Search Results (2,129)

We present a non-collisional mechanism for the formation of symmetric ring galaxies based on the dynamical relaxation of gas on top of quantized vortex configurations in Bose–Einstein Condensate Dark Matter (BECDM) solutions. For this we solve the fully coupled Gross–Pitaevskii–Poisson–Euler (GPPE) system in 3D, allowing for complete gravitational back-reaction between the baryonic gas and the dark matter component. Initializing the luminous matter as an ideal gas with random initial conditions on top of a vortex line with a topological winding number $m=1$, we systematically explore three dark matter self-interaction regimes, attractive, collisionless, and repulsive, for a characteristic ultra-light boson mass of $m_b={10}^{-22}\mathrm{eV}$. Our simulations reveal that the baryonic gas relaxes into stable, highly symmetric rings whose physical diameters from 11 to 16 kpc and total enclosed masses of order ${10}^{10}{\mathrm{M}}_{\odot }$ match the scales of intensely studied benchmarks like Arp 147. We use the detailed data from Arp 147 purely as a physical baseline to provide a structural proof of concept for our model. However, this self-consistent mechanism can be an interesting explanation for isolated ring galaxies-like Hoag-type objects, which lack the nearby companions or tidal debris required by standard collision models. Full article

A comprehensive investigation is presented for cyclohexene, C₆H₁₀, via the vacuum ultraviolet (VUV) absorption spectrum in the photon energy range 4.0–10.8 eV (310–115 nm). Quantum chemical calculations (Density Functional Theory (DFT), Time Dependent Density Functional Theory (TD-DFT) and Equation of Motion Coupled-Cluster Single and Doubles (EOM-CCSD)) are combined with experiments in order to provide the most accurate and up-to-date information about the electronic state spectroscopy of cyclohexene. The spectrum reveals several new features not previously reported in the literature, with special attention to the different Rydberg series converging to (11b)⁻¹  ${\tilde{X}}^{2}B$, (10b)⁻¹  ${\tilde{A}}^{2}B$, (12a)⁻¹ ${\tilde{B}}^{2}A$, (11a)⁻¹ ${\tilde{C}}^{2}A$, and (9b)⁻¹ ${\tilde{D}}^{2}B$ ionic electronic states of cyclohexene. We also provide absolute cross-section values from high-resolution VUV photoabsorption measurements, with photolysis lifetimes in the Earth’s atmosphere from 0 to 50 km altitude being obtained, showing that solar photolysis is not an important sink mechanism at altitudes lower than 22 km, relative to ^●OH radical reactions. Full article

The vertical excitation energies of 13 porphyrins and metalloporphyrins were benchmarked using 28 double-hybrid (DH) density functionals within the full TD-DFT framework, in conjunction with the def2-TZVP basis set and CPCM solvation model. The assessed functionals comprise global double hybrids (GDHs), range-separated double hybrids (RSDHs), and their spin-component-/spin-opposite-scaled variants (SCS/SOS-GDHs and SCS/SOS-RSDHs). Performance was evaluated for the B, Q1, and Q3 bands by comparison with experimental absorption maxima. B2PLYP provides one of the most balanced overall agreements, with mean absolute errors of approximately 0.03, 0.06, and 0.07 eV for the B, Q1, and Q3 bands, respectively. B2GPPLYP, $\omega$B88PP86, and $\omega$PBEPP86 also yield MAEs below 0.1 eV for all three bands. In contrast, several SCS/SOS-RSDH functionals perform very well for the B band but show pronounced systematic underestimation for the Q bands, revealing substantial band dependence. Comparison with published multireference vertical excitation energies further shows that close agreement with experimental absorption maxima does not necessarily imply equally close agreement with theoretical vertical excitation energies, particularly for the Q states. Overall, the results highlight the importance of state-specific assessment when benchmarking DH functionals for porphyrinic excitations. Full article

Multi-camera vehicle sensing provides a cost-effective solution for 3D environmental perception in intelligent vehicles. A practical challenge is that temporal fusion becomes unreliable when the ego-vehicle undergoes aggressive motion. Historical camera features are commonly aligned by rigid ego-pose warping, but residual motion-induced displacement can still degrade object localization, heading estimation, and velocity sensing. This paper presents GCL-BEV, a motion-aware temporal compensation framework for multi-camera vehicle sensor systems. The proposed framework uses synchronized surround-view cameras and ego-motion measurements as coupled sensing inputs. First, a Geometric-Aware Feature Enhancement (GAFE) module converts ego-motion priors into motion-conditioned BEV sampling offsets, allowing the visual sensing representation to compensate for local temporal misalignment before fusion. Second, a View-Consistency Learning (VCL) objective imposes a training-time equivariance constraint so that the sensor representation remains consistent under planar viewpoint perturbations. Across 10 random seeds on nuScenes, GCL-BEV achieves 57.80% ± 0.15 NDS and 46.22% ± 0.16 mAP with a ResNet-101 backbone. Compared with BEVDet4D, it reduces the mean Average Orientation Error by 5.4% and shows smaller degradation from steady driving to high-turn scenarios, indicating improved robustness for dynamic vehicle sensing. Full article

