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21 pages, 11040 KB  
Article
DPDN-YOLOv8: A Method for Dense Pedestrian Detection in Complex Environments
by Yue Liu, Linjun Xu, Baolong Li, Zifan Lin and Deyue Yuan
Mathematics 2025, 13(20), 3325; https://doi.org/10.3390/math13203325 (registering DOI) - 18 Oct 2025
Abstract
Accurate pedestrian detection from a robotic perspective has become increasingly critical, especially in complex environments such as crowded and high-density populations. Existing methods have low accuracy due to multi-scale pedestrians and dense occlusion in complex environments. To address the above drawbacks, a dense [...] Read more.
Accurate pedestrian detection from a robotic perspective has become increasingly critical, especially in complex environments such as crowded and high-density populations. Existing methods have low accuracy due to multi-scale pedestrians and dense occlusion in complex environments. To address the above drawbacks, a dense pedestrian detection network architecture based on YOLOv8n (DPDN-YOLOv8) was introduced for complex environments. The network aims to improve robots’ pedestrian detection in complex environments. Firstly, the C2f modules in the backbone network are replaced with C2f_ODConv modules integrating omni-dimensional dynamic convolution (ODConv) to enable the model’s multi-dimensional feature focusing on detected targets. Secondly, the up-sampling operator Content-Aware Reassembly of Features (CARAFE) is presented to replace the Up-Sample module to reduce the loss of the up-sampling information. Then, the Adaptive Spatial Feature Fusion detector head with four detector heads (ASFF-4) was introduced to enhance the system’s ability to detect small targets. Finally, to accelerate the convergence of the network, the Focaler-Shape-IoU is utilized to become the bounding box regression loss function. The experimental results show that, compared with YOLOv8n, the mAP@0.5 of DPDN-YOLOv8 increases from 80.5% to 85.6%. Although model parameters increase from 3×106 to 5.2×106, it can still meet requirements for deployment on mobile devices. Full article
(This article belongs to the Special Issue Artificial Intelligence: Deep Learning and Computer Vision)
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25 pages, 9844 KB  
Article
Deep Learning and Geometric Modeling for 3D Reconstruction of Subsurface Utilities from GPR Data
by Peyman Jafary, Davood Shojaei and Krista A. Ehinger
Sensors 2025, 25(20), 6414; https://doi.org/10.3390/s25206414 - 17 Oct 2025
Abstract
Accurate underground utility mapping remains a critical yet complex task in Ground Penetrating Radar (GPR) interpretation, essential to avoiding costly and dangerous excavation errors. This study presents a novel deep learning-based pipeline for 3D reconstruction of buried linear utilities from high-resolution GPR B-scan [...] Read more.
Accurate underground utility mapping remains a critical yet complex task in Ground Penetrating Radar (GPR) interpretation, essential to avoiding costly and dangerous excavation errors. This study presents a novel deep learning-based pipeline for 3D reconstruction of buried linear utilities from high-resolution GPR B-scan data. Three state-of-the-art models—YOLOv8, YOLOv11, and Mask R-CNN—were employed for both bounding box and keypoint detection of hyperbolic reflections, using a real-world GPR dataset. On the test set, Mask R-CNN achieved the highest keypoint F1-score (0.822) and bounding box F1-score (0.867), outperforming the YOLO models. Detected summit points were clustered using a 3D DBSCAN algorithm to approximate the spatial trajectories of buried utilities. RANSAC-based line fitting was then applied to each cluster, yielding an average RMSE of 0.06 across all fitted 3D paths. The key innovation of this hybrid model lies in its use of real-world data (avoiding synthetic augmentation), direct summit point detection (beyond bounding box analysis), and a geometric 3D reconstruction pipeline. This approach addresses key limitations in prior studies, including poor generalizability to complex real-world scenarios and the reliance on full 3D data volumes. Our method offers a more practical and scalable solution for subsurface utility mapping in real-world settings. Full article
(This article belongs to the Section Radar Sensors)
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20 pages, 1334 KB  
Article
Spatial Decoupling of Biological and Geochemical Phosphorus Cycling in Podzolized Soils
by Daniel F. Petticord, Benjamin T. Uveges, Elizabeth H. Boughton, Brian D. Strahm and Jed P. Sparks
Soil Syst. 2025, 9(4), 115; https://doi.org/10.3390/soilsystems9040115 - 16 Oct 2025
Abstract
Phosphorus (P) is essential to life yet constrained by finite reserves, heterogeneous distribution, and strong chemical binding to soil minerals. Pedogenesis progressively alters the availability of P: in ‘young’ soils, P associated with Ca and Mg is relatively labile, while in ‘old’ soils, [...] Read more.
