Search Results (1,079)

Sound source localization based on microphone arrays has attracted extensive research interest in the fields of speech communication, detection and tracking. However, conventional near-field beamforming typically assumes free-field propagation and ignores coherent ground reflections, which bias phase estimates at floor-mounted arrays and degrade localization accuracy. To address this, the total Green’s function is reformulated by embedding the image-source reflected path directly into the beamforming propagation model. Unlike conventional approaches that assume free-field propagation, this formulation enables phase compensation to jointly account for the direct and ground-reflected wavefronts. The in-phase superposition of signals is then realized through frequency-domain phase compensation, and the sound source position is estimated via the maximum likelihood criterion. Subsequently, a dual-mode operation mechanism of automatic broadband scanning and manual single-frequency analysis is implemented, with frequency-weighted wideband spatial spectrum processing for noise and aliasing suppression. Finally, a real-time sound source localization system based on a 4 × 4 MEMS array is designed. Experimental results show that sound source localization in a broad frequency band can be achieved at standoff distances of 0.1 m–0.3 m from the array plane, with a localization accuracy of less than 0.015 m. The proposed method demonstrates strong robustness against ground-reflection interference and has potential applications in acoustic monitoring, industrial fault diagnosis and other related areas. Full article

Graph labeling is an important area of graph theory that studies the assignment of integers to the vertices or edges of a graph according to specific rules. Among these labeling methods, distance magic labeling has attracted considerable attention due to its interesting combinatorial structure and applications in network design and communication systems. A distance magic labeling of a graph is a bijection from the vertex set to the set $\{1,2,\dots ,n\}$ such that the sum of the labels of the neighbors of each vertex is equal to a constant called the magic constant. This paper investigates the existence and construction of distance magic labelings for certain families of complete bipartite graphs. Two principal cases are studied, namely graphs of the form $K_{2m,2m}$ and $K_{2m-1,2m}$. For graphs of the form $K_{2m,2m}$, an explicit construction is developed showing that these graphs admit a distance magic labeling for every integer $m\ge 1$. The corresponding magic constant is derived as $k=m(4m+1)$ and the validity of the construction is verified by proving bijectivity of the labeling function and equality of vertex weights. A similar constructive approach is applied to graphs of the form $K_{2m-1,2m}$, where distance magic labelings are obtained using structured arithmetic label distributions. These constructions are further extended by applying vertex swapping techniques and block-based arguments to generate complete bipartite graphs $K_{p,q}$ that preserve the same magic constant for certain values of p and q. These findings contribute to the understanding of how arithmetic structure and partition properties influence the existence of distance magic labelings and suggest several directions for further research in graph labeling theory. Full article

We performed a before–after control–impact (BACI) experiment to assess the impact of red-legged partridge releases on mammalian predators, a common game management measure. During three consecutive years (2014–2016), 100 farm-reared red-legged partridges were kept every year for four weeks in an acclimatisation pen on a hunting estate in southern Spain. Before and after releasing the birds, we estimated partridge and predator abundances in a 1000 m buffer around the release point and at a second point where no partridges were released as a control. Results showed that red fox and red-legged partridge abundances increased significantly close to the release points immediately after releasing the birds. Fox abundance was significantly associated with partridge abundance and distance to the pen. Other non-fox predators did not show the same patterns, nor did their abundance increase after the release. The experimental results suggest that the red fox is the main mammalian predator associated with the released birds and that partridge releases may produce an attraction effect on this predator species. The release of partridges can adversely affect the image of the fox as a predatory species that may take advantage of a newly abundant resource, creating a conservation problem that encourages conflict between predators, without distinguishing species, and hunters. Full article

