Search Results (250)

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article

To address the critical challenges of geological hazards, such as water and mud inrush, encountered during the construction of deep-buried tunnels in China, this study investigates the hydraulic properties of remolded mud-infill materials. A multi-scale approach, integrating indoor variable-head permeability tests with scanning electron microscopy (SEM), was employed to characterize the evolutionary patterns of the permeability coefficient (k). Specifically, the research evaluates the independent influences of moisture content, dry density, and confining pressure, alongside the synergistic coupling between dry density and hydration state. The results demonstrate the following: Under independent variable conditions, k exhibits a monotonic decline with increasing dry density and confining pressure while showing a positive correlation with moisture content, with the sensitivity varying significantly across different parameter regimes; under coupled effects, the permeability in both low- and high-moisture ranges manifests a distinct “increase–decrease–increase” fluctuation as dry density rises, reaching a local peak at 2.20 g/cm³. Notably, a relative minimum k (6.12 × 10⁻⁷ cm/s) is achieved at the optimum moisture content (5.8%); micro-mechanistic analysis reveals that low-moisture samples are characterized by randomized angular particles and well-developed interconnected macropore networks, facilitating higher k values. Conversely, high-moisture samples exhibit preferential plate-like stacking dominated by occluded micropores, resulting in a substantial reduction in hydraulic conductivity. This study elucidates the multi-factor coupling mechanism governing the seepage behavior of remolded mud, providing essential theoretical benchmarks for the prediction and mitigation of water–mud outburst disasters in deep underground engineering, thereby ensuring the structural stability and operational safety of tunnel projects. Full article

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with sample block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

Mobile health units (MHUs) can reach populations facing barriers to traditional primary care, but information about factors associated with their utilization is limited. The objective of this ecological study was to evaluate whether MHU encounter density is increased in census tracts designated as Primary Care Health Professional Shortage Areas (HPSAs) and explore whether associations varied by socioeconomic vulnerability. We analyzed Wayne State University/Wayne Health MHU encounters with adult patients from July 2021 to September 2025. Negative binomial regression models with a log link and log(population) offset tested the a priori hypothesis that encounter density was increased in designated versus undesignated HPSA census tracts. Sensitivity analyses assessed variation by social vulnerability index score quartiles established by the US Centers for Disease Control and Prevention. One quarter of the five-county metropolitan Detroit, Michigan, catchment area census tracts were designated healthcare shortage areas. Overall, 13,852 encounters with 10,924 unique patients occurred across 924 of 1305 census tracts. Encounter rate per adult population was significantly increased by severalfold comparing designated versus undesignated shortage areas, with stronger associations at lower socioeconomic vulnerability index score quartiles (interaction p = 0.0006). These findings support continued efforts to scale and evaluate MHUs to address projected healthcare shortages, particularly in socioeconomically vulnerable areas. Full article

The Wigner operator’s normal ordering form is deduced by using the method of integration within the ordered product of operators, and the operator’s Weyl ordering symbol is employed. The integration theory within the Weyl ordering product of operators is applied, and the Wigner operator’s Weyl ordering form is deduced. Then, the Wigner operator’s slice theorem is proposed, which helps project and display a new pure-state density operator. Thus, the quantization of classical tomography theory is realized. We illustrate the derivation of the bi- and tri-partite entangled state representations, respectively, which completes the argument. Full article

In this work, a comprehensive first-principles investigation of the electronic, magnetic, and optical properties of pristine and Fe-, Co-, and Ni-doped MoS₂ monolayers is presented within the framework of density functional theory. Substitutional transition-metal doping at the Mo site is shown to induce spin-polarized impurity states within the pristine band gap, leading to significant modifications of the electronic structure, including metallic, semimetallic, or half-metallic behavior depending on the dopant species. The calculated spin-resolved band structures and projected density of states reveal a strong hybridization between the dopant $3d$ orbitals and the Mo-$4d$/S-$3p$ states, giving rise to sizable magnetic moments and dopant-dependent exchange splitting. When spin–orbit coupling is included, the combined effect of exchange interactions and relativistic effects leads to an effective valley splitting at the K and $K^{\prime }$ points, whose magnitude and sign depend sensitively on the chemical nature of the dopant. Optical properties are analyzed within a linear-response framework, showing pronounced dopant-induced modifications of the optical spectra. While the pristine monolayer exhibits well-defined excitonic features, transition-metal substitution introduces low-energy optical transitions associated with impurity-related states. Consequently, the exciton binding energies estimated from the difference between the electronic and optical gaps are interpreted as effective measures of dopant-induced perturbations to optical transitions, rather than as quantitative many-body excitonic binding energies in the strict sense. These results provide microscopic insight into the interplay between magnetism, spin–orbit coupling, and optical response in doped MoS₂ monolayers, highlighting the potential of transition-metal substitution as a route to engineer spin- and valley-dependent phenomena in two-dimensional materials. Full article