Phosphorus (P) is essential to life yet constrained by finite reserves, heterogeneous distribution, and strong chemical binding to soil minerals. Pedogenesis progressively alters the availability of P: in ‘young’ soils, P associated with Ca and Mg is relatively labile, while in ‘old’ soils, acidification and leaching deplete base cations, shifting P into organic matter and recalcitrant Al- and Fe-bound pools. Podzolized soils (Spodosols) provide a unique lens for studying this transition because podzolization vertically segregates these dynamics into distinct horizons. Organic cycling dominates the surface horizon, while downward translocation of Al, Fe, and humus creates a spodic horizon that immobilizes P through sorption and co-precipitation in amorphous organometal complexes. This spatial separation establishes two contrasting P pools—biologically dynamic surface P and mineral-stabilized deep P—that may be variably accessible to plants and microbes depending on depth, chemistry, and hydrology. We synthesize mechanisms of spodic P retention and liberation, including redox oscillations, ligand exchange, root exudation, and physical disturbance, and contrast these with strictly mineral-driven or biologically dominated systems. We further propose that podzols serve as natural experimental models for ecosystem aging, allowing researchers to explore how P cycling reorganizes as soils develop, how vertical stratification structures biotic strategies for nutrient acquisition, and how deep legacy P pools may be remobilized under environmental change. By framing podzols as a spatial analogue of long-term weathering, this paper identifies them as critical systems for advancing our understanding of nutrient limitation, biogeochemical cycling, and sustainable management of P in diverse ecosystems. Full article
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19 pages, 1101 KB  
Article
Computational and Parameter-Sensitivity Analysis of Dual-Order Memory-Driven Fractional Differential Equations with an Application to Animal Learning
by Ali Turab, Josué-Antonio Nescolarde-Selva, Wajahat Ali, Andrés Montoyo and Jun-Jiat Tiang
Fractal Fract. 2025, 9(10), 664; https://doi.org/10.3390/fractalfract9100664 - 16 Oct 2025
Viewed by 24
Abstract
Fractional differential equations are used to model complex systems where present dynamics depend on past states. In this work, we study a linear fractional model with two Caputo orders that captures long-term memory together with short-term adaptation. The existence and uniqueness of solutions [...] Read more.
Fractional differential equations are used to model complex systems where present dynamics depend on past states. In this work, we study a linear fractional model with two Caputo orders that captures long-term memory together with short-term adaptation. The existence and uniqueness of solutions are established using Banach and Krasnoselskii’s fixed-point theorems. A parameter study isolates the roles of the fractional orders and coefficients, yielding an explicit stability region in the (α,β)–plane via computable contraction bounds. For computation, we implement the Adams–Bashforth–Moulton (ABM) and fractional linear multistep (FLM) methods, comparing accuracy and convergence. As an application, we model animal learning in which proficiency evolves under memory effects and pulsed stimuli. The results quantify the impact of feedback timing on trajectories within the admissible region, thereby illustrating the suitability of dual-order fractional models for memory-driven behavior. Full article
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66 pages, 819 KB  
Article
Tossing Coins with an 𝒩𝒫-Machine
by Edgar Graham Daylight
Symmetry 2025, 17(10), 1745; https://doi.org/10.3390/sym17101745 - 16 Oct 2025
Viewed by 44
Abstract
In computational complexity, a tableau represents a hypothetical accepting computation path p of a nondeterministic polynomial time Turing machine N on an input w. The tableau is encoded by the formula ψ, defined as [...] Read more.
