Search Results (1,999)

Active galactic nuclei (AGNs) often exhibit broad-line regions (BLRs), populated by high-velocity clouds in approximately Keplerian orbits around the central supermassive black hole (SMBH) at subparsec scales. During episodes of intense accretion at super-Eddington rates, the accretion disk can launch a powerful, radiation-driven wind. This wind may overtake the BLR clouds, forming bowshocks around them. Two strong shocks arise: one propagating into the wind, and the other into the cloud. If the shocks are adiabatic, electrons and protons can be efficiently accelerated via a Fermi-type mechanism to relativistic energies. In sufficiently dense winds, the resulting high-energy photons are absorbed and reprocessed within the photosphere, while neutrinos produced in inelastic $pp$ collisions escape. In this paper, we explore the potential of super-accreting AGNs as neutrino sources. We propose a new class of neutrino emitter: an AGN lacking jets and gamma-ray counterparts, but hosting a strong, opaque, disk-driven wind. As a case study, we consider a supermassive black hole with $M_{\mathrm{BH}}={10}^6{M}_{\odot }$ and accretion rates consistent with tidal disruption events (TDEs). We compute the relevant cooling processes for the relativistic particles under such conditions and show that super-Eddington accreting SMBHs can produce detectable neutrino fluxes with only weak electromagnetic counterparts. The neutrino flux may be observable by the next-generation IceCube Observatory (IceCube-Gen2) in nearby galaxies with a high BLR cloud filling factor. For galaxies hosting more massive black holes, detection is also possible with moderate filling factors if the source is sufficiently close, or at larger distances if the filling factor is high. Our model thus provides a new and plausible scenario for high-energy extragalactic neutrino sources, where both the flux and timescale of the emission are determined by the number of clouds orbiting the black hole and the duration of the super-accreting phase. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

This study aims to develop environmentally friendly soil-stabilization materials by investigating the synergistic enhancement mechanism of industrial by-product lignin fibers (LFs) and sodium sulfate (Na₂SO₄) on the mechanical and micro-structural properties of cement-stabilized soil. A systematic evaluation was conducted through unconfined compressive strength (UCS), splitting tensile strength, and capillary water absorption tests, supplemented by microscopic analyses including XRD and SEM. The results indicate that the optimal synergistic effect occurs at 1.0% LF and 0.10% Na₂SO₄, which increases UCS and splitting tensile strength by 9.23% and 18.37%, respectively, compared to cement-stabilized soil. Meanwhile, early strength development is accelerated. Microscopically, LF physically bridges soil particles, forming aggregates, reducing porosity, and enhancing cohesion. Chemically, Na₂SO₄ acts as an activator, accelerating cement hydration and stimulating pozzolanic reactions to form calcium aluminosilicate hydrate and gypsum, which fill pores and densify the matrix. The synergistic mechanism lies in Na₂SO₄ enhancing the interaction between the LFs and clay minerals through ion exchange, facilitating the formation of a stable spatial network structure that inhibits particle sliding and crack propagation. This technology offers substantial sustainability benefits by utilizing paper-making waste LF and low-cost Na₂SO₄ to improve soil strength, toughness, and impermeability. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and treatment remains challenging due to high recurrence rates, resistance to chemotherapy, and severe side effects. Dasatinib (Das) has shown therapeutic potential against HCC, but its clinical use is limited by poor bioavailability and short half-life (~3–4 h). Here, we developed a hyaluronic acid (HA)-coated sterosome for targeted and sustained delivery of Das to CD44-overexpressing HCC cells. Sterosomes composed of octadecylamine and cholesterol at a 5:5 (v/v) ratio were prepared via thin-film hydration and sonication, yielding stable particles (~90 nm) with high encapsulation efficiency (EE ~72%) for uncoated vesicles and ~58% after HA coating. HA-sterosomes (HA-St-Das) exhibited a uniform size (≈200 nm) and negative surface charge (–26 mV), with improved storage stability and resistance to lyophilization. In vitro release studies demonstrated pH-responsive Das release accelerated under acidic conditions (pH 6.0–5.0), mimicking tumor and lysosomal environments. In HepG2 cells, HA-St-Das exhibited enhanced cytotoxicity (IC50 ~7.0 μM) and prolonged intracellular retention compared to free Das and uncoated carriers. Fluorescence microscopy confirmed receptor-mediated uptake via CD44, leading to gradual and sustained intracellular delivery. Overall, the HA-St-Das system provides biocompatible, targeted, and controlled Das delivery, addressing key limitations of current liver cancer therapies and representing a promising nanomedicine platform for further development. Full article

