Search Results (498)

Background/Objectives: Lipedema is a chronic, progressive disorder of the adipo-fascial tissue characterized by abnormal subcutaneous fat accumulation, inflammation, fibrosis, pain, and edema. Despite its considerable impact on patients’ quality of life, it remains underdiagnosed. Recent studies have suggested a potential overlap between lipedema and hypermobility spectrum disorders (HSDs), both involving connective tissue dysfunction. This work explores the shared pathophysiological features of lipedema and HSD, highlighting clinical correlations, comorbidities, and the need for integrated diagnostic and therapeutic approaches. Methods: A cross-sectional observational study was conducted through an online survey targeting individuals with lipedema and a control group with lymphedema. The questionnaire assessed symptoms typically associated with HSD, including musculoskeletal, gastrointestinal, urogynecological, vascular, and neuropsychological manifestations. Descriptive statistics were used to evaluate clinical patterns in both groups. Results: Among the lipedema patients, 44% reported joint hypermobility and 60% recalled being hypermobile during childhood. High rates of pediatric overweight (50%), low muscle tone (55%), and exercise-induced fatigue (70%) were observed. Adult symptoms included joint pain (notably in the ankles, knees, cervical spine, sacrum, and feet), digestive issues (50%), and thyroid disorders (24.4%). Compared with the control group, patients with lipedema showed significantly more connective tissue-related motor deficits and systemic symptoms. Conclusions: Connective tissue laxity may play a critical role in the pathogenesis of lipedema, contributing to multisystemic manifestations through vascular, lymphatic, gastrointestinal, and musculoskeletal involvement. The high prevalence of HSD-like features calls for a paradigm shift in the understanding of lipedema as a systemic disorder. Early identification of connective tissue alterations, especially in children with familial predisposition, could enable timely interventions, potentially mitigating disease progression. A multidisciplinary, evidence-based approach is essential for accurate diagnosis and effective management. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

Background: Although most amputations caused by diabetes and peripheral artery disease (PAD) are preventable, current limb preservation efforts in the United States remain poorly understood. This study aims to identify key barriers and facilitators to limb preservation from the primary care provider (PCP) perspective. We plan to use the insights from this work to promote targeted intervention strategies. Methods: Using a mixed-methods design, an online 5–10 min survey was distributed to Oklahoma primary care providers who could elect to participate further in a semi-structured, audio-recorded interview. Descriptive analysis was used to summarize survey results. Interviews were transcribed and qualitatively analyzed using grounded theory. Donabedian’s structure, process, and outcome framework was used to categorize how each identified barrier and facilitator increases or reduces the risk of limb loss for at-risk patients at the practice level. Finally, we compared and contrasted survey and interview findings. Results: Thirty surveys were completed (approximately 14% response rate), and seven interviews were conducted with PCPs geographically dispersed across Oklahoma. Most clinicians reported in the survey that they see at-risk limbs at least once every 1–2 months (n = 29, 96.7%). Half of clinicians were satisfied or very satisfied with access to vascular surgery (n = 15, 50.0%), interventional specialists (n = 13, 43.3%), and endocrinologists (n = 12, 40.0%). Finally, survey respondents reported that social needs most often affecting their patients with a limb at risk of amputation include income, health education, transportation, and health insurance. Interviews confirmed PCPs frequently see at-risk limbs. We identified thematic barriers to limb preservation that included limited access to specialty care, limited PCP and patient amputation prevention education, and patient social struggles surrounding transportation, finances, and insurance. Patient advocates (community, clinical, or personal), affordable medications, and more time with patients were reported as facilitators in amputation prevention. Conclusions: Oklahoma PCPs frequently see at-risk feet, realize poor access to care, and desire structural change to support excellent preventive care in diabetes and PAD. Limb preservation in Oklahoma is contingent upon shifting from disempowerment to engagement that requires systemic reform, clinical innovation, and community engagement. We identified several intervention strategies, including increasing education for PCPs to empower them to initiate early prevention, improving early identification and preventive therapy for patients at risk for limb loss, and cultivating specialty care access via networking and policy change. Full article