In computational complexity, a tableau represents a hypothetical accepting computation path p of a nondeterministic polynomial time Turing machine N on an input w. The tableau is encoded by the formula ψ, defined as ψ=ψcellψrest. The component ψcell enforces the constraint that each cell in the tableau contains exactly one symbol, while ψrest incorporates constraints governing the step-by-step behavior of N on w. In recent work, we reformulated a critical part of ψrest as a compact Horn formula. In another paper, we evaluated the cost of this reformulation, though our estimates were intentionally conservative. Here, we provide a more rigorous analysis and derive a polynomial bound for two enhanced variants of our original Filling Holes with Backtracking algorithm: the refined (rFHB) and streamlined (sFHB) versions, each tasked with solving 3-SAT. The improvements stem from exploiting inter-cell dependencies spanning large regions of the tableau in the case of rFHB, and by incorporating correlated coin-tossing constraints in the case of sFHB. These improvements are purely conceptual; no empirical validation—commonly expected by complexity specialists—is provided. Accordingly, any claim regarding P vs. NP remains beyond the scope of this work. Full article
(This article belongs to the Special Issue Symmetry in Solving NP-Hard Problems)
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18 pages, 3219 KB  
Article
Development of an Efficient Algorithm for Sea Surface Enteromorpha Object Detection
by Yan Liu, Xianghui Su, Ran Ma, Hailin Liu, Xiangfeng Kong, Fengqing Liu, Yang Gao and Qian Shi
Water 2025, 17(20), 2973; https://doi.org/10.3390/w17202973 - 15 Oct 2025
Viewed by 82
Abstract
In recent years, frequent outbreaks of Enteromorpha disasters in the Yellow Sea have caused substantial economic losses to coastal cities. In order to tackle the challenges of the low detection accuracy and high false negative rate of Enteromorpha detection in complex marine environments, [...] Read more.
In recent years, frequent outbreaks of Enteromorpha disasters in the Yellow Sea have caused substantial economic losses to coastal cities. In order to tackle the challenges of the low detection accuracy and high false negative rate of Enteromorpha detection in complex marine environments, this study proposes an object detection algorithm CEE-YOLOv8, improved from YOLOv8n, and establishes the Enteromorpha dataset. Firstly, this study integrates a C2f-ConvNeXtv2 module into the YOLOv8n Backbone network to augment multi-scale feature extraction capabilities. Secondly, an ECA attention mechanism is incorporated into the Neck network to enhance the perception ability of the model to different sizes of Enteromorpha. Finally, the CIoU loss function is replaced with EIoU to optimize bounding box localization precision. Experiment results on the self-made Enteromorpha dataset show that the improved CEE-YOLOv8 model achieves a 3.2% increase in precision, a 3.3% improvement in recall, and a 4.1% gain in mAP50-95 compared to the benchmark model YOLOv8n. Consequently, the proposed model provides robust technical support for future Enteromorpha monitoring initiatives. Full article
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23 pages, 3461 KB  
Article
Plasmonic Nanosensors for EGFR Detection: Optimizing Aptamer-Based Competitive Displacement Assays
by Alexandra Falamas, Andra-Sorina Tatar, Sanda Boca and Cosmin Farcău
Biosensors 2025, 15(10), 699; https://doi.org/10.3390/bios15100699 - 15 Oct 2025
Viewed by 193
Abstract
This study presents a comparative investigation of plasmonic sensing platforms based on colloidal gold nanoparticle (AuNP) suspensions and gold film over nanosphere (AuFoN) solid substrates for the detection of epidermal growth factor receptor (EGFR), an essential biomarker and therapeutic target in oncology. The [...] Read more.