Planning future electric vehicle charging infrastructure requires assessment of spatial suitability, demand, network gaps, and expected accessibility benefits. This study developed a national-scale framework for Bosnia and Herzegovina integrating machine-learning (ML) suitability modelling, post-modelling prioritisation, reproducible candidate selection, and scenario-based population accessibility assessment. A dataset of 188 EVCS locations and spatially balanced pseudo-absences was analysed using 28 spatial predictors. Five classifiers were evaluated through spatial cross-validation, with XGBoost providing the most balanced performance. After feature reduction, the final model retained five predictors: road density, travel time to hotels, travel time to parking, distance to major roads, and travel time to tourist attractions. The priority index combined modelled suitability with population demand, LU/LC opportunity, and the travel-time gap to existing EVCSs. Candidate locations were derived using a deterministic settlement- and road-constrained procedure followed by network-based spacing, and scenarios with 10, 20, 40, 50, and 60 new EVCSs were evaluated. At the 10 min threshold, population coverage increased from 57.9% for the existing network to 65.4% with 10 new EVCSs, 68.7% with 20, 73.8% with 40, 75.6% with 50, and 77.0% with 60. The framework provides a reproducible basis for national EVCS investment screening while distinguishing occurrence-based suitability, strategic deployment priority, and expected accessibility gains. Full article

University students, as high-quality human capital, play a critical role in promoting the sustainable development of urban systems. Drawing on survey data from 525 university students in Tianjin, China, this study employs eight widely used machine learning algorithms to predict and interpret settlement intentions. The best-performing model was selected based on predictive performance, and the SHapley Additive exPlanations (SHAP) approach was employed to reveal the relative importance and interaction effects of the influencing factors. The results indicate that Random Forest is the optimal predictive model, achieving an 83.8% accuracy rate in predicting settlement intentions. Among the 18 predictors examined, family support, occupation prospects, job opportunities, Hukou attraction, and climate & environment were the five most influential positive factors affecting settlement intentions, whereas hometown distance was identified as the strongest negative predictor. We further analyzed the heterogeneity and interaction effects of these influencing factors. This study provides new insights into systematically explaining university students’ settlement intentions. Full article

Nonlamellar liquid crystalline nanodispersions produced from single monoacylglycerols or from their combinations with fatty acids or other amphiphiles have attracted interest owing to their structural versatility and tunability. In this study, we investigated the effect of dilinolein (DLO) incorporation on the structural features of Pluronic F127-stabilized monolinolein (MLO) nanodispersions using small-angle X-ray scattering (SAXS), cryogenic transmission electron microscopy (cryo-TEM), and dynamic light scattering (DLS). We report a lipid composition-dependent direct colloidal transformation from hexosomes, defined as nanoparticles with an ordered internal inverse hexagonal (H₂) phase, toward nanoparticles with highly disordered internal nanostructures upon the partial replacement of MLO by DLO. Small-angle X-ray scattering (SAXS) measurements showed that, at relatively low DLO content, the MLO-rich MLO:DLO 90:10 (w/w) nanodispersion retained an ordered internal H₂ phase, with three well-defined characteristic Bragg peaks, whereas the SAXS patterns recorded for nanodispersions containing ≥20 wt% DLO displayed loss of the characteristic H₂ Bragg reflections and broad correlation maxima, indicating loss of long-range internal periodicity and the emergence of highly disordered internal nanostructures. Similarly, the control nanodispersion prepared from DLO alone displayed a broad, low-intensity correlation maximum rather than distinct Bragg peaks, consistent with a highly disordered internal nanostructure. For the nanodispersions displaying broad SAXS correlation maxima, the SAXS-derived characteristic distance decreased monotonically from 4.53 to 2.87 nm as the DLO fraction increased. Additional cryo-TEM observations of two nanodispersions containing relatively high DLO contents revealed predominantly spherical nanoparticles characterized by an intense lipid core and discernible internal nanoscale features. However, these internal features were not sufficiently resolved to allow full characterization and unambiguous assignment of the internal phase, consistent with a highly disordered inverse nanostructure that may include an L₂-like inverse-micellar organization alongside other possible arrangements such as nanoemulsion droplets. Taken together, the SAXS and cryo-TEM observations indicate a composition-driven colloidal transformation from hexosomes with a well-defined internal H₂ phase toward nanoparticles with a highly disordered internal phase as the DLO content increases. The experimental findings suggest that DLO modifies lipid packing at the MLO–water interface and promotes more negative spontaneous curvature, favoring the observed structural transformation. The ability to tune the internal nanostructure by lipid composition while maintaining nanoscale particle size and low dispersity makes these nanodispersions attractive platforms for drug nanocarrier development. Full article

