Search Results (150)

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

The interindividual phenotypic heterogeneity in Alpha-1 Antitrypsin Deficiency (AATD), despite a shared genetic etiology (the Z-allele of SERPINA1), is explained by the interaction of dual pathogenic mechanisms (gain-of-function vs. loss-of-function), additional genetic modifiers, and environmental or metabolic factors. Building on recent evidence suggesting divergent disease trajectories, we investigated whether pulmonary and hepatic impairments represent coupled manifestations or independent clinical dimensions within a large European cohort. Methods: This international multicenter study utilized the European Alpha-1 Research Collaboration (EARCO) registry (n = 1217). Pulmonary and hepatic severities were quantified using concurrent 0.0–10.0 composite indices. Independence was evaluated via partial Spearman correlations, multivariable multinomial regression, and geometric mapping across a continuous phenotypic space. Results: Cross-domain correlations between respiratory metrics and liver stiffness were near zero (r = −0.03), demonstrating statistical independence. Phenotypic dominance classification isolated distinct profiles; the lung-dominant group exhibited a higher age (57.0 vs. 54.0 years; p < 0.001) and tobacco exposure, while the liver-dominant group registered a higher body mass index (25.8 vs. 24.4 kg/m²; p < 0.001). Multivariable models identified age (OR 1.03; 95% CI 1.02–1.05) and smoking as independent predictors of lung dominance, whereas body mass index was independently associated with liver dominance (OR 1.04; 95% CI 1.01–1.07). Geometric mapping revealed advanced disease clusters at orthogonal margins rather than forming a systemic continuum. Conclusions: Hepatic and pulmonary impairments in AATD operate as independent clinical dimensions modulated by distinct metabolic and environmental factors. Risk stratification must transition toward organ-specific prognostic models. Full article

Public health emergencies require spatial crowdsourcing platforms to finish urgent tasks while limiting unnecessary movement across regions. Most online task assignment studies focus on profit, travel distance, latency, task coverage, or service quality. However, isolation sensitive scenarios need a different assignment goal. In such scenarios, regional crossings should be directly controlled during worker–task matching. This paper studies an isolation sensitive online task assignment problem in spatial crowdsourcing. The service space is modeled as a regional adjacency graph. The matching objective combines cross-region movement cost, an urgency reward for delayed task completion, and a dummy no-assignment cost for carry-over decisions. To handle dynamic arrivals, a time-sliced online process is used. Unfinished tasks are carried over to later time slots, and the priority of each carried-over task increases with waiting time. Based on this framework, we design two algorithms. OnlineKM serves as the basic priority-aware online matching algorithm. OnlineKM builds a matching problem in each time slot and applies KM-based partial matching with the information currently available. OnlineARC further uses δ-balanced adaptive regional coarsening. OnlineARC merges adjacent regions according to recent supply–demand balance before matching. This step adjusts the regional granularity used for movement cost evaluation and helps keep assignments close to local regions when regional merging is suitable. Experiments are conducted using a real-world task locations dataset from a 2022 COVID-19-related scenario in Changchun, with simulated worker availability and online arrivals. The results show that the proposed methods usually reduce the combined assignment objective value under the tested settings. The service quality and movement control metrics show that OnlineARC reduces the cross-region assignment ratio and average hop distance while maintaining a high task completion rate under the representative setting. OnlineKM improves running efficiency through time-sliced matching, while OnlineARC provides a trade-off between adaptive coarsening cost and locality-aware movement cost evaluation. These results suggest that adaptive regional coarsening can serve as a practical heuristic for locality-aware online task assignment in isolation sensitive spatial crowdsourcing under suitable worker–task distributions. Full article

Urban green spaces provide essential ecosystem services, yet mismatches between subjective perceptions and objective assessments may constrain effective planning. This study examines the correspondence between perceived and measured ecosystem services across two contrasting urban green spaces in Shanghai: Century Park, a managed urban park, and Sanlin Green Space, a naturalistic urban forest. Objective ecosystem services (regulating, supporting, and cultural) were quantified using UAV-based biotope mapping and indicators including biophysical metrics (Net Primary Production, Water Retention, PM10 removal, and Land Surface Temperature), structural diversity indices (Shannon Diversity of land cover, vegetation, and tree structure), and visual–spatial proxies (Green View Index, Sky View Index, Water View Index, color metrics, and spatial openness). Subjective perceptions were derived from panoramic image-based questionnaires, with perception scores predicted using XGBoost and aggregated via SHapley Additive exPlanations (SHAP). Correlation analyses, spatial regression models, and partial least squares structural equation modeling were applied to explore relationships and pathways. Results show weak but significant positive associations in the urban park, whereas no overall correspondence was observed in the urban forest. Spatial mismatches were concentrated in biotopes with distinctive visual–ecological features and in fragmented areas. Green View Index is associated with higher perceptions in both sites, while the Sky View Index reduced perception in the forest context. These findings highlight strong context dependence in perceived–measured ecosystem service relationships and underscore the importance of integrating ecological structure and visual legibility in the design and management of the studied urban green spaces in Shanghai. Full article

