Patients undergoing hemodialysis are at increased risk of physical, psychological, and lifestyle-related complications that negatively impact clinical outcomes. Although comprehensive medical care support (CMCS), particularly from nurse-inclusive multidisciplinary teams, may help address these challenges, its effectiveness remains uncertain. This study evaluated the effectiveness of CMCS provided by nurse-inclusive healthcare professionals in improving outcomes among adult patients undergoing hemodialysis. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) assessing comprehensive care interventions delivered by nurse-inclusive healthcare professionals. Eligible studies included adult patients undergoing hemodialysis and reported outcomes related to physical function, dialysis-related complications, health-related quality of life (HR-QOL), or self-management ability. Literature searches were conducted in MEDLINE (via PubMed) and Ichushi-Web (January 1970–April 2024). The risk of bias was assessed using the Cochrane Risk of Bias Tool (RoB 2) tool, and the certainty of evidence (COE) was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. The qualitative synthesis and meta-analysis comprised 25 and 11 RCTs, respectively. A total of 2093 participants were enrolled across the 25 studies. Comprehensive care interventions significantly improved normal gait speed (mean difference [MD] = 10.51 cm/s; 95
This research provides a comprehensive examination of hybrid modeling approaches that integrate physics-based and data-driven methods across three transportation sectors: automotive, aviation, and maritime. Motivated by the need for improved generalization, interpretability, and robustness, hybrid models combine the expressiveness of machine learning with the reliability of physical laws. Existing hybridization techniques are categorized according to the fusion point, including input features, loss functions, model architecture, and output correction, and their respective advantages and challenges are assessed. Domain-specific trends are identified: automotive and aviation benefit from high-fidelity physical models and abundant labeled datasets, while maritime applications often contend with sparse, noisy data and low-fidelity models, thus placing greater emphasis on hybrid methods to ensure safety and reliability. We further analyze strategies such as physics-informed neural networks, residual learning, transfer learning with synthetic data, and multimodel architectures, and evaluate their suitability under different data availability and physical modeling constraints. This review highlights the critical role of domain knowledge, the impact of physical model fidelity, and the importance of adaptive integration strategies in achieving robust and trustworthy dynamic models in real-world transport systems.
Underactuated multirotor UAV (UAV) equipped with a manipulator (UWM) face significant challenges when transporting loads, as large swings in the manipulator can resemble a double pendulum and risk dislodging the payload. With complex control requirements, nonlinear motion coupling, and variable load conditions, conventional linear controllers are often inadequate. To address these issues, we present anti-swing control method based on system dynamics characteristics and online parameter compensation (ASDOC). This novel anti-wing controller stabilizes swinging by leveraging the system’s nonlinear dynamics and performing online parameter estimation, thus avoiding the limitations of linearization. This design adapts robustly to varying load characteristics, ensuring stability and reliability across diverse scenarios. Lyapunov-based analysis confirms the control objectives, while simulations substantiate ASDOC’s enhanced anti-swing performance.
This study aimed to evaluate the effectiveness of comprehensive medical care support (CMCS) by healthcare professionals, including nurses, for patients with chronic kidney disease (CKD) in the preservation phase. All relevant studies were identified through comprehensive literature searches conducted in PubMed (MEDLINE) and Ichushi-Web. Randomized controlled trials involving patients aged ≥ 18 years with dialysis-independent CKD were included. The trials had to include nurses in the intervention details and provide CMCS by two or more healthcare professionals. A total of 20 studies were included in the systematic review, of which 14 were eligible for meta-analysis. A significant positive effect of CMCS was observed in terms of physical function, quality of life (QOL), and self-management ability. However, in the pooled analysis, no significant differences regarding renal function and blood pressure were found between the groups. The findings suggest that multidisciplinary team-based CMCS, including nursing involvement, may be associated with improvements in physical function, quality of life, and self-management in patients with CKD; however, substantial heterogeneity across studies warrants cautious interpretation and generalization. PROSPERO registration no. CRD42024529378.
This article presents an adaptive modified equivalent-input-disturbance (AMEID) approach for feedback active noise control (ANC) to enhance low-frequency noise rejection. The key idea is to represent low-frequency noise as an equivalent input disturbance (EID) and suppress it through feedback control. The AMEID framework consists of two components. The first is a model-based component that embeds an internal model of the expected EID dynamics to ensure convergence. The second is a data-driven component that improves disturbance-rejection performance. Controller synthesis is performed by solving two discrete-time algebraic Riccati equations subject to a robust stability condition. Compared with conventional model-based EID and data-driven ANC schemes, the AMEID approach achieves faster transient response and greater robustness. Experimental results verify the effectiveness and superiority of the approach.