Interlayer-sliding ferroelectricity in van der Waals bilayers enables ultralow-power switching, but practical devices are often limited by contact/interface scattering and weak coupling between polarization and transport. We propose homophase lateral architectures based on bilayer Janus MoSSe: a 1T/2H/1T ferroelectric tunnel homojunction and an H-phase lateral p–i–n photodetector (artificially doped electrode). Metallic 1T electrodes largely eliminate contact barriers and maximize polarization-driven tunneling modulation. Using non-equilibrium Green’s function–density functional theory (Perdew–Burke–Ernzerhof approximation, without explicit spin–orbit coupling), we find that AB to BA sliding reduces the current from the nA range to the pA range, with the minimum current of|I_OFF|_min = 2.83 pA, yielding giant tunneling electroresistance up to 5.3 × 10⁴%. Projected local density of states reveals a non-rigid long-range potential redistribution that reshapes the tunneling barrier and opens high-transmission channels. In the p–i–n photodetector, the response is strongly anisotropic and stacking-dependent: AB reaches photocurrent density J_ph ≈ 7.2 µA·mm⁻² at 2.6 eV for in-plane light versus ≈ 2.9 µA·mm⁻² at 3.5 eV for out-of-plane, and exceeds BA by 1.5–1.8 times due to density of states advantages and Mo-d orbital selection rules. Bilayer Janus MoSSe therefore provides a reconfigurable platform for high-contrast memory and polarization-sensitive photodetection. Full article

The decarbonisation of the transport sector is a cornerstone of the European Green Deal, necessitating a transition toward integrated, digital, and sustainable mobility solutions such as Mobility-as-a-Service (MaaS). While early MaaS implementations were characterised by local bottom-up experiments, recent state-level actions mark a shift toward large-scale systemic deployment. This paper investigates the evolving role of universities within this transition, using MaaS4Italy initiative as a primary case study. Through a qualitative analysis of 11 pilot projects, conducted between January and July 2025, the research examines how academic institutions have been integrated into the national governance framework, transitioning from traditional living labs for technical testing to pivotal institutional anchors and governance buffers. The findings reveal a dual role for universities: as scientific partners and as neutral mediators. However, a relevant paradox is highlighted as well: while the institutionalisation of universities de-risks public investment and fosters data-sharing trust, it may simultaneously limit their potential as high-density operational testbeds for innovative Corporate MaaS (CMaaS) solutions. Present research supports a broader understanding for policymakers, thus underscoring the importance of formalising the role of intermediary institutions to ensure the long-term sustainability and scalability of smart mobility ecosystems. These insights prove to be pivotal towards the implementation of multi-level environmental governance mechanisms and the strategic use of recovery funds to catalyse the transition toward climate neutrality. Full article

Zinc titanate (ZnTiO₃) is a chemically stable and non-toxic oxide perovskite whose photovoltaic potential remains largely unexplored due to its wide indirect bandgap. This study evaluates whether oxygen-vacancy (F-center) engineering can tailor its electronic structure and improve its suitability as a photovoltaic absorber. Density Functional Theory (DFT) calculations using VASP (PAW − GGA/PBE + U) were performed to evaluate structural stability, electronic properties, and electron affinity, while optical absorption was modeled through a combined Tauc–Gaussian approach. Device performance was assessed via SCAPS-1D simulations in an FTO/ZnO/ZnTiO₃/Spiro-OMeTAD architecture. Oxygen vacancies induce bandgap narrowing from ~2.96 eV to ~1.47 eV and generate Ti-3d-dominated donor-like and deep intragap states. The calculated electron affinity is ~3.77 eV. Simulated single-layer devices reach Voc ≈ 1.11 V, Jsc ≈ 8.27 mA·cm⁻², FF ≈ 83%, and a maximum efficiency of ~7.65%, primarily limited by moderate absorption strength and defect-assisted recombination. Multilayer configurations indicate that geometric optimization can significantly enhance projected efficiency, approaching 19.25% under idealized conditions. Although vacancy engineering extends visible-light absorption, the intrinsic indirect band-gap character constrains the ultimate photovoltaic performance of ZnTiO₃. Full article