We propose a gold-graphene-vanadium dioxide (VO₂) hybrid metasurface capable of reversibly switching among three operational modes in the terahertz regime. The device achieves flexible polarization control by combining the insulator-to-metal phase transition of VO₂ with the electrical tunability of graphene. Simulation results reveal three distinct behaviors depending on the biasing conditions of the materials. With graphene held at a chemical potential of 0 eV and VO₂ in the insulating state, the metasurface acts as a linear-to-linear polarization converter. The polarization conversion ratio (PCR) exceeds 0.9 over the frequency range from 5.5 to 8.6 THz. When the graphene chemical potential is raised to 0.9 eV while VO₂ remains insulating, the metasurface switches to linear-to-circular conversion. Notably, the handedness of the outgoing wave depends on the polarization of the incoming signal. Over the 6.45–8.36 THz band, the axial ratio (AR) remains below 3 dB. A third functional state emerges when VO₂ switches to its metallic phase. In this state, the device simply operates as a broadband co-polarized reflector, covering the terahertz communication band from 0.1 to 10 THz. This switchable, multifunctional behavior should prove useful for terahertz communications, polarization imaging, and sensing applications. Full article

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

Background: Cerebrovascular diseases have complex pathogenesis and pose a serious threat to human health; thus, novel diagnostic and therapeutic strategies are needed. Small extracellular vesicles (sEVs), commonly referred to as exosomes, are 30–150 nm lipid-bilayer vesicles that shield long noncoding RNAs (lncRNAs) from degradation. Because exosomal lncRNAs are more stable than free lncRNAs in blood and cerebrospinal fluid and can cross the blood–brain barrier, they are promising as biomarkers and therapeutic vectors. This review summarizes the roles and mechanisms of exosomal lncRNAs in cerebrovascular diseases. Methods: This narrative review is based on the experimental literature and focuses on the biological functions and regulatory mechanisms of exosomal lncRNAs in cerebrovascular disorders. Results: As competing endogenous RNAs (ceRNAs), they sequester microRNAs (miRNAs), thereby derepressing downstream target-gene expression and modulating neuronal injury (oxidative stress, apoptosis, neuroinflammation) through the nuclear factor kappa-B (NF-κB) and phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathways. They show altered profiles in acute stroke (ischemic/hemorrhagic) correlated with neurological deficits, and are relevant to the early diagnosis of chronic diseases (atherosclerosis, aneurysm) and vascular dementia. Conclusions: Exosomal lncRNAs demonstrate promising translational potential in preclinical studies because they combine exosome delivery capabilities with lncRNA regulatory functions, although clinical validation remains limited. Full article

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg₃), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

Nonlinear optical (NLO) active chromophores and their applications in photothermal therapy (PTT) demonstrate great potential in modern theragnostics, owing to their efficient light–matter interactions. A recognized D–π–A chromophore, FTC-3f, which is reported to exhibit a high photothermal conversion efficiency (~51.11%), was structurally modified with various spacers and acceptors to elucidate enhanced PTT and NLO behavior through DFT and TD-DFT simulations. Molecular geometries were optimized at the B3LYP/6-31G (d, p) level. Their electronic properties, charge separation, and efficient transition pathways were analyzed using frontier molecular orbitals (FMOs), density of states (DOS), UV–visible spectroscopy, photon-induced electron transfer (PET), and transition density matrix (TDM) analysis. Among all the derivatives, D4 exhibited the smallest HOMO–LUMO energy gap (1.701 eV). The highest first-order hyperpolarizability (β) values are found for D4 (1.50 × 10⁵ in the gas phase, 7.40 × 10⁵ in water, 3.06 × 10⁵ in benzene solvent). The β_HRS is found to be in the range 5.11 × 10⁴ to 2.97 × 10⁴ and DR (3.65 × 10⁵ to 4.53 × 10⁴) supports the strong NLO response and strong synergistic donor–acceptor interactions. The designed chromophores exhibit much higher SHG and EOPE responses at 532, 1907.21, and 1064 nm than FTC-3F, indicating great potential for the synthesis of effective NLO nanomedicine. Molecular docking with bovine serum albumin (PDB ID: 4F5S) and Bcl-2 (PDB ID: 2W3L) suggested favorable binding conformations, consistent with a potential for transport and apoptotic-targeting behavior. All 3D molecular descriptor parameters indicate that the designed chromophores have potential for preferential targeting in PTT. This study investigates how structural modifications of donor–π–acceptor chromophores influence their electronic, optical, nonlinear optical, and theragnostic properties, providing insights into molecular design strategies for advanced NLO-based nanomedicine applications. Full article

