Search Results (1,970)

A non-contact laser polishing method for additively manufactured polymer components with complex three-dimensional geometries is presented, employing a 6-axis robotic system. Robot-guided sample orientation, a quasi-top-hat scanning strategy, and closed-loop temperature control are combined to address curved geometries. On Selective Laser Sintering (SLS)-manufactured Polyamide 12 (PA12) tensile samples with three build orientations and two thicknesses, laser polishing yields up to a 15% increase in tensile strength (Rm) and a 50% increase in elongation at break (A). For 45°-built 5 mm samples, Rm increases from 31.53 MPa to 36.33 MPa and A from 6.52% to 9.8%, approaching the tensile strength reported for optimally oriented SLS-printed PA12 Smooth samples of the same grade. For convex–concave PA12 demonstrators, areal roughness (Sa) on convex surfaces is reduced from 33.6 µm to 2.7 µm (approximately 92%) and the high-pass-filtered micro-roughness (Sa_HP) on concave surfaces by 98.2% to 0.15 µm. For Fused Deposition Modeling (FDM)-printed Polyaryletherketone (PAEK) samples, Sa is reduced from 28.35 µm to 4.1 µm and Sa_HP from 15.98 µm to 0.23 µm (98.6%), despite the high melting temperature and anisotropic raster topography. These results demonstrate that robotic laser polishing constitutes a viable post-processing approach for functionally demanding polymer applications. Full article

In the highly competitive e-commerce landscape, platforms must strategically balance complex operational and marketing parameters. These real-world systems inherently involve high-dimensional nonlinear interactions and strongly coupled variables, leading to complex consumer response behaviors and highly non-convex optimization landscapes. Traditional optimization approaches usually suffer from high computational costs in business environments, while conventional surrogate models are prone to premature convergence during hyperparameter estimation. To address these management and operational challenges, this study proposes a Chaos-initialized Quantum-behaved Particle Swarm Optimization Kriging (CQPSO–Kriging) framework. Chaotic mapping is introduced to enhance population diversity, while quantum-behaved particle dynamics improve global exploration capability. Utilizing large-scale real-world transaction data from the Brazilian e-commerce industry, high-fidelity surrogate response surfaces are constructed for three core business indicators: profitability, customer loyalty, and value density. Experimental results show that the proposed CQPSO–Kriging model significantly outperforms conventional approaches, such as support vector regression and radial basis function networks, achieving an exceptional coefficient of determination of R² = 0.9586 in profit prediction. Furthermore, Sobol variance-based global sensitivity analysis is employed to extract critical managerial insights, revealing that financial variables act as interaction-driven utility multipliers in consumer decision-making. Multi-objective Pareto analysis further demonstrates that profit maximization naturally converges toward a balanced operational configuration, providing a robust quantitative tool for e-commerce precision marketing. Full article

This study proposes a model predictive control (MPC)-based Assist-as-Needed (AAN) strategy for upper-limb rehabilitation robots, with particular emphasis on mitigating motor slacking. In conventional error-based AAN approaches, robotic assistance is regulated through a single coefficient tied to the tracking error; thus, a reduction in voluntary effort is absorbed into the assistive channel and remains obscured by a small tracking error. The proposed method decouples this mechanism by introducing a two-channel admittance structure, in which the robotic-assistance gain $A_k$ and the user-participation-reflection gain $B_k$ are jointly optimized within a single convex MPC formulation. The cost function addresses trajectory tracking, participation-aware force alignment, assistance suppression, and passivity, enforced through energy-tank constraints. The controller was validated in two experiments on a mobile upper-limb rehabilitation robot. The first experiment confirmed differential adaptation of $A_k$ and $B_k$ across three instructed contribution levels, with the participation ratio increasing from 0.103 to 0.879 as the contribution shifted from insufficient to appropriate. The second experiment compared the controller with an error-based AAN baseline and a forgetting-factor AAN baseline under an induced motor-slacking condition, in which the task-direction contribution was reduced to 45%. Under an identical synthesized input, the proposed controller yielded a lower aggregate human-contribution ratio of 0.282, compared with 0.595 and 0.535 for the two baselines, respectively. This indicates that the externally imposed reduction in participation was represented more explicitly in the controller allocation, rather than being masked by error-driven assistive compensation. These results suggest that the proposed approach extends AAN control toward a participation-preserving, anti-slacking strategy for robot-assisted rehabilitation. Full article

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces. Full article

In this paper, we give some new Jensen, Jensen–Mercer, and Hermite–Hadamard inequalities for uniformly $\delta$-geometric convex functions. In addition, some limit bounds for Caputo–Fabrizio fractional integral operators are established as an application in the case of uniformly $\delta$-geometric convex functions. Some new examples and graphical representations are provided in order to illustrate the validity of our results. Full article

