Search Results (183)

Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, with the avoiding local minima algorithm (PSO-ALM), uses a novel fitness function that can prevent the PSO search from trapping into the local minima and thus prevent the mobile robot from misidentifying the actual location. The fitness function penalizes nonsense solutions by introducing continuous integrity checks of solutions between two different consecutive locations. The proposed methodology enables accurate and real-time global localization of a mobile robot, given the underlying a priori map, with a consistent and predictable time complexity. Numerical simulations and real-world laboratory experiments with different a priori map accuracies have been conducted to prove the proper functioning of the method. The results have been compared with the benchmarks, i.e., the plain vanilla PSO and the built-in robot’s odometrical method, a genetic algorithm with included elitism and adaptive mutation rate (GA), the same GA algorithm with the included ALM algorithm (GA-ALM), the state-of-the-art plain vanilla golden eagle optimization (GEO) algorithm, and the same GEO algorithm with the added ALM algorithm (GEO-ALM). The results showed similar performance with the odometrical method right after recalibration and significantly better performance after some traveled distance. The GA and GEO algorithms with or without the ALM extension gave us similar results according to the accuracy of localization. The optimization algorithms’ performance with added ALM algorithms was much better at not getting caught in the local minimum, while the PSO-ALM algorithm gave us the overall best results. Full article

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Background: Multiple mechanisms might lead to cancer-related hypercoagulability. In brain tumors, podoplanin, via its ability to activate platelets, seems to play a crucial role in developing venous thromboembolism (VTE). Different stimuli (including activated platelets) can trigger the release of prothrombotic neutrophil extracellular traps (NETs) by neutrophils. It remains to be elucidated whether podoplanin-induced platelet aggregates might also impact NET formation and subsequent hypercoagulability and thrombosis. Methods: Patients with glioma were enrolled in this prospective observational cohort study. The primary endpoint was VTE. Immunohistochemical staining of NETs (via citrullinated histone H3 [H3Cit]) and neutrophils (via myeloperoxidase [MPO]) was conducted in glioma specimens and correlated with intravascular platelet clusters (via CD61) and podoplanin. Results: In total, 154 patients were included. H3Cit+ tumor vessels were found in 45/154 cases. H3Cit were significantly associated with increased intravascular platelet clusters (CD61− vs. CD61+ vs. CD61++ vs. CD61+++: 3.7% (1/27) vs. 18.6% (11/59) vs. 39.4% (13/33) vs. 57.1% (20/35), p < 0.001) and podoplanin expression (PDPN− vs. PDPN+: 14.3% (7/49) vs. 36.2% (38/105), p = 0.007) in the tumor tissue. Furthermore, H3Cit+ tumor vessels were significantly associated with tumor-infiltrating MPO+ neutrophils (H3Cit− vs. H3Cit+, median [Q1-Q3]: 6.0 [3.3–12.3] vs. 12.5 [5.9–22.0] cells/mm², p < 0.001) and with D-dimer levels (H3Cit− vs. H3Cit+: 0.53 [0.32–1.10] vs. 0.84 [0.46–2.75] µg/mL, p = 0.034). The VTE risk was not linked to H3Cit+ tumor vessels (p = 0.613, log-rank). Conclusions: H3Cit in tumor vessels was not associated with VTE. However, H3Cit was linked to a local procoagulant phenotype in glioma, thereby potentially contributing to a systemic hypercoagulable state and thrombus formation. Full article

We design a one-dimensional magnetoelastic phononic crystal slab composed of the smart magnetostrictive material Terfenol-D and pure tungsten. Band inversion and topological phase transitions are achieved by modifying the geometric parameters of the non-magnetic medium within the unit cell. The emergence of topological interface states within overlapping bandgaps, exhibiting distinct topological properties, along with their robustness against interfacial structural defects, is confirmed. The coupling effects between adjacent topological interface states in a sandwich-like supercell configuration are investigated, and their tunability under external magnetic fields is demonstrated. A Su-Schrieffer-Heeger (SSH) phononic crystal slab system under gradient magnetic fields is proposed. Critically, and in stark contrast to previous static or structurally graded designs, we achieve reconfigurable rainbow trapping of topological interface states solely by reprogramming the gradient magnetic field, leaving the physical structure entirely unchanged. This highly localized, compact, and broadband-tunable topological rainbow trapping system design holds significant promise for applications in elastic energy harvesting, wave filtering, and multi-frequency signal processing. Full article