LiDAR point clouds are sparse, irregular, and unevenly distributed, which makes representative feature extraction challenging for 3D object detection. Currently, Mamba modules have been increasingly applied to 3D object detection due to their ability to efficiently model global spatial dependencies. However, preserving geometric structures while modeling global spatial dependencies remains challenging in Mamba-based frameworks. In view of this, this paper proposes a single-stage 3D object detection framework for LiDAR-based point clouds. First, this paper designs a hierarchical multiscale structure called Multiscale Voxel–Point Alternating Fusion (MVPF) Module. Within this module, the 2DMamba module is introduced into the feature extraction stage at each scale to model global spatial dependencies in the BEV plane through selective state-space scanning. Second, we design a Z-to-Channel cross-dimensional reorganization strategy that merges the voxel Z dimension into the channel dimension, yielding a BEV-form feature representation suitable for 2DMamba processing. Third, this paper proposes a Local Voxel Feature Enhancement (LVFE) module composed of a Point-to-Voxel Feature Aggregation (PVFA) module, a Voxel Densification Module (VDM), and a coordinate-indexed Voxel-to-Point Mapping (VPM) module, which enhances local voxel representations while maintaining point–voxel spatial correspondence. On the KITTI validation set, Mamba-BEV achieved higher detection accuracy and inference speed than the baseline. Under the Moderate difficulty level of AP_R11, the 3D AP for Car, Pedestrian, and Cyclist improved by 0.6%, 1.5%, and 0.4%, respectively, compared to the baseline, while the inference speed increased from 44.05 FPS to 67.11 FPS. These results demonstrate the effectiveness of the proposed Mamba-BEV for point cloud-based 3D object detection. Full article

LiDAR–camera fusion is widely used for 3D perception in intelligent connected vehicles, but a clean camera branch does not necessarily compensate for structured LiDAR corruption. We propose CMS-Attack, a cross-modal search framework in which only the LiDAR point cloud is perturbed while the camera input remains unchanged; here, “cross-modal” denotes that a single-modality LiDAR perturbation propagates through the LiDAR–camera fusion process and disrupts the multimodal detector, rather than simultaneous perturbation of both modalities. The framework has the following two access-dependent routes: the gray-box route contains FB-CMS, which uses camera-BEV, LiDAR-BEV, fused-BEV, and detection-head responses to construct a target-aware prior and prune an over-complete candidate pool, and Adaptive CMS, which substitutes architecture-specific intermediate responses; the decision-only black-box route contains FC-CMS, which refines candidates solely from display-level target states. On the nuScenes validation split, FB-CMS reduced matched target confidence from 0.80 to 0.03 under 140 injected points, corresponding to a 96.1% relative drop and 100% ASR@0.3. The ten-query FC-CMS achieved 58.97% ASR@0.3, compared with 6.17% for random frustum spoofing. On the query-based FUTR3D detector, Adaptive CMS reduced the mean matched-target score from 0.642 to 0.058, corresponding to a 90.97% relative reduction and 93.42% ASR@0.3. Intermediate camera-, LiDAR-, and fused-BEV region energies changed by less than 0.8% despite target suppression, indicating disruption at the fusion-decision stage (defined here as the decoder/detection-head and post-processing path from fused representations to final object predictions) rather than a collapse of BEV feature magnitude. These results show that structured LiDAR fabrication is substantially more disruptive than information removal and that generic outlier filtering incurs a robustness–accuracy tradeoff. Full article

Ride quality—encompassing vehicle comfort, vibration isolation, and noise, vibration, and harshness (NVH)—has become a key competitive differentiator in modern automobiles. This paper presents a comprehensive update to the foundational literature on passenger car ride quality, capturing the rapidly growing literature and new technological paradigms that have emerged over the past decade. Established approaches to human vibration response, vehicle dynamics modelling, and road surface characterisation are examined within the ISO 2631 framework. This review critically surveys advances driven by battery electric vehicle (BEV) powertrains—where the absence of internal combustion engine noise unmasks motor whine, inverter switching noise, and tyre–road excitation, lowering the perceptual ride–NVH boundary from ~25 Hz toward 15–18 Hz—as well as intelligent semi-active and active suspension technologies, deep reinforcement learning for suspension control, machine learning for ride quality prediction, and connected vehicle infrastructure enabling predictive preview control. Key research gaps are identified: the absence of validated ISO 2631 frequency weightings for autonomous vehicle postures, the lack of standardised open benchmark datasets for cross-study comparison, and the unresolved sim-to-real validation gap for data-driven suspension controllers. Ten priority research directions are proposed for the coming decade. Full article