Emerging technology cultivation is formulated as a finite-time stochastic reachability problem under a budget-simplex constraint. Novelty potential, implementation feasibility, and industrial embeddedness form a three-state cascade Itô system, and success requires their joint entrance into a target set before a fixed horizon. The analysis establishes positive invariance, a unique globally attractive deterministic equilibrium, and an explicit asymptotic budget boundary whose minimum occurs at the maximizer of the knowledge-distance kernel. For specified constant-allocation rules, projected Euler–Maruyama simulation provides rule-specific node-monitoring probabilities and budget thresholds. A repeated finite-candidate sample-average approximation with independent candidate-generation, selection, and validation samples assesses the additional gain from adaptive candidate selection. The numerical results show that the deterministic grid-search rule reaches prescribed probability levels at lower intervention rates than balanced allocation, while the repeated SAA procedure yields further gains in the tested transition region. Finite-time success therefore depends on both the total budget rate and its allocation across cascade-dependent channels. Full article

The main protease (MPro) of coronaviruses (CoVs) is an essential enzyme involved in viral replication and represents an attractive target for antiviral drug discovery. Based on the similar binding pocket residues within the MPro of different CoVs, this study aimed to identify potential inhibitors of SARS-CoV-2 MPro from PDB ID 6M2N using integrated computational approaches. Interaction-based pharmacophore modeling, virtual screening, molecular docking, MM-GBSA binding energy calculation, and molecular dynamics simulation (MDS) were performed using BIOVIA Discovery Studio. The validated pharmacophore model was utilized to screen the ZINC database, followed by docking and 100 ns MDS analyses of the top-ranked compounds. The pharmacophore model 01 demonstrated favorable predictive performance (AUC = 0.781). Virtual screening identified 483 compounds, from which 15 compounds were selected for docking studies. Among them, ZINC95473654 (Lig-1), ZINC95473725 (Lig-2), and ZINC08792368 (Lig-3) exhibited strong binding affinity toward MPro. Lig-1 demonstrated the best docking score and binding free energy, along with stable interactions with key catalytic residues HIS41, CYS145, and GLU166. MDS analyses further confirmed that Lig-1, Lig-2 and Lig-3 maintained stable conformations. The hydrogen bond distance monitoring and post MDS-MM-GBSA results suggest Lig-1 followed by Lig-3 as an inhibitor for MPro and persistent intermolecular interactions throughout the 100 ns simulation period. The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 MPro, representing promising candidates for further experimental validation. Full article

Hydration plays an essential role in protein–protein interactions. Coarse-grained modeling provides an efficient way to treat hydrated protein complexes without the use of extra-large computational resources. To enhance the capabilities of coarse-grained modeling, we developed an integral equation theory based on the solution of the Ornstein–Zerinke equation to evaluate the hydration structure of peptides and proteins within the framework of coarse-grained modeling. Our current version is based on the SPICA force field, which considers distance-dependent interaction potentials between solvent particles and amino acid segments. Our approach involves two key procedures: an accurate estimation of the structure factor of the uniform fluid and the specific construction of bridge functions obtained from MD simulations. The use of a special hybrid closure allows us to reproduce not only the details of the structure factor, but also the isothermal compressibility obtained from the simulations. The developed bridge functions include two components: an analytical repulsive contribution, which is primarily responsible for the thermodynamic properties, and an attractive contribution obtained from MD simulations. The main assumption in the construction is that the contribution of individual amino acids to the attractive bridge function is additive. By parameterizing the bridge functions, we reproduced details of the hydration structure and accurately calculated the hydration energy for various peptides and proteins. Our method is computationally inexpensive and appears to be suitable for the rapid processing of hydrated proteins of any size. Full article