This study modified corn, oat, barley, and buckwheat starches using a Henan-specific sourdough starter, revealing that the initial starch architecture governs differentiated functional transformations. Pore-dominant starches (corn/buckwheat) underwent “inside-out” enzymatic pathways—corn starch exhibited a 38.21% reduced particle size through pore expansion, with long amylopectin chain degradation forming thermally stable gels, establishing it as an ideal base for anti-staling sauces and frozen dough. Buckwheat starch demonstrated a 44% increased amylose content facilitated by porous structures, where post digestion double helix formation elevated the resistant starch (RS) content by 7%, achieving a significant 28.19% GI (Glycemic Index) reduction. Conversely, fissure-dominant starches (oat/barley) experienced “surface-inward” limited erosion—oat starch, constrained by surface cracks, showed amorphous region degradation and short-chain proliferation, accelerating glucose release and adapting it for rapid digestion products like energy bars. Barley starch primarily underwent amorphous zone modification, enhancing the pasting efficiency to provide raw materials for instant meal replacement powders. Full article

Airy beams exhibit enormous application potential in the field of optics and microwave owing to their unique self-bending, self-accelerating, and non-diffracting characteristics. In this paper, the Airy beams are dynamically generated and manipulated in both reflection and transmission spaces utilizing a full space programmable metasurface, which can achieve an approximately 360° phase coverage in the reflection space and a nearly 180° phase coverage in the transmission space in the operating frequency band from 6 GHz to 7 GHz. The direct current (DC) bias voltage is applied to the varactor diodes integrated on the metasurface by precise control of the external feeding system, allowing dynamic generation and regulation of Airy beams. Numerical simulations and experimental measurements are performed at 6.5 GHz. The Airy beams with parameters a = 56 and 61 are generated in the transmission space, while the Airy beams with parameters a = 71 and 81 are achieved in the reflection space. The parabolic propagation trajectory of the main beams and acceleration in the transverse planes can be observed. The good agreement between the simulated and measured results demonstrates that the metasurface can dynamically generate and manipulate the Airy beams in full space. The suggested Airy beam manipulation system has a wide range of applications, including optical particle manipulation, imaging, and difficult terrain exploration. Full article

Oil and gas pipelines represent critical infrastructure for energy transportation and are essential for ensurin g energy security. The seismic disaster risk assessment of these pipelines is of paramount importance for safeguarding energy supplies. Traditional assessment methodologies primarily focus on the structural integrity of the pipeline body, often neglecting the impact of auxiliary structures and site-specific disaster effects. This study proposes an enhanced risk assessment methodology to address these gaps. This research systematically compiles seismic damage case studies of pipelines from major seismic zones in China. By considering the interactions between auxiliary structure types, site conditions, and forms of disasters, 15 typical operating conditions are identified, and a seismic damage case database is constructed. We develop a failure probability model that integrates geotechnical parameters, structural responses, and ground motion characteristics to assess the impact of liquefaction, site amplification, fault activity, and collapse/landslide phenomena. Utilizing Particle Swarm Optimization (PSO) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) algorithms, this model quantifies the influence weights and coefficients of these disasters on pipeline auxiliary structures, forming a vulnerability matrix centered around Peak Ground Acceleration (PGA). Additionally, a dual-vulnerability assessment framework is established, and a failure probability formula accounting for the superposition effects of multiple disasters is proposed. This study marks a significant advancement, transitioning from traditional single-pipeline evaluations to “structure-disaster-site” coupling analysis, and provides a scientific basis for pipeline seismic design, operation, and maintenance under specific environmental conditions. This work contributes to the development of quantitative and refined seismic risk assessments for oil and gas pipelines. Full article

