demonstrate that, under high-adhesion double lane change maneuvers, the proposed strategy reduces peak lateral error and sideslip angle by 31.53% and 28.08%, respectively, compared to traditional LQR. In low-adhesion S-curve limit maneuvers, where traditional LQR fails, the proposed strategy outperforms the NMPC benchmark, further reducing these indices by 61.98% and 8.33%, respectively, significantly improving control performance under extreme conditions. Full article

Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail dependencies among random variables. However, existing research has not thoroughly explored the multivariate dynamic coupling characteristics related to forecasting errors, nor has it sufficiently considered the complementary advantages among different Copula functions. To address this, we propose a method for forecasting photovoltaic power output probabilities days in advance, integrating multivariate dynamic Copula functions with reinforcement learning. First, to capture the time-varying structure of photovoltaic power-related variables, we introduce a sliding time window for segmented modeling of historical data, fitting marginal probability distributions for predicted irradiance, forecasting power, and forecasting error. Second, a joint probability distribution of dynamic Gaussian Copula and t-Copula is constructed based on historical samples within the time window, generating a probabilistic prediction interval for the target time. Finally, reinforcement learning is employed to adaptively combine the probability prediction intervals derived from both Copula types, yielding the final photovoltaic power probability forecast. Simulations using actual operational data from a photovoltaic power plant in Shanxi Province validate the effectiveness of the proposed method. Full article

Early-age cracking is a common issue in the prefabrication of large-scale box girders, and the application of pre-tensioning techniques to introduce pre-compressive stress is an effective measure to mitigate such cracking. To determine an optimal pre-tensioning scheme for the 60 m large-scale box girder used in the Ningbo–Xiangshan intercity railway, friction coefficient tests and field stress monitoring were conducted. A numerical model simulating the pre-tensioning process of the box girder, accounting for the constraint of the steel formwork, was developed using Abaqus 2021. Based on the validated finite element model, a parametric study was performed to investigate the effects of friction coefficient, internal formwork roof, and prestressing tendon arrangement on the pre-compressive stress. The results indicate that the bond force between cast-in-place concrete and steel formwork is approximately 2.1 times the sliding friction force. As the friction coefficient increases, the pre-compressive stress in the box girder exhibits a notable decreasing trend. For the critical midspan section S40, the inclusion of frictional effects results in a more uniform distribution of pre-compressive stress. Compared to the case without the internal formwork roof, its inclusion leads to a 9.2% to 10.4% reduction in pre-compressive stress at section S40. To mitigate prestress losses transmitted from the ends to the midspan section, it is recommended that the internal formwork be completely removed prior to prestressing tensioning. The pre-compressive stress in the box girder varies considerably with different prestressing combinations. The comparative analysis of different prestressing combinations reveals substantial variations in pre-compressive stress distribution. After evaluating multiple schemes, the optimal pre-tensioning sequence for the 60-m railway box girder is determined as follows: sequentially tensioning tendon groups F1-2, F1-4, F1-5, F1-6, and B2-3, with an anchorage stress controlled at 558 MPa. This scheme ensures that all critical sections of the box girder remain in a pre-compressive state. In particular, the pre-compressive stress at the key midspan section S40 ranges from 1.12 to 1.26 MPa, achieving the desired effect and effectively suppressing early-age cracking in the large-scale box girder concrete. Full article

This paper presents a systematic experimental comparison of three sliding-mode-based current control strategies—traditional sliding mode control (SMC), fast terminal sliding mode control (FTSMC), and super-twisting sliding mode control (STSMC)—applied to a grid-connected five-level active neutral point clamped flying capacitor (5L-ANPC-FC) inverter. Unlike existing studies that typically investigate a single controller or topology, this work provides a fair, hardware-validated benchmark under identical operating conditions, enabling a clear assessment of convergence speed, harmonic performance, robustness, and implementation complexity. All controllers are designed within a unified framework and their stability is rigorously analyzed using Lyapunov theory. Experimental evaluations are conducted under steady-state operation, step changes in reference current, grid-voltage sag/swell, and DC-link voltage variations. The results demonstrate that while all three controllers ensure robust current tracking and inherent DC-side capacitor voltage balancing without additional control loops, FTSMC achieves the lowest grid-current total harmonic distortion (THD) and fastest convergence. STSMC effectively suppresses chattering, and traditional SMC offers a simple yet reliable baseline solution. The presented findings provide practical design guidelines for selecting appropriate sliding-mode controllers in high-performance multilevel inverter applications. Among the assessed control techniques, FTSMC has the most rapid dynamic response, characterized by a rise time of 0.1 ms and a minimal grid-current THD of 1.95%, indicating exceptional steady-state and transient performance. STSMC markedly diminishes chattering and ripple, attaining a THD of 2.04% with enhanced waveform smoothness relative to traditional SMC. Conversely, traditional SMC offers a more straightforward implementation but demonstrates elevated ripple and THD levels of around 2.29%, along with a peak current inaccuracy of 6–8%. The results underscore the trade-offs between implementation simplicity, dynamic responsiveness, and harmonic performance of the evaluated control techniques. Full article