Taxi fleets operate intensively, accumulate high annual mileage, and contribute disproportionately to urban greenhouse gas emissions, making them attractive candidates for transport electrification. Despite growing interest in electric mobility, integrated evaluations of the environmental, energy, and economic implications of taxi fleet electrification remain limited, particularly for medium-sized cities in Türkiye. This research examines the replacement of the commercial taxi fleet operating in the central districts of Denizli with battery electric vehicles (BEVs) through a framework that integrates operational emission estimation, electricity demand, charging infrastructure, and life-cycle cost analysis (LCCA). Operational emissions were quantified using the IPCC Tier 1 fuel-based approach together with a distance-based consistency check. Under the adopted assumptions, electrification reduced annual operational CO₂ emissions by 4217.48 tCO₂ (57.9%). The electrified fleet required 18.36 MWh of electricity per day (6.70 GWh annually), while estimated peak charging demand varied between 1.22 MW and 5.57 MW, depending on the charging strategy. Economic evaluation showed a positive net present value (NPV) of 1.32 million TL per vehicle, an internal rate of return (IRR) of 80.86%, and a discounted payback period (DPP) of 1.37 years. Although profitability varied with energy prices, vehicle costs, and annual mileage, fleet electrification remained economically feasible across all scenarios considered. These results suggest that electrifying commercial taxi fleets can support urban decarbonization while remaining financially attractive when accompanied by appropriate charging infrastructure and coordinated transport planning. Full article

Gonadotropin-Releasing Hormone receptors (GnRHRs) are overexpressed in several hormone-dependent malignancies, making them attractive molecular targets for selective anticancer drug delivery. Peptide–drug conjugates (PDCs) are a promising therapy for cancer and autoimmune diseases with high specificity and reduced toxicity. In this study, the three-dimensional structures of two previously synthesized mitoxantrone–GnRH conjugates, con3 and con7, were elucidated using high-resolution NMR spectroscopy in combination with molecular dynamics (MD) simulations. Complete ¹H and ¹³C resonance assignments were achieved in DMSO-d₆ through two-dimensional NMR experiments. NOESY-derived distance restraints were subsequently used to refine the conformational ensembles obtained from MD simulations performed in water and DMSO. Both conjugates exhibited compact bent conformations with a U-shaped peptide backbone. The mitoxantrone moiety is positioned close to the peptide backbone in water simulations and NMR-refined structures, while it is positioned farther away in DMSO, without affecting the orientation of key residues involved in GnRH receptor binding. Importantly, His², Trp³, and Arg⁸ remain solvent-exposed, whereas the disulfide bond is easily accessible to the solvent, consistent with the proposed drug release mechanism by the thioredoxin system. NMR-restrained molecular modeling confirmed the dominant conformational features predicted by the unconstrained theoretical simulations. Overall, these findings provide better structural understanding of the molecular organization of mitoxantrone–GnRH conjugates, highlighting key receptor-recognition residues and supporting both the proposed thioredoxin-mediated drug release mechanism and their previously reported biological properties. These insights may facilitate the rational design and optimization of improved GnRH peptide–drug conjugates for targeted therapy. Full article

With the development of constellations and satellite chains, trajectory planning for constellation satellites has gradually attracted research attention. To address the infeasibility issues of trapezoidal sequential convex programming (T-SCP) in this strong non-convexity problem, this paper proposes high-precision adaptive SCP based on the Hermite Simpson method for Low-Earth-Orbit (LEO) constellation aggregation and aimed at enhanced local reconnaissance with practical constraints, including collision avoidance. The method introduces an adaptive trust region and slack variables, which avoid convergence failures due to strong nonlinearity or large state variations over long distances and accelerate the convergence rate. Furthermore, a third-order accurate Hermite Simpson method is adopted along with a adaptive iterative collision warning mechanism, improving accuracy while reducing computational cost. Simulation results demonstrate that the proposed method improves computational efficiency by 6% and 21% compared with the T-SCP and pseudospectral method, respectively. When the number of discrete nodes is 50, it achieves position errors of 284.7 m and velocity errors of 0.33 m/s, and these errors are far lower than those of T-SCP. Monte Carlo simulations across 10,000 scenarios validate the robustness of the proposed method with a 91% overall success rate and 100% for LEO. Full article

Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such workflows, allowing geographically separated quantum processors to execute joint computations using shared entanglement while preserving data privacy. In this work, we investigate the feasibility of satellite-enabled distributed quantum computing for federated quantum learning. As a representative application, we consider a distributed distance-based quantum classifier in which multiple parties contribute local data through quantum operations. To support this application, we develop a hybrid space-ground quantum network architecture in which satellites distribute entanglement between distant ground stations. The communication layer is combined with a noise-aware neutral-atom processor model, enabling a system-level analysis that captures both network and hardware constraints. Simulation results across varying network sizes, feature dimensions, and coherence regimes show that classifier performance is jointly determined by communication resources, processor noise, and data representation. In low-coherence regimes, decoherence destroys the classifier’s discriminative signal, whereas high-coherence regimes reveal limitations arising from feature-space conditioning and feature redundancy. These results demonstrate that satellite quantum networks could support distributed quantum learning over long distances, while highlighting the importance of coherence time, entanglement-distribution performance, and learning-aware data encoding for future large-scale deployments. Full article

With the rapid growth of collaborative production demands, social manufacturing (SM) has emerged as a decentralized paradigm that enables the dynamic allocation of socialized manufacturing resources (MRs). Organizing manufacturing cluster into collaborative networks under this paradigm is vital for the aggregation of discrete resources. However, due to the self-interested nature of manufacturing entities, traditional network formulation methods fail to mitigate the risks of structural paralysis caused by passive behaviors. This paper proposes a dynamic collaborative network generation and evolution mechanism based on multi-agent evolutionary games. By quantifying the collaborative willingness of participants, this mechanism resolves the difficulties of precise resource clustering and stable network evolution. Specifically, a collaborative potential (ColP) model is established by integrating the attraction of capability complementarity, the repulsion of spatial distance, and dynamic load constraints to drive the self-organized generation of network topology. Next, a multi-agent game model is designed, where the connectivity and trust states of agents are mapped as penalty costs. Finally, a trust-driven dynamic rewiring mechanism is proposed for network evolution, employing the Fermi rule to update the strategies of agents. Experimental results demonstrate that the proposed models significantly improve the modularity and resource alignment of the collaborative network compared with benchmark paradigms. Full article

Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly understood. Here, we investigate the role of internal fluctuations—temporal micro-variability of hidden channel states—in a trained GNCA model, hypothesizing that they constitute a functional component of the dynamics rather than merely residual stochastic noise. We analyzed the trained model through dynamical-systems analysis (low-dimensional embedding and recurrence analysis of collective state trajectories) and information-theoretic analysis (transfer entropy and partial information decomposition), including its response to localized damage and to suppression of small-magnitude updates. These analyses show that internal fluctuations are spatially structured, dynamically coupled to an attracting collective state, and associated with distributed small-magnitude updates that contribute to damage recovery. Damage induces a global deviation in latent state space followed by gradual re-convergence, and suppressing distributed small-magnitude updates associated with baseline fluctuation dynamics outside a permissive radius that encompasses the majority of the cells significantly impairs recovery. Transfer entropy analysis characterizes a spatially differentiated repair response: corrective inward flow near the damage site coexists with outward perturbation propagation at greater distances. Partial information decomposition further suggests a regime shift from synergy-dominant resting computation to redundancy-increased coordination during recovery. These findings indicate that GNCA self-maintenance and self-repair emerge from high-dimensional nonlinear collective dynamics in which internal fluctuations serve as a functional component supporting information flow, coordination, and return toward an attracting recurrent state. Full article