Search Results (3,037)

Artificial intelligence is reshaping employment in smart cities, yet centralized hiring platforms can deepen exclusion for persons with disabilities through privacy risk, biased models, weak multilingual support, and limited accommodation awareness. Because disability-related records are highly sensitive, no single institution holds enough representative data to train fair models, and centralizing such data is rarely permissible across borders. We propose FedAgent-Chain, a framework that integrates federated learning, blockchain-based auditability, multilingual processing, rule-based agentic services, and human-in-the-loop governance, extended with an education-to-employment module that builds individualized, accessible job-readiness pathways. Institutions across Saudi Arabia, the United States, China, and Europe train shared models without exchanging raw data. In a prototype evaluation on synthetic records over five seeds, the framework reached a mean F1 of 0.7207 (95% CI: [0.6506, 0.7909]), comparable to a centralized logistic-regression baseline while preserving data locality, with a formal $(\epsilon =3.2,\delta ={10}^{-5})$ differential-privacy guarantee after 20 rounds. Multi-dimensional fairness regularization lowered disability-category and work-mode disparity by 32.3% and 40.3% relative to local-only training. We report the fairness behavior transparently, including a case where the penalty does not outperform standard FedAvg on disability-category disparity, and we position cross-institutional integration with accountable governance, rather than raw metric superiority, as the central contribution. Full article

We present state-of-the-art kinetic theory determination of the neutron abundance available for the Big-Bang nucleosynthesis (BBN). This paper is motivated by the study of the neutron lifespan measured in the laboratory and the unknown strength of weak interactions coupling constant $G_{\mathrm{F}}$ at finite temperature in the primordial Universe. We draw attention to the relevant dependence of $G_{\mathrm{F}}$ on the symmetry breaking Weinberg angle $s_{\mathrm{W}}^2$, which is a free parameter in the standard model of particle physics. We establish how the value of $s_{\mathrm{W}}^2$ by way of $G_{\mathrm{F}}$ modification influences the neutron abundance available for BBN and neutron lifetime. Full article

Background/Objectives: To investigate the immediate regulatory effects of magnetic stimulation with different parameters on the excitability of glutamatergic neurons and GABAergic neurons in the mouse hippocampal dentate gyrus (DG), and to analyze the underlying mechanisms using the Hodgkin–Huxley (HH) model. Methods: Whole-cell patch-clamp recordings were performed on acute brain slices to measure changes in resting membrane potential (RMP), the number of action potentials (APs) evoked by 500-ms long-duration stimulation, as well as AP threshold, peak, half-width, maximum rising slope, and maximum falling slope under magnetic stimulation at various frequencies (1, 10, 20 Hz) and intensities (50, 75 mT). An improved HH model was established based on experimental data to analyze the dynamic changes in gating variables under magnetic stimulation. Results: High-frequency magnetic stimulation (10–20 Hz) significantly increased the number of APs in both neuron types. In glutamatergic neurons, the number of APs increased from 10.12 ± 0.52 in the control group to 15.62 ± 0.84 in the 20 Hz-75 mT group; in GABAergic neurons, it increased from 7.88 ± 0.40 to 12.62 ± 0.53. Magnetic stimulation also depolarized RMP and significantly altered multiple AP waveform parameters in both neuron types. Glutamatergic neurons showed a more distinct frequency dependence, whereas GABAergic neurons were more sensitive to changes in both frequency and intensity in terms of RMP and multiple waveform parameters. Simulation results showed that the 1 Hz conditions produced negligible changes in AP firing, gating-variable dynamics, and steady-state ion-channel parameters compared with the Control condition. In contrast, high-frequency stimulation enhanced the dynamic changes of sodium and potassium channel gating variables and altered their voltage-dependent steady-state properties. Specifically, sodium channel activation shifted toward more negative potentials, whereas sodium channel inactivation and potassium channel activation shifted toward more depolarized potentials. Conclusions: Under the experimental conditions of this study, magnetic stimulation immediately enhanced the excitability of glutamatergic and GABAergic neurons in the hippocampal dentate gyrus of male mice in a frequency-dependent manner. The modified HH model reproduced both the weak effects under low-frequency stimulation and the enhanced excitability under high-frequency stimulation, suggesting that these immediate effects may be related to frequency-dependent changes in the gating kinetics and voltage-dependent properties of sodium and potassium channels. Full article