Background: Hypertension is a major global public health problem whose prevalence is increasing across the world. In consideration of this, there is insufficient understanding of the barriers that hinder the taking of anti-hypertensive medication among patients. In this regard, the aim of this study was to analyze the possible barriers undermining adherence to anti-hypertensive medication among patients in private healthcare in Edenvale, South Africa. Methodology: This study used an exploratory cross-sectional research design within which quantitative data were collected through an online survey on a sample of randomly selected hypertensive patients attending private healthcare facilities in Edenvale. Participation in this study was voluntary, and informed consent was sought from each participant. Anonymity was assured during data collection through the de-identification of respondents and any data about them. The collected data were subjected to descriptive statistical analysis. Results: One hundred and twenty-two patients participated in this study. From this, 34.4% of participants revealed that lack of awareness was a barrier to a very small extent. Forgetfulness was a possible barrier to adherence to a large extent amongst 16.4% of participants, and 26.2% of the respondents had, to a large extent, doubts about their hypertension diagnosis. However, 42.6% revealed that side effects and difficulty taking medication whilst away from home (47.5%) were a barrier to a small extent. The fear of side effects (19.7%), interference of alcohol or drug use (29.5%) were challenges to a moderate extent. Conclusions: The findings of this study support that hypertensive patients in private healthcare encounter financial constraints, occasionally forget to take their medication, doubt their hypertension diagnosis, and lack awareness about the benefits of taking anti-hypertensive medication. This may be compounded by patients finding the anti-hypertensive medication regimen too complicated, feeling overburdened by having to take too many pills every day, the complexity of the medication regimens, perceived incorrect diagnosis, and lack of social support from family and friends. Understanding the extent of the barriers encountered by patients in taking anti-hypertension medication may help address adherence challenges, which may help improve health outcomes and lessen the burden on health systems in pursuing Sustainable Development Goal 3 and universal health coverage. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail to maintain optimal power output. Dynamic load changes and complex operating conditions exacerbate this issue. As a result, system response is slowed, and tracking accuracy is reduced. To address these problems, an online identification method based on recursive least squares (RLS) is employed. A cubic power–current model is identified in real time. Polynomial fitting and the golden section search are then applied to estimate the current at the maximum power point. Following model-based estimation, adaptive particle swarm optimization (APSO) is utilized to tune the PID controller parameters. Precise regulation is thus achieved. The use of RLS enables real-time model identification. The golden section search improves the efficiency of current estimation. APSO enhances global optimization, while PID provides fast dynamic response. By integrating these methods, both tracking accuracy and system responsiveness are significantly improved in fuel cell MPPT applications. Simulation results demonstrate that the proposed strategy enhances maximum power output by up to 12.40% compared to conventional P&O, fuzzy logic control, GWO-PID, and PSO-PID methods, as well as maintaining a consistent improvement of 1.50% to 1.90% even when compared to other optimization algorithms. Full article

Identifying the botanical origin of honey is essential for ensuring its quality, preventing adulteration, and protecting consumers. Traditional techniques, such as melissopalynology, physicochemical analysis, and PCR, are often labor-intensive, time-consuming, or limited to the detection of only known species, while advanced DNA sequencing remains prohibitively costly. In this study, we aim to develop a deep learning-based approach for identifying pollen grains extracted from honey and captured through microscopic imaging. To achieve this, we first constructed a dataset named VNUA-Pollen52, which consists of microscopic images of pollen grains collected from flowers of plant species cultivated in the surveyed area in Hanoi, Vietnam. Second, we evaluated the classification performance of advanced deep learning models, including MobileNet, YOLOv11, and Vision Transformer, on pollen grain images. To improve performances of these model, we proposed data augmentation and hybrid fusion strategies to improve the identification accuracy of pollen grains extracted from honey. Third, we developed an online platform to support experts in identifying these pollen grains and to gather expert consensus, ensuring accurate determination of the plant species and providing a basis for evaluating the proposed identification strategy. Experimental results on 93 images of pollen grains extracted from honey samples demonstrated the effectiveness of the proposed hybrid fusion strategy, achieving 70.21% accuracy at rank 1 and 92.47% at rank 5. This study demonstrates the capability of recent advances in computer vision to identify pollen grains using their microscopic images, thereby opening up opportunities for the development of automated systems that support plant traceability and quality control of honey. Full article