The detection of gamma-ray burst GRB 221009A has attracted significant attention due to its record brightness and first-ever detection of multi-TeV $\mathit{\gamma }$-rays from a GRB. Located at redshift $z=0.151$, this event is relatively nearby by GRB standards yet remains cosmologically distant, making the survival of multi-TeV photons surprising. The Large High Altitude Air Shower Observatory detected photons with energies up to ∼13 TeV during the early afterglow phase, challenging standard EBL models. We investigate whether several theoretical frameworks can explain this anomalous emission: reduced EBL opacity due to cosmic voids along the line of sight, novel emission mechanisms within the GRB environment, secondary $\mathit{\gamma }$-ray production through cosmic-ray cascades, and new physics scenarios involving Lorentz invariance violation or axion-like particles. Our analysis reveals areas of consensus regarding the exceptional nature of this event, while highlighting ongoing theoretical tensions about the dominant physical processes. We discuss the limitations of current models and identify specific observational signatures that future multi-wavelength and multi-messenger observations could provide to discriminate between competing explanations. The continued study of similar events with next-generation facilities will be crucial for resolving these theoretical challenges and advancing our understanding of extreme particle acceleration processes in astrophysical environments. Full article

Different polyurethanes (PURs) and silicone for potential use in particle accelerators and detectors have been characterized in the uncured state, after curing, and after exposure to ionizing irradiation in ambient air and in liquid helium. The viscosity evolution during processing was measured with a rheometer. Dynamic mechanical analysis (DMA) and Shore A hardness measurements were applied to detect irradiation-induced crosslinking and chain scission effects. Uniaxial tensile and flexural tests under ambient and cryogenic conditions have been performed to assess changes in mechanical strength, elongation at break, and elastic properties. The initial viscosity of 550 cP at 25 °C of the uncured PUR RE700-4 polyol and RE106 isocyanate system for protective encapsulation is sufficiently low for impregnation of small magnet coils, but the pot life of about 30 min is too short for impregnation of large magnet coils. The cured RE700-4 system has outstanding mechanical properties at 77 K (flexural strength, impact strength, and fracture toughness). When RE700-4 is exposed to ionizing radiation, chain scission and cross-linking occur at a similar rate. In the other casting systems, irradiation-induced changes are cross-linking dominated, as manifested by an increase of the rubbery shear modulus (G’_rubbery), the ambient temperature Young’s modulus (E_RT), and the Shore A hardness. Cross-linking rates are strongly reduced when irradiation occurs in liquid helium. The irradiation effect on mechanical properties can be strongly dependent on the testing temperature. The RT mechanical strength and strain at fracture of the cross-linking silicone is drastically decreased after 1.6 MGy, whereas its 77 K strain at fracture has almost doubled. In addition, 77 K elastic moduli are similar for all pure resins and only slightly affected by irradiation. Full article

Recently, plastic and agricultural waste have gained attention as sustainable alternatives. Despite efforts to recycle these materials, much still ends up in landfills, raising environmental concerns. To optimize their potential, these wastes ought to be transformed into value-added products for diverse industrial applications. This work focused on producing thin composite material films using olive oil solid waste called JEFT and recycled plastic bottles. JEFT was cleaned, dried, and processed mechanically via ball milling to produce nano- and micron-sized particles. Composite films were produced via melt compounding and compression molding with a rapid cooling process for controlled crystallinity and enhanced flexibility. Their density, water absorption, tensile strength, thermal stability, water permeability, functional groups, and biodegradation were comprehensively analyzed. Results indicated that 50% JEFT in recycled plastic accelerated thermal deterioration (42.7%) and biodegradation (13.4% over 60 days), highlighting JEFT’s role in decomposition. Peak tensile strength (≈32 MPa) occurred at 5% JEFT, decreasing at higher concentrations due to agglomeration. Water absorption and permeability slightly increased with JEFT content, with only a 1% rise in water permeability for 50% JEFT composites after 60 days. JEFT maintained the recycled plastic’s surface chemistry, ensuring stability. The findings of this study suggest that JEFT/r-HDPE films show potential as greenhouse coverings, enhancing crop production and water efficiency while improving plastic biodegradation, offering a sustainable waste management solution. Full article

