Search Results (4,065)

Nearshore environments, characterized by complex obstacle distributions and dynamic disturbances, pose significant challenges to global path planning for unmanned underwater vehicles (UUVs). To address these challenges, this paper proposes an improved artificial potential field-guided RRT* (IAPF-RRT*) method for efficient and robust path planning in coastal environments. The proposed approach integrates an improved artificial potential field into the sampling and node expansion processes to enhance goal-directed exploration and obstacle avoidance capability. In addition, a target-biased sampling strategy and an adaptive attraction mechanism are introduced to accelerate convergence, while a NURBS-based refinement scheme is employed to improve trajectory continuity and smoothness. Extensive simulations in representative scenarios demonstrate that the proposed method significantly improves planning efficiency, reducing planning time by up to 80% compared with conventional RRT-based methods, while substantially decreasing redundant node expansion and improving trajectory smoothness. A consistently high success rate is maintained across all scenarios. Field experiments in nearshore environments further validate the robustness and practical applicability of the proposed method. These results indicate that the proposed IAPF-RRT* method achieves a favorable balance between efficiency, robustness, and path quality, making it well-suited for real-world UUV operations in complex nearshore environments. Full article

Background: Chronic kidney disease (CKD) is characterised by a disrupted gut–kidney axis, wherein intestinal dysbiosis is associated with the accumulation of uraemic toxins and the potential depletion of beneficial short-chain fatty acids (SCFAs). Whilst acetate, propionate, and butyrate are known to modulate systemic inflammation and blood pressure, their precise circulating concentrations across different CKD stages and age groups remain poorly defined. This systematic review and meta-analysis aimed to quantify blood SCFA concentrations in CKD patients compared to healthy controls. Methods: We conducted a systematic search of Medline, EMBASE, and the Cochrane Library for clinical studies reporting blood SCFA concentrations in humans with CKD. Methodological quality was assessed using the NIH tool. Standardised mean differences (SMDs) were calculated for the quantitative meta-analysis, with subgroup analyses performed for age, CKD stage, and treatment modality (dialysis vs. transplantation). Results: Twenty-one studies encompassing 9661 participants were included. Quantitative synthesis revealed a significant and consistent systemic depletion of circulating acetate and propionate in adult CKD patients compared to healthy controls (p < 0.05). This depletion followed a stage-dependent trajectory, worsening alongside declining glomerular filtration rates. Notably, a “butyrate paradox” was identified in paediatric cohorts; whilst adults showed progressive butyrate depletion, children with CKD often maintained or exhibited elevated levels, particularly in the context of hypertension. Furthermore, whilst haemodialysis patients exhibited the most profound SCFA deficiencies, kidney transplantation appeared to partially restore these metabolites toward healthy baseline levels. Conclusions: CKD is associated with a profound systemic reduction in acetate and propionate, supporting the model of a compromised gut–kidney axis based on converging evidence. The divergent results for butyrate in paediatric versus adult populations suggest that SCFA metabolism is influenced by age-related factors or compensatory mechanisms. These findings highlight the potential for SCFA monitoring as a candidate or emerging markers for detecting early renal damage and stratifying risk. Full article

Background: Despite the availability of safe vaccines, Ethiopia’s human papillomavirus (HPV) vaccine uptake remains suboptimal, particularly among out-of-school girls (OOSGs). This study examines the effect of multi-channel demand generation messages in two districts to determine which interventions most effectively improve uptake. Methods: A convergent mixed-methods design was employed across four districts in the Somali and South Ethiopia regions, with Jigjiga and Derashe serving as intervention sites and Gode and Kolango Zuria as controls. For the quantitative component, 950 sample households were recruited using cluster sampling. The qualitative inquiry involved 27 in-depth interviews (IDIs) and 16 focus group discussions (FGDs) within the intervention sites. Results: A total of 950 caregivers and 1134 girls completed the survey. Awareness was significantly higher among caregivers (AOR: 4.42; 95% CI: (3.06, 6.39)) and girls (AOR: 7.63; 95% CI: (3.49, 16.67)) in intervention sites, as well as among in-school girls (AOR: 13.46; 95% CI: (4.09, 41.90)). The mean vaccination coverage reached 71%, with significantly higher rates in intervention sites (AOR: 4.07; 95% CI: (2.29, 7.23)) and among in-school girls (AOR: 47.16; 95% CI: (20.23, 109.9)). Interpersonal communication—via teachers, peers, community health workers and vehicle-mounted promotion—was more effective in influencing awareness, attitude and uptake. Barriers for OOSGs included limited access to vaccination sites, low campaign awareness, misconceptions and gender-related issues. Conclusions: Appropriate demand generation strategies effectively enhance HPV awareness and vaccine uptake, yet a significant equity gap remains, as only one-third of OOSGs received the vaccine compared with 85% of in-school girls. Targeted interventions are recommended for OOSGs focused on both access to service and context-specific demand creation to address this disparity. Full article