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest. Full article

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems. Full article

Video surveillance systems are a key tool for the identification of anomalous events, but they still rely heavily on human analysis, which limits their efficiency. Current video anomaly detection models aim to automatically detect such events. However, most of them provide only a binary classification (normal or anomalous) and do not identify the specific type of anomaly. Although recent proposals address anomaly classification, they typically require full video analysis, making them unsuitable for online applications. In this work, we propose a modular framework for the joint detection and classification of anomalies, designed to operate on individual clips within continuous video streams. The architecture integrates interchangeable modules (feature extractor, detector, and classifier) and is adaptable to both offline and online scenarios. Specifically, we introduce a multi-category classifier that processes only anomalous clips, enabling efficient clip-level classification. Experiments conducted on the UCF-Crime dataset validate the effectiveness of the framework, achieving 74.77% clip-level accuracy and 58.96% video-level accuracy, surpassing prior approaches and confirming its applicability in real-world surveillance environments. Full article

This work presents the development of soft sensor models for monitoring the operation of online process analyzers used to measure the sulfur content in the product of the refinery hydrodesulfurization process. Since sulfur content often fluctuates over time, soft sensor models must account for these frequency fluctuations. We have therefore developed dynamic data-driven models based on linear and nonlinear system identification techniques (finite impulse response—FIR, autoregressive with exogenous inputs—ARX, output error—OE, nonlinear ARX—NARX, Hammerstein–Wiener—HW) and machine learning techniques, including models based on long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as artificial neural networks (ANNs). The core steps in model development included the selection and preprocessing of continuously measured plant process data, collected from a full-scale industrial hydrodesulfurization unit under normal operating conditions. The developed soft sensor models are intended to support or replace process analyzers during maintenance periods or equipment failures. Moreover, these models enable the application of inferential control strategies, where unmeasured process variables—such as sulfur content—can be estimated in real time and used as feedback for advanced process control. Full article

The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers. Full article

In practical applications, precise tuning of current controllers is essential for achieving desirable dynamic performance and stability margins. Traditional tuning techniques rely heavily on accurate plant parameter identification. However, this process is often challenged by inherent nonlinearities and unmodeled dynamics in motor systems. To address this issue, this paper proposes a current loop parameter tuning algorithm based on open-loop frequency sweeping. As the swept Bode diagram reveals nonlinear factors typically neglected during modeling, it provides a basis for control parameter correction. A pulse-sine voltage injection method is first introduced to identify motor parameters, serving as initial values for the controller. By analyzing the magnitude and phase characteristics of the open-loop transfer function, the delay time constant in the high-frequency range can be accurately identified, and mismatched parameters in the low-to-mid frequency range can be corrected. This method does not rely on complex model structures or extensive online adaptation mechanisms. Experimental results on a mechanical test platform demonstrate that the proposed tuning strategy significantly enhances the current loop’s closed-loop bandwidth and dynamic performance. Full article

Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. In a Cartesian coordinate system, we first use the universal approximation ability of the RBFNN to accurately identify and actively compensate for complex unknown disturbances in robot dynamics online. Then, an improved sliding mode impedance controller, which uses robust sliding mode control to effectively suppress the influence of RBFNN identification error and residual disturbance on trajectory tracking and ensure the accuracy of impedance control, is implemented. This approach improves the control performance and overcomes the inherent chattering phenomenon of the traditional sliding mode. Full article

A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article