Binding affinity prediction in computational drug discovery is confounded by trivial correlations between molecular size and measured potency. We introduce cellular sheaf Laplacians as descriptors of ligand molecular geometry that quantify geometric frustration independent of system size. Sheaves are constructed over molecular graphs by assigning three-dimensional coordinate spaces to atoms and projection operators encoding ideal bonding geometry to edges; eigendecomposition of the resulting Laplacian yields spectral features measuring inconsistencies between local geometric constraints and global topology. Applied to 14,050 protein-ligand complexes from the PDBbind v2020 refined set, MW-residualized Sheaf features capture a statistically significant geometric signal ($r_{\mathrm{partial}} = 0.171$, $p<{10}^{-70}$) that is orthogonal to the Wiener index ($r=0.013$) and persists after controlling for both molecular weight and classical graph-theoretic descriptors ($r_{\mathrm{partial}} = 0.390$, $p<{10}^{-9}$). Sheaf spectral features alone achieve predictive performance ($R^2=0.403$) approaching that of fourteen classical cheminformatics descriptors ($R^2=0.446$), and their combination yields consistent improvements across the binding affinity spectrum (RMSE $=1.43$$p{K}_d$). Permutation importance analysis confirms the Sheaf Frobenius norm as the second most influential descriptor after molecular weight. We introduce Topological Binding Efficiency as a size-normalized quality metric identifying ligands that achieve potent binding through geometric complementarity rather than molecular bulk. Gaussian mixture analysis of the maximum eigenvalue distribution among strong binders reveals two distinct spectral modes corresponding to planar aromatic and three-dimensional sp3-rich scaffolds, confirmed by significant differences in fraction of sp3 carbons and aromatic ring counts ($p<{10}^{-8}$). As an intentionally ligand-centric framework, our approach complements rather than replaces protein-aware co-modelling architectures. This work establishes cellular sheaf theory as a principled framework for encoding molecular topology with statistically significant associations with binding affinity, providing interpretable geometric insights that are inaccessible to conventional molecular descriptors. Full article

The purpose of this research work is to propose and develop the notion of $\left(\alpha ,\psi \right)$-contractions in the setting of extended $\mathcal{F}$-metric spaces and to establish corresponding fixed point results. Using these results, we derive fixed point results for graphic contractions in extended $\mathcal{F}$-metric spaces as well as for mappings in partially ordered extended $\mathcal{F}$-metric spaces. To demonstrate the validity and novelty of the proposed results, a non-trivial example is provided. Moreover, the constructed framework serves as a tool to investigate the existence of solutions for Caputo fractional differential equations, thereby highlighting both its effectiveness and practical significance. Full article

The importance of sequence spaces has increased with the emergence of various new convergence methods such as statistical convergence. On the other hand, partial metric spaces hold an important place in computer science, data science, and convergence analysis because they contain points whose distance from themselves is non-zero. For these reasons, in the present paper, we generalize the concept of statistical convergence, previously defined for cone metric spaces, to partial cone metric spaces, defining statistical convergence of order $\alpha$ and $\lambda$-statistical convergence of order $\alpha$ and demonstrating their relationships. Furthermore, we define the statistical Cauchy sequences of order $\alpha$ and the $\lambda$-statistical boundedness of order $\alpha$ and examine some inclusion theorems. Additionally, in partial cone metric spaces, we show that a non-convergent sequence is statistically convergent of order $\alpha$. Full article