Reinforcement learning (RL) has demonstrated considerable promise in the field of autonomous driving. However, decision-making processes in real-world environments are inevitably influenced by measurement noise and perception inaccuracies, which can result in suboptimal or even unsafe actions. To mitigate these risks, it is imperative for autonomous vehicles to model and account for such perception uncertainties effectively. This study focuses on the ramp merging scenario in autonomous driving, where environmental uncertainties are modeled as adversarial perturbations to the states observed by RL agents. We propose a novel adversarial attack framework that combines RL with momentum-based projected gradient descent (MPGD), aiming to simulate worst-case perception errors by perturbing the sensory inputs of the driving agent. Experimental evaluations across varying traffic densities and multiple RL algorithms for autonomous driving demonstrate that our approach outperforms three baseline adversarial attack strategies in simulating the worst-case perception errors. Additionally, adversarial training of the driving agent with our attack model significantly enhances the robustness of the autonomous vehicle, improving its performance in the presence of such worst-case perception uncertainties. Full article

Background/Objectives: Lipid metabolism is fundamental to energy homeostasis and cellular structural integrity, and its dysregulation is a hallmark of biological aging. While DNA methylation clocks are well-established, it remains unclear whether epigenetic sites associated with specific lipid markers—High-Density Lipoprotein (HDL), Total Cholesterol (TCH), and Triglycerides (TGY)—are evolutionarily conserved across mammals and how they manifest across different metabolic tissues. Methods: We identified lipid-associated CpG sites in humans using the Korean Genome and Epidemiology Study (KoGES) cohort and projected these sites onto the Mammalian Methylation Consortium (GSE223748) dataset. Using the Hybrid Pi (HyPi) score, we selected robust markers to analyze their evolutionary conservation, tissue specificity, and age-related dynamics across over 300 mammalian species. Specifically, we examined the phylogenetic concordance between blood and three major metabolic organs (Liver, Adipose, Muscle) in five representative species. Results: Lipid-related CpGs were highly conserved across diverse mammals. t-SNE analysis revealed that these epigenetic signatures clustered samples by tissue identity and species. Methylation levels of these CpGs showed significant correlations with maximum lifespan and distinct aging rates across tissues. Notably, phylogenetic tanglegram analysis revealed a high degree of concordance between blood and key metabolic organs, suggesting that blood methylation profiles mirror the evolutionary trajectory of internal metabolic tissues. Furthermore, these patterns were consistent between sexes, indicating a fundamental, non-dimorphic regulation of lipid epigenetics. Conclusions: Our findings suggest that epigenetic mechanisms governing lipid metabolism are deeply conserved to maintain tissue identity and regulate biological aging, with blood serving as a reliable evolutionary proxy for internal metabolic states. Full article

Magnetic fields, either imposed externally or produced spontaneously, are often present in laser-driven high-energy-density systems. In addition to changing plasma conditions, magnetic fields also directly modify laser–plasma interactions (LPI) by changing the participating waves and their nonlinear interactions. In this paper, we use two-dimensional particle-in-cell (PIC) simulations to investigate how magnetic fields directly affect crossbeam energy transfer (CBET) from a pump to a seed laser beam when the transfer is mediated by the ion-acoustic wave (IAW) quasimode. Our simulations are performed in the parameter space where CBET is the dominant process and in a linear regime, where pump depletion, distribution function evolution, and secondary instabilities are insignificant. We use a Fourier filter to separate out the seed signal and project the seed fields onto two electromagnetic eigenmodes, which become nondegenerate in magnetized plasmas. By comparing the seed energy before CBET occurs and after CBET reaches quasi-steady state, we extract the CBET energy gains for both eigenmodes in lasers that are initially linearly polarized. Our simulations reveal that, starting from a few MG fields, the two eigenmodes have different gains, and magnetization alters the dependence of the gains on laser detuning. The overall gain decreases with magnetization when the laser polarizations are initially parallel, while a nonzero gain becomes allowed when the laser polarizations are initially orthogonal. These findings qualitatively agree with theoretical expectations. Full article