To improve the operational efficiency of the park source-load-storage system and reduce operation costs and the wind-solar curtailment rate, this paper establishes a Park Integrated Energy System (PIES) model with multiple energy storage and vehicle-to-grid (V2G) components and proposes an adaptive comprehensive fitness multi-objective particle swarm optimization algorithm. First, each component of the PIES is modeled. Second, electric vehicle (EV) scheduling boundaries, determined by wind and PV output, as well as a dynamic charging-discharging incentive mechanism, are designed to enhance renewable energy accommodation. Finally, an adaptive comprehensive fitness index is defined, and convergence and particle-update strategies are improved to achieve better scheduling performance. Simulation results verify that the proposed PIES model achieves optimal performance in terms of carbon-emission cost, total operation cost, and wind-solar curtailment rate. Meanwhile, the improved algorithm also outperforms traditional multi-objective methods in PIES scheduling. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ε_i/(1−γ), which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

Particle swarm optimization (PSO) is an optimizing method that is based on the theory of swarm intelligence. PSO is an effective algorithm that is used to search in a parallel manner compared to other methods. However, PSO has a tendency towards local optima when tackling complex multimodal optimization problems. It also has the disadvantages of slow convergence process and poor stability in the latter evolutionary period. In view of these demerits, a hybrid PSO method based on chaotic opposition-based initialization and an adaptive learning strategy is presented in this work (abbreviated as ACMPSO). First, the chaos initialization and opposition-based learning (OBL) are employed to produce high-quality initial particles in the feasible region, which is able to improve the quality of the initial solutions. Second, the logistic mapping embedded inertia weight is formulated to better trade off the global and local search process. Third, the global optimal particle is regulated by an exclusive velocity and position updating strategy whereas the rest particles are adjusted by the standard updating mechanism so as to prevent particles from premature convergence. Furthermore, an adaptive position update paradigm is developed to finely regulate the global exploration and local exploitation. Finally, conducted experiments on CEC’13 and CEC’22 reveal that the proposed ACMPSO outperforms several other advanced PSO variants regarding their convergence rate and accuracy. Alternatively, to further illustrate the effect of ACMPSO, we have applied it to two real-world problems, and simulation results ascertain its effectiveness and robustness. Full article

This paper presents on-policy and off-policy algorithms for the $H_{\infty }$ control of continuous-time mean-field stochastic Markov jump systems. Using online state and input data, these algorithms can learn control and disturbance strategies without the need for prior knowledge of system matrices. Under the standard assumptions that the system is mean-square stabilizable and detectable, we rigorously prove the monotonicity, boundedness, and convergence of the proposed iterative algorithms to obtain the unique stabilizing solution of cross-coupled generalized algebraic Riccati equations. Moreover, the off-policy algorithm features high data efficiency because the collected data can be utilized again after each iteration. Numerical simulations demonstrate the effectiveness of two algorithms and explicitly show that the off-policy algorithm achieves a faster convergence rate compared to its on-policy counterpart. Full article