Flood-induced scour remains one of the leading causes of bridge failure, yet the chain linking flood hazard to bridge decisions is still commonly treated as a sequence of disconnected tasks. This review examines that chain using uncertainty as a unifying interpretive framework, synthesizing the recent literature on non-stationary flood hazard assessment, bridge-scale hydraulics, scour processes and predictive models, scour monitoring, monitoring-informed forecasting, structural vulnerability, and risk-informed decision-making. The review synthesizes the state of the art across all these stages of the chain, highlighting how the dominant uncertainty changes along it: climate and hydrologic variability upstream; model-form, sediment, and parameter uncertainty in scour prediction; measurement noise and inverse-inference uncertainty in monitoring; and threshold and consequence uncertainty in closure, retrofit, and network-level decisions. Although major advances have been achieved in probabilistic modelling, machine learning, hybrid physics-informed methods, and multimodal sensing, most published frameworks still transfer deterministic outputs from one stage to the next. As a result, uncertainty is rarely propagated consistently to the decision level. The main value of this review lies in making the chain’s weak interfaces explicit, in showing how uncertainty propagation can serve as a unifying framework across otherwise disconnected literatures, and in identifying which methodological directions are most promising for connecting prediction, monitoring, and decision support into a coherent end-to-end probabilistic chain supporting climate-resilient bridge management. Full article

Ionization potentials and electron affinities provide the energetic basis for several conceptual density functional theory descriptors, but their use in donor–acceptor maps requires careful distinction between physically bound anions, weak or borderline electron-affinity cases, and formally computed diagnostic states. In this work, a periodic donor–acceptor descriptor map was constructed for main-group atoms from H to Kr using a $\Delta$SCF-DFT framework. Neutral atoms, monocations, and formally defined monoanionic states were evaluated to obtain ionization potentials, electron affinities, and global reactivity descriptors, including electronegativity, chemical hardness, chemical potential, electrophilicity, electrodonating power, and electroaccepting power. The production dataset was calculated at the $\omega$B97X-D4/def2-QZVPPD level and benchmarked against reference atomic data. This protocol reproduced ionization potentials with a mean absolute error of 0.134 eV and electron affinities with a mean absolute error of 0.116 eV for the reference EA set, including the weak calcium case. A functional and basis-set sensitivity analysis using $\omega$B97X-D4/def2-TZVPPD, PBE0/def2-QZVPPD, and PBE0/def2-TZVPPD showed that ionization potentials are comparatively robust, whereas electron affinities are strongly affected by the quality of the diffuse basis set. The normalized donor–acceptor map reproduces chemically intuitive periodic trends, with alkali metals occupying the strong-donor region and halogens defining the strong-acceptor region. The analysis explicitly separates core validation atoms from weak or borderline electron-affinity cases and diagnostic finite-basis anionic states, emphasizing that formally computed negative electron affinities for unbound anions should not be interpreted as physical bound states. The resulting nonrelativistic dataset provides a reproducible atomic descriptor reference for interpreting donor–acceptor behavior in atoms, clusters, superatoms, doped materials, and charge-transfer systems. Full article

Accurate identification of drilling operation states is essential for improving drilling efficiency and operational safety. However, existing methods often suffer from limited temporal feature extraction capability, weak cross-well generalization, and insufficient model interpretability. To address these issues, this study proposes a drilling-state recognition framework based on MultiHead-BiLSTM and low-rank adaptation (LoRA) transfer learning. The MultiHead-BiLSTM model combines multi-head attention with bidirectional long short-term memory to capture both critical temporal segments and global sequential dependencies in drilling time series data. To improve cross-well adaptability while reducing training computational cost, a parameter-efficient LoRA fine-tuning strategy is introduced within the transfer learning framework. In addition, SHAP-based feature attribution and attention visualization are employed to enhance model interpretability. Experimental results show that the proposed method achieves an accuracy of 95.11% and an F1-score of 94.00%, outperforming LSTM, GRU, BiLSTM, and Transformer baselines. The LoRA-based transfer strategy reduces the cross-well error rate to 1.91%, compared with 8.79% for direct transfer and 4.48–5.39% for partial-layer freezing methods. Interpretability analysis qualitatively suggests that bit depth, weight on bit, and block position contribute strongly to drilling-state discrimination, while attention visualization qualitatively suggests that the model tends to focus on operational transition periods. The proposed framework provides an effective and computationally efficient solution for intelligent drilling-state recognition and cross-well deployment. Full article

