Search Results (33,481)

Hemodynamic management is extremely important in cardiac patients undergoing surgery. Traditionally, the approach towards hemodynamic stabilization included the control of both mean arterial pressure (MAP) and cardiac output (CO) using Sodium Nitroprusside and Dopamine. More efficient and safer drugs have been introduced, such as Norepinephrine. The focus of this manuscript is to provide some preliminary results regarding the closed loop control of MAP using Norepinephrine. However, to design a dedicated control system, a mathematical model describing the effect of Norepinephrine on mean arterial pressure is required. Only a handful of papers describe a pharmacokinetic–pharmacodynamic (PK-PD) model. In this paper, a simplified model suitable for designing a controller is determined based on PK-PD insights and existing clinical data. Existing closed loop controllers are based on the simple proportional integral derivative (PID) controller, with limited robustness to patient variability. In this paper, two advanced control strategies are proposed to replace PID. The closed loop simulation results include reference tracking and disturbance rejection and show the efficiency and robustness of the proposed control algorithms. The preliminary results set the background for further research in this area. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

This paper presents the design and implementation of a modular cloud-based architecture that enables generative music capabilities in Digital Audio Workstations through a MIDI microservices backend and a user-friendly VST plugin frontend. The system comprises a generative harmony engine deployed as a standalone service, a microservice layer that orchestrates communication and exposes an API, and a VST plugin that interacts with the backend to retrieve harmonic sequences and MIDI data. Among the microservices is a dedicated component that converts textual chord sequences into MIDI files. The VST plugin allows the user to drag and drop the generated chord progressions directly into a DAW’s MIDI track timeline. This architecture prioritizes modularity, cloud scalability, and seamless integration into existing music production workflows, while abstracting away technical complexity from end users. The proposed system demonstrates how microservice-based design and cross-platform plugin development can be effectively combined to support generative music workflows, offering both researchers and practitioners a replicable and extensible framework. Full article

Background: Accurate assessment of volume status remains a central challenge in hemodialysis (HD). Although bioimpedance spectroscopy (BIS) can quantify fluid compartments, it is time-consuming and requires a lot of personnel. Modern HD machines provide continuous relative blood volume (RBV) monitoring. We examined whether intradialytic RBV dynamics reflect pre-dialysis extracellular fluid (ECW) status to inform personalized fluid management. Methods: In an ancillary, monocentric, prospective study of the SkInDialysis trial (DRKS00036332), 11 maintenance-HD patients underwent three standardized dialysis sessions with simultaneous measurement of RBV and BIS. BIS was performed at five time points per session (pre-HD; 20, 80, and 160 min after the start of HD; and post-HD). Ultrafiltration (UF), RBV, total body water (TBW), ECW, and intracellular water (ICW) were recorded. Results: Mean total UF was 2809 ± 894 mL/session. RBV declined to 94.7 ± 3.1% at 20 min and to 87.6 ± 5.5% by the end of the session. TBW decreased by 2.9 ± 2.7%, driven by ECW reduction (−3.15 ± 2.9%) over ICW (−1.1 ± 1.65%). Cumulative UF correlated with declines in TBW (R² = 0.18; p = 0.02) and ECW (R² = 0.23; p = 0.01) and more modestly with ICW (R² = 0.16; p = 0.04). In contrast, ΔRBV (pre- vs. post-HD) did not correlate with UF, weight loss, or compartmental water changes. Early steady-state RBV at 80 min correlated with pre-HD ECW (R² = 0.19; p = 0.02) and more strongly with the pre-HD ECW/ICW ratio (R² = 0.34; p = 0.001). Conclusions: In this small, repeated-measures cohort, absolute early steady state RBV levels were associated with pre-dialysis ECW and the ECW/ICW ratio, whereas RBV change (ΔRBV) did not track absolute fluid removal. Our data support a time-anchored RBV level as a pragmatic, device-embedded indicator of the pre-dialysis extracellular reservoir. Full article