Proteins that span cellular membranes represent around 30% of the proteome and over 50% of drug targets. A variety of synthetic and naturally-occurring small organic molecules interact with membrane proteins and up- and down-regulate protein function. The atomic details of these regulatory molecules offer important information about protein function and aid the discovery, refinement and optimization of new drugs. X-ray crystallography and cryo-electron microscopy (cryo-EM) are not always able to resolve the structures of small molecules in their physiological sites on membrane proteins, particularly if the molecules are unstable or are reactive enzyme substrates. Solid-state nuclear magnetic resonance (SSNMR) is a valuable technique for filling in missing details on the conformations, dynamics and binding environments of small molecules regulators of membrane proteins. SSNMR does not require diffracting crystals possessing long-range order and can be performed on proteins within their native membranes and with freeze-trapping to maintain sample stability. Here, work over the last two decades is described, in which SSNMR methods have been developed to report on interactions of the ATP substrate with the Na,K-ATPase (NKA), an ion-transporting enzyme that maintains cellular potential in all animals. It is shown how a combination of SSNMR measurements on membranous NKA preparations in the frozen and fluid states have provided unique information about the molecular conformation and local environment of ATP in the high-affinity nucleotide site. A combination of chemical shift analysis using density functional theory (DFT) calculations, dipolar coupling measurements using REDOR and measurements of the rates of proton spin diffusion is appraised collectively. The work described herein highlights the methods developed and challenges encountered, which have led to a detailed and unrivalled picture of ATP in its high-affinity binding site. Full article

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. Full article

Electrons trapped above the surface of helium provide a means to study many-body physics free from the randomness that comes from defects in other condensed-matter systems. Localizing an electron in an electrostatic quantum dot makes its energy spectrum discrete, with controlled level spacing. The lowest two states can act as charge qubit states. In this paper, we study how the coupling to the quantum field of capillary waves on helium—known as ripplons—affects electron dynamics. As we show, the coupling can be strong. This bounds the parameter range where electron-based charge qubits can be implemented. The constraint is different from the conventional relaxation time constraint. The electron–ripplon system in a dot is similar to a color center formed by an electron defect coupled to phonons in a solid. In contrast to solids, the coupling in the electron on helium system can be varied from strong to weak. This enables a qualitatively new approach to studying color center physics. We analyze the spectroscopy of the pertinent synthetic color centers in a broad range of the coupling strength. Full article

Hybrid metaheuristic algorithms have been widely used to solve global optimization problems, making the concept of hybridization increasingly important. This study proposes a new hybrid multi-strategy metaheuristic algorithm named COSGO, which combines the strengths of grey wolf optimization (GWO) and Sand Cat Swarm Optimization (SCSO) to effectively address global optimization tasks. Additionally, a chaotic opposition-based learning strategy is incorporated to enhance the efficiency and global search capability of the algorithm. One of the main challenges in metaheuristic algorithms is premature convergence or getting trapped in local optima. To overcome this, the proposed strategy is designed to improve exploration and help the algorithm escape local minima. As a real-world application, multi-level thresholding for color image segmentation—a well-known problem in image processing—is studied. The COSGO algorithm is applied using two objective functions, Otsu’s method and Kapur’s entropy, to determine optimal multi-level thresholds. Experiments are conducted on 10 images from the widely used BSD500 dataset. The results show that the COSGO algorithm achieves competitive performance compared to other State-of-the-Art algorithms. To further evaluate its effectiveness, the CEC2017 benchmark functions are employed, and a Friedman ranking test is used to statistically analyze the results. Full article

One of the fundamental problems of quantum statistical physics is how an ideally isolated quantum system can ever reach thermal equilibrium behavior despite the unitary time evolution of quantum-mechanical systems. Here, we study, via explicit time evolution for the generic model system of an interacting, trapped Bose gas with discrete single-particle levels, how the measurement of one or more observables subdivides the system into observed and non-observed Hilbert subspaces and the tracing over the non-measured quantum numbers defines an effective, thermodynamic bath, induces the entanglement of the observed Hilbert subspace with the bath, and leads to a bi-exponential approach of the entanglement entropy and of the measured observables to thermal equilibrium behavior as a function of time. We find this to be more generally fulfilled than in the scenario of the eigenstate thermalization hypothesis (ETH), namely for both local particle occupation numbers and non-local density correlation functions, and independent of the specific initial quantum state of the time evolution. Full article

We investigate the entanglement dynamics of two giant atoms coupled to a one-dimensional photonic lattice with synthetic chirality. The atoms are connected to multiple lattice sites in a braided configuration and interact with a structured photonic reservoir featuring direction-dependent hopping phases. By tuning the atomic detuning and the synthetic gauge phase, we identify distinct dynamical regimes characterized by decoherence-free population exchange, damped oscillations, long-lived revivals, and excitation trapping. Using a combination of time-domain simulations and resolvent-based analysis, we show how interference and band structure effects lead to the emergence of bound states, quasi-bound states, and phase-dependent entanglement dynamics. We compare the initial states with localized and delocalized atomic excitations, demonstrating that pre-existing entanglement can enhance the robustness against decoherence or accelerate its loss, depending on the system parameters. These results highlight the utility of synthetic photonic lattices and nonlocal emitter configurations in tailoring quantum coherence, entanglement, and information flows in structured environments. Full article