Renewable energy systems dominated by powered electronic devices generally exhibit weak disturbance tolerance and limited grid-support capability. Following the blocking of a flexible DC transmission system, emergency load shedding in renewable-rich grid regions may induce overvoltage or undervoltage at the point of common coupling, forcing renewable energy units into a voltage ride-through state. This, in turn, reduces their active power output and threatens the frequency stability of the power system. To address this issue, this paper proposes an emergency load-shedding decision model based on a constrained Markov decision process (CMDP). First, an emergency frequency control model for AC–DC hybrid power systems is established within the Markov decision process framework, thereby formulating power system frequency stability control as a Markov decision problem. Second, Lagrange multipliers are introduced into the CMDP framework to transform the constrained optimization problem with security constraints into an unconstrained objective optimization problem. Finally, the Proximal Policy Optimization (PPO) algorithm is adopted to accelerate the training process and improve the decision accuracy of the intelligent agent. The simulation results, based on the modified IEEE 39-bus system, demonstrate that, compared with the traditional contingency strategy and the conventional Markov decision algorithm, the proposed load-shedding strategy can satisfy system frequency stability requirements, effectively avoid voltage violations at renewable energy grid-connection points, and minimize the total load shedding amount. Full article

We develop a two-age smoking-dynamics model for youth and adult groups that incorporates education acquisition, aging, cessation and relapse, disease progression, age-dependent social mixing, and a weak Allee effect in smoking initiation. Education is modeled as a protective status acquired through schooling and aging transitions, while initiation depends on both education status and prevalence-dependent social reinforcement. We establish the well-posedness of the system, derive the smoking-free equilibrium in closed form, and obtain the compact age-structured threshold ${\mathcal{R}}_0^{\mathrm{age}}=\rho \left(\mathrm{diag}(g_Y,g_A)C\right)$, where C is the age-mixing matrix and $g_a$ summarizes the within-age smoking-invasion potential. Using center-manifold analysis, we derive conditions under which Allee-type social reinforcement can generate a backward bifurcation, implying that reducing ${\mathcal{R}}_0^{\mathrm{age}}$ below one may not always be sufficient for elimination when endemic prevalence is high. We also analyze the impact of cross-age mixing on the threshold and use a quasi-steady-state approximation to characterize the quitting–relapse loop while preserving the threshold structure. Numerical simulations illustrate baseline youth and adult prevalence trends, identify youth initiation, relapse, cessation, and education protection as dominant drivers of threshold sensitivity, and show that education-based interventions are most effective when they directly reduce the susceptibility of educated youths to smoking initiation. Full article

Robust unmanned aerial vehicle (UAV) detection in real low-altitude anti-UAV scenarios remains challenging due to motion blur, extreme illumination, cluttered backgrounds, and tiny target sizes. Most existing UAV detectors rely on RGB imagery, but their performance often degrades severely under these adverse conditions. Event cameras, as a neuromorphic sensing modality, capture motion-sensitive responses with high temporal resolution and thus provide complementary cues for robust UAV detection. However, existing RGB–event fusion detectors usually employ homogeneous feature extraction and generic fusion mechanisms, which are insufficient to handle heterogeneous modality degradation and exploit reliable cross-modal cues. To address this limitation, we propose SMG-UAV, a sparse mutual guided RGB–event fusion network for robust small-UAV detection. The proposed method integrates a hybrid dual-branch backbone for modality-specific representation learning, a Sparse Mutual Guided Bridge for bidirectional sparse cross-modal refinement, and a Selective Gated Pyramid Neck for multiscale enhancement of weak UAV responses. Experiments on the Florence RGB-Event Drone Dataset (FRED) and the Neuromorphic-RGB Drone Detection Dataset (NeRDD) demonstrate that SMG-UAV achieves state-of-the-art performance, outperforming the strongest competing method by an average of 5.2 points in ${\mathrm{AP}}_{50}$, while delivering stronger robustness under multiple challenging anti-UAV conditions. Full article

Climate warming, pavement heat storage and lateral heat intrusion accelerate active-layer deepening and uneven thaw settlement along permafrost transportation corridors. In wide asphalt embankments, heat is stored across a broad pavement-embankment section, while slope-aspect solar input drives asymmetric thermal erosion toward the sunny-side toe. Existing embankments protected by two-phase closed thermosyphons (TPCTs) are commonly evaluated by temperature reduction, maximum thaw depth or local cooling efficiency, but these metrics do not describe frozen-state continuity or residual weak zones. This study develops a three-dimensional hydrothermal model to compare a no-TPCT reference embankment, a conventional inclined TPCT layout and a base-arranged novel horizontal TPCT layout under long-term regional warming. Without TPCTs, the year-20 thaw depth reached 10.06 m at the sunny-side shoulder and 9.76 m beneath the centerline, with thermal disturbance propagating toward the sunny-side toe. Both TPCT layouts stabilized the 0 °C isotherm beneath the embankment. The inclined layout generated deep localized cooling, whereas the horizontal layout formed a more continuous shallow frozen zone, with longer operating durations and year-20 annual cumulative cooling capacities of 1870 and 1600 MJ on the sunny and shaded sides, respectively. The findings support an assessment based on frozen-state continuity, cross-sectional temperature uniformity and residual weak-zone development. Base-arranged novel horizontal TPCTs are better suited to shallow continuity, whereas inclined TPCTs remain useful for deep localized cooling. Full article