Compared to the L-filter, the LCL-filter provides superior high-frequency harmonic attenuation for a given inductance. However, it also introduces resonance issues that can compromise system stability. Consequently, the bandwidth of the inner current loop must be maintained well below the resonant frequency of the filter. This paper proposes a robust controller design strategy for LCL-filtered converters to extend the harmonic control range under wide variations in grid impedance. An analysis of the resonant controller phase-frequency characteristics reveals its capability to provide phase compensation up to 2π. Building on this finding, the damping ratio and phase leading angle are systematically optimized through a joint analysis of the phase characteristics introduced by the resonant controller and active damping, thereby enhancing system robustness. With these optimized parameters, the center frequency of the resonant controller can be tuned above the LCL-filter resonance frequency without inducing instability. In contrast to conventional methods, the proposed approach allows the LCL-filter to be designed with a lower resonance frequency. This enables improved attenuation of switching-frequency harmonics without compromising the tracking performance for higher-order harmonics. Such a capability is particularly beneficial in high-power and weak-grid scenarios, where the filter resonance frequency may fall to just a few hundred hertz. Experimental results validate the effectiveness of the proposed design strategy. Full article

This paper presents an optimal control for autonomous vehicles navigating in intersection scenarios. The proposed controller is based on solving a Quadratic Programming optimization technique to provide a feasible control signal respecting actuator constraints. The proposed controller was implemented in a scale-sized vehicle and is executed using only on-board perception and computing systems to retrieve the state dynamics, i.e., an inertial measurement unit and a monocular camera, to compute the estimated states through intelligent computer vision algorithms. The stability of the error signals of the closed-loop system was proved both mathematically and experimentally, using standard performance indices for ten trials. The proposed technique was compared against LQR and MPC strategies, showing 67% greater accuracy than the LQR approach and 53.9% greater accuracy than the MPC technique, while turning during the intersection. Moreover, the proposed QP controller showed significantly greater efficiency by reducing the control effort by 63.3% compared to the LQR, and by a substantial 78.4% compared to the MPC. These successful results proved that the proposed controller is an effective alternative for autonomously navigating within intersection scenarios. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

Accurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both accurate and inaccurate recognition of five basic emotions (anger, disgust, fear, happiness, and sadness) in healthy young adults. Fifty participants (twenty-four women) completed a forced-choice categorization task with 10 stimuli (female/male poser × emotion). A remote eye tracker (60 Hz) recorded fixations mapped to eyes, nose, and mouth areas of interest (AOIs). The analyses combined accuracy and decision-time statistics with heatmap comparisons of misclassified versus accurate trials within the same image. Overall accuracy was 87.8% (439/500). Misclassification patterns depended on the target emotion, but not on participant gender. Fear male was most often misclassified (typically as disgust), and sadness female was frequently labeled as fear or disgust; disgust was the most incorrectly attributed response. For accurate trials, decision time showed main effects of emotion (p < 0.001) and participant gender (p = 0.033): happiness was categorized fastest and anger slowest, and women responded faster overall, with particularly fast response times for sadness. The AOI results revealed strong main effects and an AOI × emotion interaction (p < 0.001): eyes received the most fixations, but fear drew relatively more mouth sampling and sadness more nose sampling. Crucially, heatmaps showed an upper-face bias (eye AOI) in inaccurate trials, whereas accurate trials retained eye sampling and added nose and mouth AOI coverage, which aligned with diagnostic cues. These findings indicate that the scanpath strategy, in addition to information availability, underpins success and failure in basic-emotion recognition, with implications for theory, targeted training, and affective technologies. Full article

Synthetic inertia (SI) enables wind turbine generators (WTGs) to support frequency stability by releasing stored kinetic energy during disturbances. Existing grid-code requirements, such as those of Hydro-Québec TransÉnergie (HQT) and ENTSO-E/Nord Pool, improve the first frequency nadir but often aggravate a second frequency dip (SFD) or risk rotor over-deceleration (OD) when the boost magnitude is large. This paper proposes an enhanced SI requirement that retains the stepwise boost-and-hold structure but replaces the time-based ramp-down with a rotor-speed-dependent recovery, followed by a smooth transition back to maximum power point tracking (MPPT). The proposed scheme was validated using an electromagnetic transient model of the Unison U151 wind turbine (4.569 MW, inertia constant 9.68 s), designed for Korea’s low-wind conditions. Five case studies at wind speeds of 5 and 7 m/s with varying boost levels confirmed that all methods yield identical first nadirs for a given boost, but only the proposed approach consistently maintained a higher second nadir, stabilized rotor dynamics, and prevented repeated dips. These results demonstrate that rotor-speed-dependent SI requirements, when combined with high-inertia turbines, can enhance frequency stability while protecting turbine operation, offering practical guidance for future grid-code revisions. Full article