This study presents a comparative investigation of plasmonic sensing platforms based on colloidal gold nanoparticle (AuNP) suspensions and gold film over nanosphere (AuFoN) solid substrates for the detection of epidermal growth factor receptor (EGFR), an essential biomarker and therapeutic target in oncology. The strategy relies on fluorescence emission modulation of an Atto647N-labeled DNA oligomer competitively bound to an EGFR-specific aptamer. Our results demonstrate that the colloidal AuNPs can function as competitive binding sensors, leading to fluorescence quenching upon fluorophore attachment to the surface of the NPs and partial fluorescence recovery due to EGFR-induced displacement of the fluorophore–aptamer complex. This specificity was confirmed by reversed binding experiments. However, the system proved highly sensitive to the experimental design: excessive washing (centrifugation) led to unspecific aggregation and signal loss, while reduced washing steps improved signal retention and revealed EGFR-induced fluorophore displacement into the supernatant. On the contrary, film-based substrates exhibited strong initial fluorescence, but failed to retain the fluorophore–aptamer complex after washing, resulting in fluorescence decay independent of EGFR incubation. This indicates that AuFoN lacked the binding stability necessary for specific displacement-based sensing. These findings highlight that while colloidal AuNPs can support competitive binding detection, their reproducibility is limited by colloidal stability and protocol sensitivity, whereas AuFoN substrates require improved surface functionalization strategies. The study emphasizes the critical role of surface chemistry, aptamer–fluorophore affinity, and washing protocols in determining the success or failure of plasmon-enhanced aptamer-based biosensing systems and suggests opportunities for improving specificity and robustness in future designs. Full article
(This article belongs to the Special Issue Aptamer-Based Sensing: Designs and Applications)
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23 pages, 4308 KB  
Article
Oligosaccharyltransferase Is Involved in Targeting to ER-Associated Degradation
by Marina Shenkman, Navit Ogen-Shtern, Chaitanya Patel, Haddas Saad, Bella Groisman, Metsada Pasmanik-Chor, Sonya M. Schermann, Roman Körner and Gerardo Z. Lederkremer
Cells 2025, 14(20), 1593; https://doi.org/10.3390/cells14201593 - 14 Oct 2025
Viewed by 283
Abstract
Most membrane and secretory proteins undergo N-glycosylation, catalyzed by oligosaccharyltransferase (OST), a membrane-bound complex in the endoplasmic reticulum (ER). Proteins failing quality control are degraded via ER-associated degradation (ERAD), involving retrotranslocation to cytosolic proteasomes, or relegated to ER subdomains and eliminated via ER-phagy. [...] Read more.
Most membrane and secretory proteins undergo N-glycosylation, catalyzed by oligosaccharyltransferase (OST), a membrane-bound complex in the endoplasmic reticulum (ER). Proteins failing quality control are degraded via ER-associated degradation (ERAD), involving retrotranslocation to cytosolic proteasomes, or relegated to ER subdomains and eliminated via ER-phagy. Using stable isotope labeling by amino acids in cell culture (SILAC) proteomics, we identified OST subunits as differential key interactors with a misfolded ER protein bait upon proteasomal inhibition, suggesting unexpected involvement in ERAD. Previous reports implied additional roles for OST subunits beyond N-glycosylation, such as quality control by ribophorin I. We tested OST engagement in glycoprotein and non-glycosylated protein ERAD; overexpression or partial knockdown of OST subunits interfered with ERAD in conditions that did not affect glycosylation. We studied the effects on model misfolded type I and II membrane-bound proteins, BACE476 and asialoglycoprotein receptor H2a, respectively, and on a soluble luminal misfolded glycoprotein, α1-antitrypsin NHK variant. OST subunits appear to participate in late ERAD stages, interacting with the E3 ligase HRD1 and facilitating retrotranslocation. Molecular dynamics simulations suggest membrane thinning by OST transmembrane domains, possibly assisting retrotranslocation via membrane distortion. Full article
(This article belongs to the Section Intracellular and Plasma Membranes)
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26 pages, 5572 KB  
Article
Targeting GPR55 with Cannabidiol Derivatives: A Molecular Docking Approach Toward Novel Neurotherapeutics
by Catalina Mares, Andra-Maria Paun, Maria Mernea, Cristina Matanie and Speranta Avram
Processes 2025, 13(10), 3261; https://doi.org/10.3390/pr13103261 - 13 Oct 2025
Viewed by 264
Abstract
This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives [...] Read more.