This study of the BaSO₄-Bi phosphor has revealed that the accumulated energy after external optical excitation exhibits specific characteristics. During irradiation with photon energy exceeding the bandgap, in addition to the intrinsic ultraviolet emission of the Bi³⁺ ion, several recombination emissions and emission from the Bi²⁺ ion are observed. At 80 K, the recombination luminescence states and Bi²⁺ ion emission form combined electronic states. Upon heating of the BaSO₄-Bi phosphor, these combined electronic states decay into recombination emissions at 2.34 eV, 2.4 eV, 3.1 eV, and 2.7 eV, as well as Bi²⁺ ion emission at 1.97 eV. It is assumed that the 2.34 eV, 2.4 eV, and 3.1 eV emissions are associated with the recombination of electrons released from ionized ${SO}_4^{3-}$ electron trapping centers with nonequivalently localized holes in the host lattice. The 2.7 eV emission is attributed to the decay of an exciton formed by electron–hole recombination near a Bi³⁺ ion. Full article

This study introduces a state transition simulated annealing algorithm that incorporates integrated destruction operators and backward learning strategies (DRSTASA) to address complex challenges in UAV path planning within multidimensional environments. UAV path planning is a critical optimization problem that requires smooth flight paths, obstacle avoidance, moderate angle changes, and minimized flight distance to conserve fuel and reduce travel time. Traditional algorithms often become trapped in local optima, preventing them from finding globally optimal solutions. DRSTASA improves global search capabilities by initializing the population with Latin hypercube sampling, combined with destruction operators and backward learning strategies. Testing on 23 benchmark functions demonstrates that the algorithm outperforms both traditional and advanced metaheuristic algorithms in solving single and multimodal problems. Furthermore, in eight engineering design optimization scenarios, DRSTASA exhibits superior performance compared to the STASA and SNS algorithms, highlighting the significant advantages of this method. DRSTASA is also successfully applied to UAV path planning, identifying optimal paths and proving the practical value of the algorithm. Full article

Brassica campestris L. var. toria, a major oilseed crop cultivated in India, is primarily an entomophilic species. Hymenopteran flower-visiting species provide important ecological services like pollination or pest control in Brassica crops. In this context, a study was conducted during 2015–2017 in three localities in Assam, a state in northeast India that falls under two global biodiversity hotspots—Indo–Burma and Himalayan—to bring data on the diversity of hymenopteran flower visitors of toria crops by using multiple sampling techniques and to compare the efficiency of these techniques. Altogether, nine sampling treatments were used. To assess the sampling effectiveness of the different treatments, the data from the two cropping periods of toria in each locality were analysed cumulatively and comparatively. Variable transect outperformed the other sampling methods with the highest number of hymenopteran flower visitor species recorded in toria crops at 54, representing 84.4% of the total number of species, and was followed by standard transect (34 species, 53.1%), elevated yellow trap (22 species, 34.4%), and observation plot (21 species, 32.8%). However, the importance of multiple sampling methods in this diversity study was noticed; one method alone could not sample all the species recorded. The cluster of traps and netting with transect walks was proven to be complementary and considered useful for future research studies in the upstream basin of the Burhidihing River of Assam, India. Full article

A cable accessory is a critical component in constructing high-voltage direct current (HVDC) power grids, and it is typically composed of multiple materials. Due to the discontinuity of the insulation medium, it is prone to failure. This study focuses on a two-layered composite insulation medium simplified from HVDC cable accessories, and its surface potential decay (SPD) characteristics are related to the space charge transport characteristics. Previous studies on surface charge migration have been limited and primarily focused on single-layered insulation materials. However, the actual insulation structure is mostly composite. Therefore, it is of great practical significance to explore the surface charge migration characteristics of two-layered structures. This study presents a bipolar charge transport model after pre-depositing surface charges to investigate the surface charge migration characteristics of an ethylene–propylene–diene monomer (EPDM)/polyethylene (PE) two-layered polymer film. The effects of charge injection and trap related to nano-doping, local defects, and thermal aging on the surface potential decay (SPD) and space charge distribution in EPDM/PE were analyzed. The results show that the increase in the electron injection barrier slows surface charge dissipation and inhibits charge accumulation at the interface. An increase in the trapping coefficient leads to a higher surface potential in the stable state and a greater space charge density. During the early depolarization stage, the SPD rate is weakly dependent on the trap depth, with charge migration primarily governed by the external electric field. Full article