Background/Objectives: Persistent and long-standing persistent atrial fibrillation (AF) presents a therapeutic clinical challenge balancing complex rhythm management with a heightened stroke risk. The left atrial appendage (LAA) is the primary source of thromboembolisms in these patients. This study evaluated the safety and efficacy of combining LAA exclusion with Convergent Hybrid Ablation for stroke prevention and rhythm control in a refractory patient cohort. Methods: A single-center observational cohort study was conducted including 28 patients with symptomatic persistent or long-standing persistent AF. The cohort was highly refractory, with 82.1% having failed at least one endocardial catheter ablation. The hybrid procedure consisted of sub-xiphoid epicardial ablation, thoracoscopic LAA exclusion (AtriClip), and endocardial catheter ablation. Safety and efficacy were assessed at 3 months and 12 months. Results: LAA exclusion was successfully performed in 96.4% of patients. The peri-operative safety profile was acceptable, with zero procedure-related strokes or deaths. At the 12-month follow-up, the rate of stroke or any other major adverse events was at 0.0%. Freedom from AF was 75.0%, shown by a 12-lead electrocardiography (ECG). Freedom from any atrial arrhythmia off anti-arrhythmic drugs (AADs) was achieved in 50.0% of patients. A total of 32.1% of the cohort required catheter ablation within 12 months to maintain sinus rhythm as part of the hybrid treatment. Conclusions: Concomitant LAA exclusion during Convergent Hybrid Ablation is a safe procedure with a high clinical success rate in maintaining sinus rhythm in a highly complex AF patient group. While no thromboembolic events were observed at 12 months, larger studies with longer follow-up are needed to confirm the potential for long-term stroke risk reduction. The findings suggest that for many patients, the hybrid procedure should be viewed as part of a multi-step strategy often requiring endocardial “touch-up” ablation. Full article

Let $G\subset \mathbb{C}$ be a bounded simply connected domain with rectifiable Jordan boundary. Denote by $\varphi$ the conformal map of the exterior of G onto the exterior of the unit disk. For $R>1$, let ${\mathsf{\Gamma }}_R$ be the level curve defined by $\left|\varphi \left(z\right)\right|=R$, and let $G_R$ denote its interior, so that $G\subset G_R$. Suppose that f is analytic in $G_R$. In this paper, we investigate the maximal convergence properties of the Fourier series of f with respect to the Bergman orthogonal polynomials of G. By employing the strong asymptotics of Bergman polynomials outside the domain G of orthogonality, determined by the boundary properties of G, we obtain estimates for the maximal convergence rate of the partial sums of the Fourier series of f in the uniform norm on $\overline{G}$. These estimates are expressed in terms of the best polynomial approximation of f in the domain $G_R$ where f is analytic. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

In single-carrier underwater acoustic (UWA) communication systems, sparse multipath channels and long delay spreads pose significant challenges to adaptive equalization, often leading to limited steady-state accuracy and degraded detection performance. To address this issue, this paper proposes a data reuse-based ${\ell }_1$-regularized proportionate recursive least-squares algorithm (DR-${\ell }_1$-PRLS) for sparse adaptive equalization. The proposed method incorporates a data reuse (DR) mechanism into the ${\ell }_1$-PRLS framework, enabling multiple equivalent uses of each received–reference sample pair without increasing pilot overhead. Meanwhile, by combining the proportionate update strategy with the ${\ell }_1$ sparsity constraint, the structural information of sparse channels can be more fully exploited, thereby improving parameter estimation accuracy. Numerical simulations are conducted to evaluate the proposed method in terms of convergence behavior, tracking capability, computational complexity, and bit error rate (BER), and comparisons are made with LMS, RLS, PRLS, ${\ell }_1$-PRLS, and DR-PRLS algorithms. Simulation results show that, under sparse underwater acoustic channel conditions, DR-${\ell }_1$-PRLS achieves lower steady-state error and better BER performance while maintaining good tracking capability, thereby demonstrating its effectiveness and robustness for sparse adaptive equalization in single-carrier underwater acoustic communications. Full article

To address the current surge and speed fluctuation that occur when high-speed surface-mounted permanent magnet synchronous motors (HSPMSMs) switch from I-f open-loop control to sensorless closed-loop control, an adaptive switching method based on the cosine of the angle error is proposed. In this method, the angle error between the I-f open-loop reference angle and the angle estimated by the sensorless observer serves as the regulating variable, and its cosine is introduced to construct an adaptive attenuation factor, so that the rate of current reduction can vary continuously with the angle error. Specifically, a relatively large rate of current reduction is generated in the early stage of the switching process, when the angle error is large, to shorten the switching time. As the angle error decreases, the rate of current reduction is gradually lowered, allowing the current regulation process to better match the convergence process of the angle error and thereby improving switching stability. The proposed switching method is validated on a high-speed air compressor experimental platform. The experimental results show that the proposed method can shorten the switching time, reduce the current surge and speed fluctuation at switching, and exhibit good robustness under varying operating conditions. Full article