This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives complied with Lipinski′s Rule of Five, exhibiting favorable physicochemical properties with molecular weights below 500 Da and acceptable logP values. Molecular docking simulations, performed using AutoDock Vina through PyRx, revealed strong binding affinities, with docking scores ranging from −9.2 to −7.2 kcal/mol, indicating thermodynamically feasible interactions. Visualization and interaction analysis identified a conserved binding pocket involving key residues, including TYR101, PHE102, TYR106, ILE156, PHE169, MET172, TRP177, PRO184, LEU185, LEU270 and MET274. Ligand clustering in this region further supports the presence of a structurally defined binding site. Molecular dynamics simulations of GPR55 in complex with the three top-scoring ligands (3″-HOCBD, THC, and CBL) revealed that all ligands remained stably bound within the cavity over 100 ns, with ligand-specific rearrangements. Predicted oral bioavailability was moderate (0.55), consistent with the need for optimized formulations to enhance systemic absorption. These findings suggest that CBD derivatives may act as potential modulators of GPR55, offering a basis for the development of novel CNS-targeted therapeutics. Full article
(This article belongs to the Section Biological Processes and Systems)
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38 pages, 5488 KB  
Article
Data-Driven Spatial Zoning and Differential Pricing for Large Commercial Complex Parking
by Yuwei Yang, Honggang Zhang, Jun Chen and Jiao Ye
Mathematics 2025, 13(20), 3267; https://doi.org/10.3390/math13203267 - 13 Oct 2025
Viewed by 202
Abstract
This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data [...] Read more.
This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data to capture heterogeneous user preferences across trip purposes. A dual clustering algorithm is then applied to generate spatially coherent pricing zones, integrating geometric, functional, and occupancy-based attributes. Two differential pricing strategies are formulated: an administered model with regulatory price bounds and a market-based model without such constraints. Both pricing models are solved using an improved multi-objective Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) algorithm that jointly optimizes spatial zoning and zone–time pricing schedules. Using data from the Kingmo Complex in Nanjing, China, the results show that both strategies significantly reduce spatio-temporal occupancy variance and improve utilization balance. The administered strategy reduces variance by up to 67% on weekdays, with only a 1% increase in revenue, making it suitable for contexts prioritizing regulatory compliance and price stability. In contrast, the market-based strategy reduces variance by over 40% while generating substantially higher revenue, particularly during periods of high and uneven demand. The proposed framework demonstrates the potential of integrating behavioral modeling, spatial clustering, and multi-objective optimization to improve parking efficiency. The findings provide practical guidance for operators and policymakers seeking to implement adaptive pricing strategies in large-scale parking facilities. Full article
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24 pages, 7771 KB  
Article
Cross-Domain OTFS Detection via Delay–Doppler Decoupling: Reduced-Complexity Design and Performance Analysis
by Mengmeng Liu, Shuangyang Li, Baoming Bai and Giuseppe Caire
Entropy 2025, 27(10), 1062; https://doi.org/10.3390/e27101062 - 13 Oct 2025
Viewed by 131
Abstract
In this paper, a reduced-complexity cross-domain iterative detection for orthogonal time frequency space (OTFS) modulation is proposed that exploits channel properties in both time and delay–Doppler domains. Specifically, we first show that in the time-domain effective channel, the path delay only introduces interference [...] Read more.