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems. Full article

To achieve accurate monitoring of dust intensity, this study developed a coupled model based on a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) to monitor dust intensity in a 24 h dynamic pattern. During this process, progressive dust temporal (PDT) features reflecting the temporal dynamics of dust events, including clear-sky state values, adjacent observation state values, and current observation state values for spectral indices and brightness temperatures, were first designed. Then, a PCBNet model combining CNN and Bi-LSTM was established and compared with PCLNet (CNN and LSTM), random forest (RF), and support vector machine (SVM) using only single-time observations, as well as PDT-RF and PDT-SVM, which used PDT features as inputs. Finally, a dust intensity product was generated by the optimal model, and its relationship with PM10 concentrations at air quality stations was examined. Furthermore, a dust storm event in April 2021 was analyzed to evaluate the ability of the products to capture event dynamics. The results indicate that PCBNet achieved the highest accuracy among all models on the validation dataset. Predicted dust intensity levels were well correlated with PM10 concentrations, and the monitoring product effectively tracked the spatiotemporal evolution of dust event. Full article

Background: Anthracycline-based chemotherapy is associated with a risk of cancer therapy-related cardiac dysfunction (CTRCD), particularly in patients with moderate to high cardiovascular risk. Left ventricular global longitudinal strain (GLS) is a sensitive marker for early myocardial dysfunction, but the prognostic value of baseline GLS in this population remains unclear. Objective: We aimed to evaluate whether baseline GLS can predict CTRCD in moderate- to high-risk cancer patients undergoing anthracycline chemotherapy. Methods: In this prospective, single-center observational study, 80 anthracycline-naive cancer patients classified as moderate or high risk were enrolled. Baseline GLS was assessed via speckle-tracking echocardiography, with a threshold of ≥−18% considered decreased. Patients were followed for 12 months, and the primary endpoint was the development of CTRCD per ESC 2022 Cardio-oncology guidelines. Results: Of the 77 patients completing follow-up, 27.3% had decreased baseline GLS. CTRCD occurred in 62.4% of patients, with higher incidence among those with decreased GLS (76.7%) compared to those with normal GLS. In multivariable analysis, GLS ≥−18% was the only significant independent predictor of CTRCD (RR 12.0, 95% CI 2.0–71.9; p = 0.0065). All-cause mortality was also significantly higher in patients with decreased baseline GLS (19.1% vs. 1.8%, p = 0.018). Conclusions: Decreased baseline global longitudinal strain is an independent predictor of cancer therapy-related cardiac dysfunction and all-cause mortality in moderate- to high-risk patients receiving anthracycline therapy. These findings support the inclusion of baseline GLS in pre-treatment cardiovascular risk assessment, particularly in patients with an LVEF above 54%, to more effectively identify those who may benefit from early cardioprotective interventions. Full article

Active helidecks systems (AHS) provide an effective solution scheme for the safe landing of helicopters on ships. This article proposes a novel adaptive antidisturbance prescribed performance control law of AHS subject to input saturation, ship motion-induced external disturbances. Specifically, we develop novel saturation-triggered boundaries to guarantee prescribed tracking error constraints under input saturation. This effectively addresses the control singularity issue inherent in traditional prescribed performance control, which occurs when input saturation causes the control error to exceed prescribed constraint boundaries. Subsequently, we design a continuous auxiliary dynamic system to further mitigate the effects of input saturation. Furthermore, leveraging the internal model principle and the periodic nature of ship motion, external disturbances are treated as the outputs of a linear exosystem with known structure but unknown parameters. These unknown parameters are then estimated using adaptive techniques, enabling asymptotic estimation of external disturbances. Building upon these developments and employing the backstepping design tool, we achieve adaptive antidisturbance stabilization of AHS. Both theoretical analysis and comparative simulations validate the proposed control law. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article