Seismic collapse can cause catastrophic losses, and acceptable annual collapse probability with its CMR target is a core metric in performance-based design. Existing ATC-63-based CMR research mainly addresses non-damped systems and often uses a single lumped dispersion, obscuring damper-reliability contributions and hindering alignment with CECS 392 limits. This study proposes a unified, code-consistent decision framework for acceptable annual collapse probability and CMR that jointly accounts for seismic hazard and damper-related uncertainty. The total collapse dispersion is decomposed as ${\sigma }_{\mathrm{total},\mathrm{damp}}^2={\sigma }_{\mathrm{base}}^2 +{ \sigma }_{\mathrm{damper}}^2$, where ${\sigma }_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ represents background dispersion independent of dampers and ${\sigma }_{\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}}$ captures incremental uncertainty induced by degradation and partial failure. A code-designed viscous-damped RC frame is evaluated under three scenarios (nominal damping, 20% damping-coefficient reduction, and 7% random damper failures). Using the same 14 records and $\mathrm{S}\mathrm{a}\left({\mathrm{T}}_1,5\%\right)$ as the intensity measure, multi-stripe IDA and Probit-based lognormal fragility fitting yield median collapse intensities $\mathrm{S}\mathrm{c}\approx 2.18-2.24 \mathrm{g}$, with only ~2–3% reduction under mild degradation/failure. A random-effects variance decomposition identifies σ_damper ≈ 0, indicating a limited marginal contribution of damper-related uncertainty within the degradation range considered in this study. Closed-form relationships between annual collapse rate, $\mathrm{S}\mathrm{c}$, and ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ are then derived under a power-law hazard model and inverted to generate acceptable-risk intervals and CMR target curves/matrices. Results show that higher design intensity and larger ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ demand substantially higher CMR, highlighting potential risk underestimation when relying solely on nominal CMR. The framework enables explicit identification of damper-related uncertainty from limited collapse data and provides a practical workflow for collapse-prevention design and post-assessment under explicitly defined scenario conditions, with a clear pathway for extension to broader scenario spaces. Full article

The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency. However, domain-specific text summarization for pipeline communication maintenance remains challenging due to scarce labeled data and the high computational cost of fine-tuning large pretrained models. Parameter-efficient fine-tuning alleviates this issue, but its effectiveness strongly depends on appropriate hyperparameter selection. This paper proposes a unified framework that combines weight-decomposed low-rank adaptation with an enhanced Artificial Bee Colony algorithm for automated hyperparameter optimization. The enhanced algorithm addresses two specific limitations of the standard Artificial Bee Colony algorithm: uninformed random initialization that ignores promising regions, and premature abandonment of stagnated solutions that discards partially useful search directions. These two components represent principled design choices, each targeting a distinct bottleneck in applying swarm intelligence search to high-dimensional mixed-type hyperparameter spaces. The method introduces a hybrid initialization strategy to exploit prior knowledge and a stagnation-guided local search mechanism to refine stagnated solutions instead of discarding them, achieving a better balance between exploration and exploitation. Experimental results on a public Chinese summarization benchmark and an industrial oil and gas pipeline communication maintenance corpus show that the proposed approach consistently outperforms full fine-tuning, manually tuned parameter-efficient methods, and several evolutionary optimization baselines in terms of ROUGE metrics. The automated search introduces modest additional computational overhead compared to manual tuning while eliminating expert-dependent hyperparameter configuration and achieving consistent performance gains across both datasets. Overall, the proposed framework provides an efficient and robust solution for adapting large language models to specialized summarization tasks in the context of pipeline communication system maintenance. Full article

In this paper, we investigate the properties of the boundedness of fractional integral operators $K_{\alpha }$ defined on general measure metric spaces. We study their action in Lebesgue spaces $L^p\left(Y\right)$, Morrey spaces $L_{\phi }^p\left(Y\right)$, and extend our analysis to fractional Sobolev spaces $W^{\alpha ,p}\left(Y\right)$. Using classical dyadic decomposition and the Hardy–Littlewood maximal operator, we establish sharp bounds for $K_{\alpha }$ in terms of kernel parameters and the geometric structure of the space. A significant contribution of this work is the proof that $K_{\alpha }$ is bounded from $W^{\alpha ,p}\left(Y\right)$ to $L^q\left(Y\right)$, where thus linking our operator-theoretic framework with the theory of nonlocal and fractional partial differential equations. These results provide valuable tools for studying regularity, a priori estimates, and solution mappings in nonlocal problems involving the fractional Laplacian and related operators on irregular or non- Euclidean domains. Full article

Introduction: Acute ischemic stroke demands interventions that restore perfusion and protect neurons within a narrow therapeutic window. We propose a unified theranostic platform that couples adaptive imaging, topology-aware decision-making, and immediate neuroprotective and micro-dosimetric intervention. Methods: The platform integrates three components. First, a topology-preserving MR–PET engine employs adaptive Poisson-disc sampling, partial Fourier constraints, and structured Hankel low-rank priors in a closed loop. Persistent-homology metrics quantify vascular graph uncertainty and guide subsequent k-space and PET projections, reducing acquisition time while preserving collateral topology. Second, immediate post-reperfusion delivery of glycolic acid attenuates glutamate-driven calcium influx and stabilizes mitochondrial function. Third, trace doses of sol–gel-derived, neutron-activated ⁹⁰Y₂O₃ microspheres provide sharply confined beta irradiation for micro-scale metabolic modulation. Results: In a porcine stroke model replicating the human recanalization workflow, the imaging engine maintained vascular Betti-number invariants within three percent of fully sampled reference scans while reducing acquisition time by nearly half. Glycolic acid reduced glutamate-induced intracellular calcium rise by approximately sixty percent in vitro and decreased infarct volume by thirty-eight percent in vivo. Micro-dosimetry confirmed a mean perivascular beta dose of twenty-eight grays, and histology demonstrated a forty-two percent increase in NeuN-positive neuronal survival compared with standard recanalization. Conclusions: These results demonstrate that intelligent compressed-sensing MR–PET, targeted micro-radioembolization, and glycolic acid neuroprotection can act synergistically to bridge diagnostic imaging and immediate intervention. By coupling imaging, decision-making, and therapy in a closed-loop manner and elevating topological fidelity from a reconstruction byproduct to a control variable, the proposed platform reframes MR–PET from passive diagnostics into an active, decision-driven theranostic system and establishes a foundation for future human trials. Full article