Aiming at the problems of the severe active–reactive power coupling, insufficient adaptive inertia–damping regulation, and degraded dynamic performance of virtual synchronous generators (VSGs) under the operating conditions of a weak grid, high resistance-to-reactance ratio, and large power angle, this paper proposes a cooperative control strategy that combines reactive power feedforward decoupling with adaptive damping–inertia regulation. First, a small-signal power model of the VSG is established, and a dynamic relative gain array is employed to quantitatively analyze the effects of the resistance-to-reactance ratio and power angle on power coupling characteristics, revealing that large power angles and high resistance-to-reactance ratios significantly aggravate active–reactive power coupling. Based on this analysis, a reactive-power-oriented feedforward decoupling strategy is designed to suppress the cross-coupling between reactive power and power angle while preserving the intrinsic inertia support characteristics of the active power loop. Eigenvalue migration analysis further demonstrates that the proposed reactive-power-oriented decoupling provides higher damping ratios and larger stability margins than conventional full active–reactive power decoupling. Furthermore, a deep deterministic policy gradient-based adaptive damping–inertia control method is developed by incorporating frequency deviation, power fluctuation, voltage deviation, and coupling degree into the state space, enabling the online coordinated optimization of virtual inertia and damping coefficients. The hardware-in-the-loop experimental results verify that the proposed strategy effectively suppresses active–reactive power coupling, reduces power overshoot and oscillation, enhances frequency support capability and dynamic response speed, and maintains superior stability under weak grid conditions. Sensitivity analysis under grid impedance estimation errors further confirms its strong robustness against parameter uncertainty, while tests under composite disturbance scenarios demonstrate excellent transient performance. The proposed strategy provides an effective solution for improving the grid-connected operation performance and adaptability of VSGs in low-inertia power systems. Full article

Two-dimensional (2D) human pose estimation is one of the key research directions in Computer Vision (CV), which has wide application prospects in behavior recognition, such as gesture tracking, intelligent monitoring, and identity recognition. Therefore, it has recently attracted extensive attention from academia and industry. However, although a large amount of literature has been published, existing reviews often lack a unified theoretical perspective and fail to capture the latest paradigm shifts brought by foundation models. To this end, this paper reviews the applications of deep learning in the domain of 2D body pose estimation from 2010 to 2025 through a cascading approach. First, the mainstream body pose datasets and related evaluation metrics are introduced in a comprehensive and convincing way through mathematical formulas. Subsequently, an in-depth analysis of the performance of the algorithms in single-person and multi-person scenarios, and a comprehensive comparative analysis of the strengths and weaknesses of each algorithmic model, are conducted. A comprehensive comparative analysis encompassing both traditional architectures and the latest deep learning breakthroughs are provided, specifically highlighting Vision Foundation Models (VFMs), generative Diffusion processes, and State Space Models (SSMs). Finally, the current state of research in the field of 2D human pose estimation is summarized, and three main challenges, emerging solutions, and expected development trends are pointed out. This survey is an exhaustive compilation of existing research in 2D human pose estimation, providing a blueprint for researchers in the field and laying the foundation for future research work. Full article