In this paper, we investigate the partial approximate controllability of a class of fractional diffusion control systems with three-parameter damping and nonlinear perturbations. First, based on the theory of $(\mu ,\nu ,\xi ,e,k)$-resolvent families developed in our previous work, we define the mild solution of the system. Then, by constructing a proper objective functional and using the strict convexity, we prove the existence and uniqueness of the minimal norm control. Furthermore, employing the Arzelà–Ascoli theorem and variational inequalities, we establish the precompactness of the solution family and derive the key controllability estimate. Finally, we provide an example to illustrate the effectiveness of our theoretical results. Full article

Background: Functional appliance therapy is widely employed for the management of skeletal Class II malocclusion in growing patients. However, treatment outcomes are influenced by multiple biological and behavioural variables, including genetic background, craniofacial growth pattern, neuromuscular adaptability, orofacial resting postures, and patient adherence. These factors often limit direct comparison of different appliance systems. Monozygotic twin studies provide a unique biological model by minimizing genetic and environmental variability, allowing more accurate evaluation of appliance-specific effects. Methods: This case report presents a comparative evaluation of a clear functional jaw corrector and a conventional twin block appliance in two 11-year-old female monozygotic twins at cervical vertebral maturation index stage 3. Both patients exhibited similar skeletal Class II patterns, vertical growth tendencies, proclined maxillary incisors, and convex soft tissue profiles. Twin A was treated with a removable clear functional jaw corrector fabricated using mandibular advancement blocks incorporated into a 1.5-mm Essix retainer sheet, while Twin B received a conventional twin block appliance. Treatment objectives, wear protocol, and duration were identical. Neither patient received orofacial myofunctional therapy. Results: Post-treatment clinical and cephalometric evaluation demonstrated improvement in sagittal jaw relationships, facial profile, and occlusal relationships in both patients. However, differences were observed in the magnitude of skeletal correction, dentoalveolar effects, vertical control, and the extent of molar and canine relationship correction. Conclusions: Both appliance designs were effective in improving sagittal relationships under similar biological conditions, with minor differences favoring the clear functional jaw corrector. However, the findings also highlight that orthodontic appliance therapy alone does not address underlying orofacial myofunctional factors, emphasizing the importance of incorporating functional assessment and adjunctive myofunctional therapy for optimal and stable outcomes. Full article

We introduce and study a new subclass of Janowski-type harmonic close-to-convex functions in the open unit disk defined via the Jackson q-derivative operator. The structure of the operator naturally reflects certain symmetric properties in the analytic representation of the considered harmonic mappings. By applying subordination techniques, we establish sufficient conditions for sense-preserving close-to-convexity and distortion estimates. The extreme points of the class are determined, and its topological properties are examined, showing that the class is convex and compact. We further obtain the radius of starlikeness and prove that the class is closed under convolution. Moreover, as $q\to 1^{-}$, the operator reduces to the classical derivative, and our results recover several known results in the existing literature, demonstrating that the present work extends and generalizes earlier findings. Full article

Subdiffusion on graphs is often modeled by time-fractional diffusion equations; yet, its structural and dynamical consequences remain unclear. We show that subdiffusive transport on graphs is a memory-driven process generated by a random time change that compresses operational time, produces long-tailed waiting times, and breaks Markovianity while preserving linearity and mass conservation. While the subordination representation and complete monotonicity properties of the Mittag-Leffler function are classical, we develop a graph-based synthesis in which Mittag-Leffler dynamics admit an exact convex, mass-preserving representation as a superposition of Laplacian semigroups evaluated at rescaled times. This perspective reveals fractional diffusion as ordinary diffusion acting across multiple intrinsic time scales and enables new structural and dynamical interpretations of graphs. This framework uncovers heterogeneous, vertex-dependent memory effects and induces transport biases absent in classical diffusion, including algebraic relaxation, degree-dependent waiting times, and early-time asymmetries between sources and neighbors. These features define a subdiffusive geometry on graphs, enabling the recovery of global shortest paths, in contrast to the graph exploration of diffusive geometry, while simultaneously favoring high-degree regions. Finally, we show that time-fractional diffusion can be interpreted as a singular limit of multi-rate diffusion, in an appropriate asymptotic sense. Full article

We study functions satisfying the composition law $F\left(xy\right)+F(x/y)=P\left(F\right(x),F(y\left)\right)$ with a symmetric polynomial combiner P. We prove that symmetry together with a quadratic degree bound on P forces a composition law of d’Alembert type. We establish a degree mismatch exclusion criterion showing that symmetric polynomial combiners with $degP(u,v)\ge 3$ do not admit nonconstant continuous solutions, provided the leading term does not cancel (Theorem 1). For continuous nonconstant functions $F:{\mathbb{R}}_{>0}\to \mathbb{R}$ with $F\left(1\right)=0$ satisfying the composition law with a symmetric polynomial P of degree at most two, the combiner is necessarily of the form $P(u,v)=2u+2v+cuv$, $c\in \mathbb{R}$ (Theorem 3). The equation reduces in logarithmic coordinates to the classical d’Alembert functional equation. For $c\ne 0$, one obtains hyperbolic or trigonometric branches, while $c=0$ yields the squared-logarithm family. Under the cost-function assumptions $F\ge 0$ and convexity, only the hyperbolic branch with $c>0$ remains. A unit log-curvature calibration selects the canonical value $c=2$, which yields the canonical reciprocal cost $F\left(x\right)=\frac{1}{2}(x+x^{-1})-1$. For $c\ne 0$, the result extends to ${\mathbb{R}}_{>0}^n$: every solution depends only on a single linear combination of coordinate logarithms; for $c=0$, the solution is a general quadratic form ${\sum }_{i,j}{a}_{ij}ln{x}_{i}ln{x}_j$. In either case, nontrivial coordinate-wise separable costs are excluded. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