This work focuses on the study of tribo-mechanical and microstructural properties of TiCN/ZrCN multilayer coatings with a modulation period of 12 nm, obtained by a conventional cathodic arc technique. The coatings were deposited at a temperature of 320 °C using nitrogen and methane reactive gases (N₂/CH₄) mixture in three different proportions. Surface morphology, composition, hardness, adhesion, friction and wear behavior were studied using atomic force microscopy, scanning electron microscopy with energy dispersive spectroscopy, X-ray diffraction, Raman spectroscopy, nanoindentation, and scratch and wear tests. The analysis of the coating composition revealed a strict dependence of the carbon content on the CH₄ flow rate. It was found that the coatings with a carbon content of 14.6 at.% and 15.9 at.% consist of crystalline TiZr (C,N) with the presence of amorphous carbon. All the studied TiCN/ZrCN coatings showed improved tribo-mechanical properties compared to TiN/ZrN multilayers obtained under the same deposition conditions. The highest hardness of 40 GPa was obtained for the coating deposited at a N₂/CH₄ flow rate of 370/100 sccm. The lowest wear rate of 3.16 × 10⁻⁶ mm³/N·m under dry sliding conditions was observed in the multilayer coatings deposited at the N₂/CH₄ flow rates of 330/140 sccm. Full article

Background/Objectives: Staphylococcus epidermidis (RP62A) and Staphylococcus aureus (UAMS-1) are clinically relevant pathogens frequently implicated in implant-associated infections due to their ability to form biofilms. RP62A is typically linked to persistent, chronic, low-grade infections, whereas UAMS-1 is associated with acute, invasive disease. Both strains serve as representative models for chronic and acute periprosthetic joint infections (PJIs). The objective of this study was to examine and compare in vitro biofilm formation by RP62A and UAMS-1 on orthopaedic materials/disc surfaces of defined composition. Methods: In vitro biofilm formation assays were performed using orthopaedic disc surfaces composed of cobalt–chromium alloy (CoCr), titanium alloy (Ti), and polyethylene (PE) after 72 h of incubation. Biofilm biomass was quantified using crystal violet staining, with absorbance measured at OD₅₇₀. A polystyrene (PS) surface served as a control. Additionally, retrieved orthopaedic explant components were used as substrates for in vitro biofilm assays, in which RP62A was incubated for 72 h on the explanted surfaces. Supporting assays on glass slides were conducted to examine strain-specific biofilm-related architecture. Results: In vitro biofilm mass quantification assays showed strong biofilm formation by RP62A across all tested surfaces, with the highest absorbance on CoCr (OD₅₇₀ = 5.80 ± 0.19). Notably, biofilm formation on CoCr was 76% higher compared to PS (p < 0.0001). No significant differences were observed among all three surface discs (p > 0.1). Biofilm formation was highest on PE for UAMS-1 (OD₅₇₀ = 1.29 ± 0.09) and was significantly greater than on Ti (178%, p < 0.001) and CoCr (196%, p < 0.0001). In the in vitro assays performed on retrieved explant components, RP62A showed pronounced biofilm accumulation on polyethylene tibial inserts, particularly in regions of mechanical wear and friction. Supporting assays on glass slides were performed to examine strain-specific surface microstructural, revealing dense network-like structures for RP62A and thinner, discontinuous layers for UAMS-1. Conclusions: RP62A formed dense biofilms in vitro on multiple orthopaedic implant materials and retrieved explant components, consistent with its association with chronic periprosthetic joint infections. Increased biofilm accumulation was observed on mechanically worn polyethylene surfaces. In contrast, UAMS-1 showed lower biofilm formation on metallic disc surfaces, indicating strain- and material-dependent differences. These findings highlight the relevance of implant material selection and surface integrity for strategies targeting biofilm-associated implant infections. Full article