Hybrid workplace environments are increasingly adopted in public-sector organizations; however, empirical evidence on how these spaces function in practice remains limited. This study evaluates space utilization and workplace experience in the Los Angeles Department of Water and Power (LADWP) WorkHub, a hybrid-ready and technology-enabled workplace, using a convergent mixed-methods post-occupancy evaluation grounded in affordance theory. Phase I consisted of a participatory Think Tank session with 24 employees to identify perceived strengths, barriers, and improvement priorities related to indoor environmental quality (IEQ) and spatial design. Phase II employed a Space-Centered Behavioral and Environmental Mapping (SC-BEM) protocol over four weeks, generating 272 valid zone-level observations capturing occupancy, activity type, seat utilization, functional alignment, and perceived environmental conditions. The results indicate that workplace use was concentrated in reservable, enclosed, and technology-supported spaces, whereas many open seating areas remained underutilized. Observed behaviors were primarily associated with collaborative and communal activities, with comparatively fewer focused individual activities, suggesting that the WorkHub functioned predominantly as a hub for interaction. Although acoustics and thermal comfort emerged as consistent experiential constraints, observer-rated IEQ did not significantly predict occupancy. Interpreted through an affordance-based lens, these findings suggest that space use was shaped more strongly by the clarity and usability of spatial affordances than by environmental quality alone. Full article

We present an improved version of the Hănțilă Method (HM) for circuits with multiple nonlinear resistive elements (NRCEs). The method replaces NRCEs with generators consisting of linear resistors and nonlinear controlled sources, whose values are updated iteratively. At each iteration, the resulting linear circuit is solved in the frequency domain for each harmonic. We evaluate the computational effort and compare computation times and results with those obtained using conventional methods applied to a circuit comprising parallel-connected single-phase half-wave rectifiers. Full article

The formation and spatial distribution of sedimentary systems in rift-lake basins are jointly controlled by multiple factors, including sediment supply rates from source areas, clastic sediment transport pathways, and basin geometry and intrabasinal structural configuration (e.g., accommodation zones and faults), which strongly influence the architecture of depositional systems and basin filling processes. The Wenliu Formation (Wenliu Member, Late Paleogene) of the Wenchang Group in the Enping 20/21 Depression of the Yangjiang East Sag, Pearl River Mouth Basin, developed a multi-source and multi-channel sand-transport system; however, the matching relationships and coupling mechanisms among different source areas, transport pathways, and depositional systems remain poorly understood. Based on three-dimensional seismic data, drilling, and well-log information, combined with heavy mineral assemblages and detrital zircon U–Pb age spectra, this study comprehensively investigates the source areas, paleochannel clastic sediment transport pathways, and depositional systems of the Wenliu Member, systematically establishing the source-to-sink (S2S) framework. The results indicate that sediments of the Wenliu Member were supplied from four main source areas, including the northwestern Yangchun Uplift, northeastern Enyang low uplift, and southwestern Yangjiang low uplift, with nine major paleochannel clastic sediment transport pathways identified. The different source zones show distinct variations in area, slope characteristics, and sediment supply modes, corresponding to differentiated paleochannel types and paleodrainage configurations. The study area overall exhibits a typical multi-channel convergence depositional pattern, dominated by braid-delta and fan-delta systems. The Enyang low-slope source zone generated the largest braid-delta deposits, whereas fault-transformed source zones produced fan-delta deposits adjacent to active faults and along basin-margin fault systems. Quantitative analysis further indicates that depositional-system scale is significantly correlated with source-area size, paleodrainage development, and paleochannel geometric parameters. Large depositional bodies are more likely to form when the source area exceeds ~60 km², the paleochannel width exceeds ~1.4 km, and the cross-sectional area exceeds ~10 km². Integrating the spatial relationships among source areas, transport pathways, and depositional systems, four source-to-sink subsystems are identified, which can be further classified into two typical depositional patterns: a long-source gentle-slope braid-delta pattern and a proximal-source rapid-accumulation fan-delta pattern. This study elucidates the coupling relationships among source areas, clastic sediment transport pathways, and depositional sinks in a multi-source rift-lake basin, providing a geological basis for predicting sedimentary systems and guiding hydrocarbon exploration in the study area. Full article