Multi-modal 3D object detection is an important task in autonomous driving systems, where cameras and LiDAR provide complementary semantic and geometric information. Most existing BEV fusion methods are designed based on the Cartesian representation space, which does not fully match the sensing geometry of camera and LiDAR. This generally leads to redundant computation in distant regions. To address this issue, GARF, a geometry-aware polar BEV framework, is presented for multi-modal 3D object detection. GARF organizes camera and LiDAR features in a unified polar BEV space, which can represent spatial resolution more compactly. For the camera branch, the uncertainty-guided transformation of the polar view is designed to improve the reliability of depth estimation. Then, the generated polar BEV feature is further refined to attenuate radial noise and angular discontinuity. For the LiDAR branch, the polar-aware sparse feature extraction and distortion correction modules are designed to deal with the anisotropic structure and geometric distortion caused by polar voxelization. For multi-modal fusion, the region-aware cross-modal fusion strategy and polar detection head with anisotropic Gaussian center response map are developed, which achieve effective feature interaction and consistent geometry supervision. The experimental results on nuScenes show that GARF achieves 71.8% mAP and 73.7% NDS, improving the baseline by 3.3% mAP and 2.3% NDS. Meanwhile, the inference speed increases from 7.1 FPS to 8.9 FPS, and the consumption of GPU memory decreases from 41,114 MiB to 33,346 MiB. Full article

Tendinopathies represent a major clinical challenge. Vasculature, neuromuscular junctions, low cellularity, and slow extracellular matrix (ECM) turnover restrict endogenous repair and predispose injured tendons to fibrosis, mechanical weakness, and reinjury. Current therapeutic strategies including rehabilitation protocols, anti-inflammatory medications, platelet-rich plasma (PRP) injections, and surgical repair primarily address symptoms or structural deficits without correcting the underlying biological limitations of tendon healing. Cell-based therapies have emerged as a promising regenerative approach aimed at restoring tissue homeostasis through modulation of angiogenesis, collagen synthesis, immune responses, and tenogenic differentiation. Mesenchymal stem cells (MSCs), adipose-derived stem cells (ADSCs), tendon-derived stem cells (TDSCs), induced pluripotent stem cells (iPSCs), differentiated tenocytes, and extracellular vesicle (EV)-based products have demonstrated the ability to enhance vascularization, promote type I collagen remodeling, suppress excessive inflammation, and stimulate tenocyte lineage commitment. These effects are mediated through paracrine signaling, growth factor secretion, and activation of key pathways, including HIF-1α, TGF-β/SMAD, NF-κB, and PI3K/Akt signaling. Despite promising preclinical data, significant translational challenges remain, including limited cell survival at the injury site, variability in cell sources and dosing, immunogenicity, risk of misdifferentiation, and lack of standardization across clinical protocols. Emerging strategies such as genetic modification, hypoxic preconditioning, scaffold-based delivery systems, and extracellular vesicle engineering aim to enhance therapeutic efficacy and reproducibility. This review synthesizes current evidence on cell-based tendon repair, critically evaluates mechanistic insights, clinical trials, and translational barriers, and outlines future directions toward biologically informed regenerative therapies. Full article

Existing vision-based parking slot perception methods predominantly adopt Around View Monitor (AVM) or Bird’s Eye View (BEV) imagery and target intact markings. Robust segmentation of heavily worn parking lines from monocular rear-facing cameras, however, remains largely unexplored. This paper proposes a lightweight fully convolutional network for degradation-robust monocular parking-line segmentation. Coordinate Attention (CoordAtt) and Atrous Spatial Pyramid Pooling (ASPP) operate in parallel at the encoder bottleneck, and their outputs are fused through a learnable spatial-adaptive gate that assigns pixel-wise weights to each branch, eliminating the mutual interference inherent in serial fusion. On a self-constructed dataset of 2273 annotated monocular parking-line images, the proposed network achieves 64.03% IoU and 280.5 FPS with 7.65 M parameters. Ablation experiments confirm that serial fusion degrades directional precision, whereas parallel gated fusion preserves both directional cues and multi-scale context through pixel-level adaptive allocation. These results demonstrate improved segmentation accuracy and wear-completion robustness under severe marking degradation, providing an efficient perception solution for monocular camera-based automatic parking systems. Full article