This paper develops a unified finite-bit distributed optimization framework for UAV swarms operating over bandwidth-limited communication graphs. We consider strongly convex and smooth global objectives decomposed over local UAV cost functions and study three communication-efficient algorithmic regimes. First, we design a quantized distributed gradient-tracking descent scheme with fixed finite-bit communication and show that, under bounded quantization errors, the method converges R-linearly to a quantization-dependent neighborhood of the global optimizer. Second, we introduce an adaptive quantization strategy that dynamically adjusts the number of transmitted bits according to the current convergence stage. By forcing the quantization distortion to decay proportionally to the optimization error, the proposed adaptive scheme recovers exact linear convergence to the optimal solution while substantially reducing the cumulative communication load. Third, we develop a fully distributed 1-bit communication mode in which UAVs exchange only sign information and use coordinate-wise majority voting to aggregate both descent and consensus directions. The robust linear-contraction property is proved to a small neighborhood under a sign-Polyak–Lojasiewicz condition and a probabilistic majority-correctness assumption. Full article

The increasing penetration of photovoltaic (PV) systems in distribution networks has introduced new challenges in voltage regulation and energy loss mitigation, particularly under time-varying loading conditions. This paper presents a constrained multi-objective mathematical optimization framework for the optimal allocation and sizing of PV-STATCOM devices in radial distribution systems. The problem is formulated as a nonlinear optimization model that minimizes the daily energy losses over a 24 h operating horizon while satisfying network operational constraints, inverter capacity limits, and renewable penetration restrictions. To efficiently solve the resulting non-convex optimization problem, a metaheuristic algorithm based on the weighted mean of vectors (WMV) is employed. The WMV method integrates wavelet-based weighting mechanisms, mean-driven update rules, vector combination strategies, and a local refinement operator to balance global exploration and local exploitation within the feasible search domain. Constraint violations are handled through a penalty-based mathematical transformation of the objective function. The proposed framework is validated on the IEEE 33-bus and IEEE 69-bus distribution systems under realistic daily load variations. The numerical results demonstrate significant reductions in daily energy losses compared to differential evolution, particle swarm optimization, artificial rabbits optimization, and golden search optimization algorithms. Furthermore, convergence analysis confirms the robustness and computational efficiency of the WMV approach in solving large-scale constrained power system optimization problems. Full article

To address the issues of information leakage risks faced by the base station (BS) when communicating with multiple users in an integrated sensing and communication (ISAC) system, as well as the blockage of the direct link between the BS and the target to be detected, which limits sensing functionality, this paper introduces the active intelligent reflecting surface (IRS) into the ISAC system. By creating a virtual line-of-sight (LoS) path, signal blockage is effectively mitigated, while the active IRS enhances the incident signal strength and adjusts the reflection phase shifts, thereby improving the reliability and security of communication. This paper proposes a joint optimization scheme for the active IRS-assisted ISAC system, which jointly designs the BS beamforming and the IRS reflection coefficient matrix. A non-convex optimization problem is formulated with the objective of maximizing the radar output signal-to-noise ratio (SNR) subject to communication performance constraints. To solve this problem, this paper employs an iterative algorithm based on alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR). Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes without IRS assistance and with passive IRS assistance in terms of enhancing the sensing performance of the ISAC system. Full article

In this paper, we propose a Newton-inspired black-box optimization algorithm for multiobjective optimization in constrained ergodic Markov chain environments. The method is motivated by challenges in application areas, where decision-making under uncertainty and limited access to structural information is pervasive. A central contribution of the proposed algorithm is the complexity analysis, which yields substantial computational advantages over conventional optimization approaches. Operating in a purely black-box setting, the algorithm relies exclusively on function evaluations and derivative approximations, without requiring explicit knowledge of the objective function’s internal structure. To approximate system dynamics, we employ an Euler-based scheme that enhances the scalability and adaptability of convex optimization problems. While Markov chains are seldom leveraged in black-box optimization, we demonstrate that constrained ergodic Markov chains constitute a powerful and underexplored modeling framework for learning and decision-making under structural constraints. We provide a complexity analysis and illustrate the effectiveness of the proposed method through a numerical example, highlighting its potential to advance applications in multiobjective optimization and decision-making. Full article