Ultra-high-energy cosmic rays (UHECRs) with energies exceeding ${10}^{19}$ eV are believed to originate from extragalactic environments, potentially associated with relativistic jets in active galactic nuclei (AGN). Among AGNs, blazars, particularly those detected in very-high-energy (VHE) gamma rays, are promising candidates for UHECR acceleration and high-energy neutrino production. In this work, we investigate three blazar sources, AP Librae, 1H 1914–194, and PKS 0735+178, using multiwavelength spectral energy distribution (SED) modeling. These sources span a range of synchrotron peak classes and redshifts, providing a diverse context to explore the physical conditions in relativistic jets. We employ both leptonic and lepto-hadronic models to describe their broadband emission from radio to TeV energies, aiming to constrain key jet parameters such as magnetic field strength, emission region size, and particle energy distributions. Particular attention is given to evaluating their potential as sources of UHECRs and high-energy neutrinos. Our results shed light on the complex interplay between particle acceleration mechanisms, radiative processes, and multi-messenger signatures in extreme astrophysical environments. Full article

With accelerating urbanization, the growing density of buildings and the expansion of road networks have fundamentally reshaped the interplay between geological hazards and urban infrastructure. Traditional vulnerability assessment models for buildings (VAB) frequently overlook how human engineering activities—such as construction and city expansion—intensify disaster risk. To address this gap, we introduce VAB-HEAIC, a novel framework that integrates three dimensions of vulnerability: geological environment, building attributes, and dynamics of human engineering activity. Leveraging historical high-resolution imagery, we construct a human engineering activity intensity change indicator by quantifying variations in both road network density and building density. Nineteen evaluation factors, identified via spatial statistical analysis and field surveys, serve as model inputs. Within this framework, we evaluate four machine learning algorithms (Support Vector Regression, Random Forests, Back Propagation Neural Networks, and Light Gradient Boosting Machines), each coupled with four hyperparameter-optimization techniques (Particle Swarm Optimization, Sparrow Search Algorithm, Differential Evolution, and Bayesian Optimization), and three data augmentation strategies (feature combination, numerical perturbation, and bootstrap resampling). Applied to 5471 buildings in Dajing Town, the approach is validated using Root Mean Squared Error (RMSE). The optimal configuration—LGBM tuned with Differential Evolution and enhanced via bootstrap resampling—yields an RMSE of 0.3745. An ablation study further demonstrates that including the human engineering activity intensity change factor substantially improves prediction accuracy. These results offer a more comprehensive methodology for urban disaster risk management and planning by explicitly accounting for the role of human activity in building vulnerability. Full article

The pepper-pot method is a beam diagnostics technique used to measure the transverse beam profile, divergence angle, and envelope in particle accelerators. However, its practical application faces challenges, such as insufficient point recognition accuracy and signal quality degradation in complex environments. Based on the Boron Neutron Capture Therapy (BNCT) facility at the Hefei Comprehensive National Science Center—Energy Research Institute (Anhui Energy Laboratory), this study developed an improved pepper-pot beam diagnostics system to optimize the beam quality of the accelerator ion source. The key innovation is adaptive threshold segmentation for spot segmentation, and the experimental results indicate that the enhanced image segmentation method outperforms traditional methods in terms of segmentation accuracy and robustness. Full article