This paper presents a further step in the development of scale invariant fully conformal cosmology (FCC), formulated in our previous study. Whereas the previous paper focused mainly on the global cosmological consequences of the fully conformal metric and their confrontation with selected astrophysical data, here we analyze its local gravitational and background consequences. On the background of the fully conformal metric we formulate an effective generalization of the weak Schwarzschild field in the corresponding FCC global coordinates and derive from it the associated modified intensity of the Newtonian central field. We further derive the cosmological state/constitutive equation p = − ε/3 as a direct consequence of the fully conformal metric rather than as an ad hoc additional postulate. Likewise, within the fully conformal metric, spatial flatness and the critical density ρ_crit are understood as direct consequences of this metric structure rather than as independently postulated inputs. From the condition of global equilibrium between negative cosmological pressure and the gravitational cohesive pressure of homogeneously distributed matter, the effective particulate fraction is obtained as β ≈ 0.45 of the total critical density ρ_crit. For the relatively well-confirmed baryonic matter fraction ${\overline{\mathrm{\Omega }}}^{bar} \approx 0.05$, this stable-equilibrium condition then leads to the corresponding particulate fraction of collisionless dark matter ${\overline{\mathrm{\Omega }}}_{FCC}^{dm} \approx 0.40$, which is in principle determined by the global cosmological equilibrium within this framework. Because direct identification of the entire dark fraction with standard collisionless cold dark matter would very probably be incompatible with the main structural observables, we discuss an effective phenomenological decomposition into a structuring cold dark matter component (cdm) and an almost homogeneous residual warm-dark-matter-like component (wdm). In this interpretation, the paper preserves the previously introduced global FCC framework while simultaneously providing a concrete background prediction for the matter content and a physically motivated basis for further testing of structure formation within scale invariant fully conformal cosmology. Full article

Porosity-related quantities from industrial X-ray CT depend on segmentation and inference choices. When inference defaults are omitted from the report, void or phase fractions can shift by amounts comparable to slice-to-slice variability. The contribution is metrological rather than architectural: we document a reproducible nnU-Net 2D workflow on Dataset601 CTVoid from semantic labels to slice-wise void fraction, optional two-dimensional connected-component pore summaries, isotropic three-dimensional stacking at 0.058 mm spacing, and spatial axis diagnostics, with region of interest and voxel spacing stated explicitly. The main results pair a weak export policy, defined as a single forward pass per slice without multi-scale fusion or test-time augmentation, with a strong policy that enables multi-scale fusion and flip-based augmentation on the same slice exports and identical weights, on one hundred consecutive slices from one cementitious industrial stack of 1028 × 1028 pixels. In parallel we report trainer validation on eight named Dataset601 validation cases and mirroring-based test-time augmentation off versus on re-inference on those same cases; case identifiers and the cross-validation split appear in the main text. These quantities answer different questions and must not be substituted for one another or for independent full-stack ground truth. Porosity-related scalars from industrial X-ray CT depend on how segmentation and inference are configured; when defaults are omitted, void fractions can shift by amounts comparable to slice-to-slice variability. For fixed nnU-Net weights on one cementitious industrial slice stack (1028 × 1028 pixels), we benchmark weak inference (single forward pass, no multi-scale fusion or test-time augmentation) against a strong export policy (multi-scale fusion and flip-based augmentation) on 100 paired slices, and report parallel trainer validation and TTA-off versus TTA-on re-inference on eight Dataset601 hold-out cases. For the industrial dataset, mean void-class IoU between modes is 0.716 (SD 0.043), while strong inference is ~2.6× slower and predicts lower mean void area (2.37% vs. 3.04%). The full weak export gives a 3D void ratio of 2.44% and integrated void volume of 5175 mm³. On validation patches, mean void Dice/IoU against the reference are 0.835/0.728, while weak–strong void IoU reaches 0.924 under the nnU-Net-native TTA contrast—quantities that must not be interchanged across domains or definitions. The present benchmark does not include a systematic polymer dosage series, and the study does not equate semantic void with open porosity but provides a reproducible disclosure template relevant to porous and polymer-modified cementitious CT reporting. Full article

The tree-contact single-phase-to-ground fault (TSF) in 10 kV distribution networks has high transition resistance, weak fault currents, and nonlinear steady-state waveforms. As existing high-impedance fault models cannot accurately describe its complete physical evolution, this paper proposes a novel modeling and parameter identification algorithm for TSF. First, based on recorded data from full-scale experiments, the initiation and development processes of TSF are studied, revealing the main factors affecting fault electrical characteristics—such as moisture evaporation, pyrolysis carbonization, air gap breakdown, and tree body current dissipation. Then, a dynamic resistance series model for TSF is constructed, with parameters identified and calibrated using experimental data, objective functions, and physical constraints. Finally, a 10 kV TSF simulation model is built and verified. Furthermore, a cross-condition predictive validation is performed using different voltage and geometric boundaries. Results demonstrate that the proposed physics-constrained model can effectively reproduce the RMS fault current envelope with asymmetric moisture evaporation characteristics. It also accurately predicts steady-state nonlinear waveform features without parameter re-tuning, providing more physically consistent data support for future TSF identification studies. Full article