This paper proposes a building reconstruction framework for airborne LiDAR data to address the challenge of automated modeling under conditions of uneven point cloud density and missing vertical walls, generating high-precision and structurally compact 3D building models. The method first combines adaptive resolution hypervoxels with a global graph cut optimization strategy to extract precise roof plane primitives from sparse point clouds of buildings. Subsequently, it infers building facades and internal vertical walls based on point cloud projection contours and height change detection, thereby completing the wall structures commonly missing in airborne LiDAR data. Finally, a feature line constraint term is introduced into the hypothesis-and-selection-based reconstruction framework to guide the structural optimization of candidate planes, ensuring the reconstructed model closely matches the actual building geometry. The proposed method was evaluated on multiple public airborne LiDAR datasets, demonstrating its effectiveness through qualitative and quantitative comparisons with various state-of-the-art approaches. Full article

Global agriculture faces unprecedented challenges, including a projected population of 10 billion by 2050, declining arable land, and the urgent need to phase out antibiotic growth promoters (AGPs) to stem antimicrobial resistance (AMR). This review evaluates fermentation technology as a sustainable solution to the “food–feed–fuel” three competing land uses. We systematically compare solid-state fermentation (SSF) and submerged fermentation (SmF), highlighting their quantitative advantages: SSF offers 2–3× higher volumetric productivity and 70–90% lower water usage for solid wastes (e.g., soybean meal, wheat bran), while SmF provides superior process control for high-value products (e.g., single-cell protein). Key molecular mechanisms are discussed, including enzymatic degradation of anti-nutritional factors (up to 95% phytate and 98.8% tannin removal), mycotoxin detoxification (60–80% reduction), and biosynthesis of bioactive compounds (e.g., vitamin B12 enrichment up to 15-fold). Fermented feeds benefit many livestock species, particularly in organic and high-density farming systems, improving growth performance, gut health, and disease resistance while reducing environmental footprints. Advanced technologies such as AI-driven digital twins, CRISPR-based strain engineering, and precision fermentation are explored to overcome bottlenecks, including heat dissipation, strain stability, and process control. Despite challenges in scale-up, economics, and divergent global regulations (EU, USA, China, Southeast Asia, and Africa), fermentation is a critical biotechnological paradigm for circularity—the circular bioeconomy—and long-term food security. Future research should prioritize cost-effective large-scale implementation and the harmonization of regulatory frameworks. Full article

Conventional object detectors represent each object by a deterministic bounding box, regressing its center and size from RGB images. However, such discrete parameterization ignores the inherent uncertainty in object appearance and geometric projection, which can be more naturally modeled as a probabilistic density field. Recent works have introduced Gaussian-based formulations that treat objects as distributions rather than boxes, yet they remain limited to 2D images or require late fusion between image and depth modalities. In this paper, we propose a unified Gaussian-based framework for direct 3D object detection from RGB-D inputs. Our method is built upon a vision transformer backbone to effectively capture global context. Instead of separately embedding RGB and depth features or refining depth within region proposals, our method takes a full four-channel RGB-D tensor and predicts the mean and covariance of a 3D Gaussian distribution for each object in a single forward pass. We extend a pretrained vision transformer to accept four-channel inputs by augmenting the patch embedding layer while preserving ImageNet-learned representations. This formulation allows the detector to represent both object location and geometric uncertainty in 3D space. By optimizing divergence metrics such as the Kullback–Leibler or Bhattacharyya distances between predicted and target distributions, the network learns a physically consistent probabilistic representation of objects. Experimental results on the SUN RGB-D benchmark demonstrate that our approach achieves competitive performance compared to state-of-the-art point-cloud-based methods while offering uncertainty-aware and geometrically interpretable 3D detections. Full article