In this paper, a reduced-complexity cross-domain iterative detection for orthogonal time frequency space (OTFS) modulation is proposed that exploits channel properties in both time and delay–Doppler domains. Specifically, we first show that in the time-domain effective channel, the path delay only introduces interference among samples in adjacent time slots, while the Doppler becomes a phase term that does not affect the channel sparsity. This investigation indicates that the effects of delay and Doppler can be decoupled and treated separately. This “band-limited” matrix structure further motivates us to apply a reduced-size linear minimum mean square error (LMMSE) filter to eliminate the effect of delay in the time domain, while exploiting the cross-domain iteration for minimizing the effect of Doppler by noticing that the time and Doppler are a Fourier dual pair. Furthermore, we apply eigenvalue decomposition to the reduced-size LMMSE estimator, which makes the computational complexity independent of the number of cross-domain iterations, thus significantly reducing the computational complexity. The bias evolution and variance evolution are derived to evaluate the average MSE performance of the proposed scheme, which shows that the proposed estimators suffer from only negligible estimation bias in both time and DD domains. Particularly, the state (MSE) evolution is compared with bounds to verify the effectiveness of the proposed scheme. Simulation results demonstrate that the proposed scheme achieves almost the same error performance as the optimal detection, but only requires a reduced complexity. Full article
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20 pages, 49845 KB  
Article
DDF-YOLO: A Small Target Detection Model Using Multi-Scale Dynamic Feature Fusion for UAV Aerial Photography
by Ziang Ma, Chao Wang, Chuanzhi Chen, Jinbao Chen and Guang Zheng
Aerospace 2025, 12(10), 920; https://doi.org/10.3390/aerospace12100920 - 13 Oct 2025
Viewed by 328
Abstract
Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes [...] Read more.
Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes an enhanced small target detection model, DDF-YOLO, which achieves higher detection performance. First, a dynamic feature extraction module (C2f-DCNv4) employs deformable convolutions to effectively capture features from irregularly shaped objects. In addition, a dynamic upsampling module (DySample) optimizes multi-scale feature fusion by combining shallow spatial details with deep semantic features, preserving critical low-level information while enhancing generalization across scales. Finally, to balance rapid convergence with precise localization, an adaptive Focaler-ECIoU loss function dynamically adjusts training weights based on sample quality during bounding box regression. Extensive experiments on VisDrone2019 and UAVDT benchmarks demonstrate DDF-YOLO’s superiority. Compared to YOLOv8n, our model achieves gains of 8.6% and 4.8% in mAP50, along with improvements of 5.0% and 3.3% in mAP50-95, respectively. Furthermore, it exhibits superior efficiency, requiring only 7.3 GFLOPs and attaining an inference speed of 179 FPS. These results validate the model’s robustness for UAV-based detection, particularly in small-object scenarios. Full article
(This article belongs to the Section Aeronautics)
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30 pages, 754 KB  
Article
Quantum Simulation of Variable-Speed Multidimensional Wave Equations via Clifford-Assisted Pauli Decomposition
by Boris Arseniev and Igor Zacharov
Quantum Rep. 2025, 7(4), 47; https://doi.org/10.3390/quantum7040047 - 13 Oct 2025
Viewed by 201
Abstract
The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the [...] Read more.