Voltage stability assessment under transmission contingencies is traditionally performed using severity-based indices evaluated on isolated outage scenarios. While effective for identifying extreme events, such approaches provide limited insight into which transmission corridors structurally govern voltage-stress behavior across the full contingency space. This paper introduces a persistence-based diagnostic framework for voltage stability assessment under exhaustive $N-1$ line contingencies, using the Fast Voltage Stability Index (FVSI) as a base indicator. Rather than ranking lines by instantaneous severity, the proposed methodology identifies dominant transmission lines—defined as those attaining the maximum FVSI in each convergent contingency—and aggregates these outcomes statistically to quantify dominance persistence, conditional severity, and dispersion. A dominance concentration metric ($k_{90}$) is introduced to measure how many transmission corridors are sufficient to explain the majority of dominant voltage-stress events. The framework is applied to IEEE 14, 30, and 118-bus benchmark systems under exhaustive $N-1$ enumeration. Results reveal a clear phenomenon of dominance collapse: as system size increases, dominant voltage-stress outcomes concentrate onto an extremely small set of transmission corridors. While IEEE 14 exhibits partial dominance dispersion ($k_{90}=2$), both IEEE 30 and IEEE 118 demonstrate near-total dominance collapse ($k_{90}=1$), where a single corridor governs more than 90% of dominant FVSI events. The proposed approach is fully deterministic, scalable, and independent of control or optimization assumptions, making it well-suited for planning-stage screening, monitoring prioritization, and pre-filtering of large-scale contingency studies. By shifting voltage stability analysis from severity-only screening to persistence-based structural diagnosis, this work provides new insight into vulnerability concentration in modern transmission networks. Full article

The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates the aerodynamic characteristics of a quadrotor drone with a vertically offset rear-rotor configuration through computational fluid dynamics (CFD) simulations. By varying the vertical spacing ratio between the front and rear rotors (H/R), quantitative analyses were conducted of key performance metrics, including rotor thrust and power loading, with explanations provided from the perspective of the flow field structure. Furthermore, the underlying physical mechanisms influencing the observed performance variations are explored. The results indicate that, under the operating conditions investigated in this study, which include a single rotor RPM, a 10° inflow angle, and a specific forward-flight speed, the vertically offset configuration demonstrates superior aerodynamic performance at H/R = 1. At this spacing ratio, the rear rotor disk avoids most of the downwash-induced velocity generated by the front rotor, allowing partial recovery of the effective angle of attack. Consequently, vortex-propeller interaction (PVI) is significantly weakened, turbulent kinetic energy (TKE) levels in the interference region are reduced, and premature flow separation on the rear rotor blades is suppressed. These combined effects enhance overall aerodynamic efficiency. This study clarifies the role of vertical rotor spacing in regulating aerodynamic interference in multi-rotor drones, offering valuable insights for the aerodynamic design of compact rotorcraft. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

In this paper, a two-phase spatio-temporal point process (STPP) defined on a countable metric space and characterized by a conditional intensity function is introduced. In the first phase, the process is memoryless, generating completely random point patterns. In the second phase, the location and occurrence time of each event depend on the spatial configuration of previous events, thereby inducing spatio-temporal correlation. Theoretical results that characterize the distributional properties of the process are established, enabling both efficient numerical simulation and Bayesian inference. A statistical inference framework is developed, for the setting in which the STPP is observed at discrete calendar dates while the spatial locations of events are recorded, their exact occurrence times are unobserved, i.e., interval-censored. This partial observation scheme commonly arises in ecological and epidemiological applications, such as the monitoring of plant disease or insect pest spread across a spatial grid over time. The methodology is illustrated through an analysis of the spatio-temporal spread of sugarcane yellow leaf virus (SCYLV) in an initially disease-free sugarcane plot in Guadeloupe, FrenchWest Indies. Full article