The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the high gate-level complexity of implementing those oracles. We present a framework for constructing a quantum algorithm for the multidimensional wave equation with a variable speed profile. The core of our method is a decomposition of the system Hamiltonian into sets of mutually commuting Pauli strings, paired with a dedicated diagonalization procedure that uses Clifford gates to minimize simulation cost. Within this framework, we derive explicit bounds on the number of quantum gates required for Trotter–Suzuki-based simulation. Our analysis reveals significant computational savings for structured block-model speed profiles compared to general cases. Numerical experiments in three dimensions confirm the practical viability and performance of our approach. Beyond providing a concrete, gate-level algorithm for an important class of wave problems, the techniques introduced here for Hamiltonian decomposition and diagonalization enrich the general toolbox of quantum simulation. Full article
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24 pages, 1550 KB  
Article
Tester-Guided Graph Learning with End-to-End Detection Certificates for Triangle-Based Anomalies
by Manuel J. C. S. Reis
Big Data Cogn. Comput. 2025, 9(10), 257; https://doi.org/10.3390/bdcc9100257 - 12 Oct 2025
Viewed by 156
Abstract
We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate (δ+α). The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound [...] Read more.
We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate (δ+α). The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound (δ) via Bernstein’s inequality, with (ii) a lightweight classifier over structural features whose validation error contributes (α). The overall certificate is given by the sum (δ+α), quantifying the probability of missed anomalies under bounded sampling. On synthetic communication graphs with n = 1000, edge probability p = 0.01, and anomalous subgraph size k = 120, PT-GNN achieves perfect detection performance (AUC = 1.0, F1 = 1.0) across all tested regimes. Moreover, the miss-probability certificate tightens systematically as the tester budget m increases (e.g., for ε = 0.06, enlarging m from 2000 to 8000 reduces (δ+α) from ≈0.87 to ≈0.49). These results demonstrate that PT-GNN effectively couples graph learning with property testing, offering both strong empirical detection and formally verifiable guarantees in anomaly detection tasks. Full article
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24 pages, 3369 KB  
Article
The Effects of Heparin Binding and Arg596 Mutations on the Conformation of Thrombin–Antithrombin Michaelis Complex, Revealed by Enhanced Sampling Molecular Dynamics Simulations
by Gábor Balogh and Zsuzsanna Bereczky
Int. J. Mol. Sci. 2025, 26(20), 9901; https://doi.org/10.3390/ijms26209901 - 11 Oct 2025
Viewed by 197
Abstract
The inactivation of thrombin by antithrombin is highly enhanced by the presence of heparin chains forming “bridges” between the two proteins. X-ray structures for such ternary complexes have been published, but the molecular background of the lower efficiency of smaller heparinoids on thrombin [...] Read more.
The inactivation of thrombin by antithrombin is highly enhanced by the presence of heparin chains forming “bridges” between the two proteins. X-ray structures for such ternary complexes have been published, but the molecular background of the lower efficiency of smaller heparinoids on thrombin inhibition remains poorly understood. Antithrombin-resistant prothrombin mutants (mutations affecting Arg596 in prothrombin) have been reported that cause severe thrombophilia. Our aim was to study the interactions in the antithrombin–thrombin Michaelis complex both in the presence and the absence of a heparinoid chain and in the presence of pentasaccharide by using molecular dynamics. We also intended to study the complexes of thrombin mutants as well as a known alternative antithrombin conformation at the “hinge” region built using docking. The binding between the proteins was investigated by Gaussian Accelerated Molecular Dynamics (GaMD). We compared the contribution of several amino acids at the binding “exosites” between AT and the wild type and mutant thrombins and between systems containing or not containing a heparinoid. In the docking-based simulations, several of the analyzed amino acid pairs no longer contributed to the interaction, suggesting that the open “hinge” conformation has limited biological relevance. We could identify multiple conformational types using clustering, revealing high flexibility in mutants and systems without heparinoid, probably indicating lower stability. We were also able to detect the allosteric effects of the ligands on the bound thrombin. In summary, we were able to obtain conformations using GaMD that can explain the better protein–protein interactions in the ternary complexes and the impaired AT binding of the thrombin Arg596 mutants at an atomic level. Full article
(This article belongs to the Special Issue Coagulation Factors and Natural Anticoagulants in Health and Disease)
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