Search Results (10,725)

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Phase change thermal storage fibers with high latent heat have attracted significant attention in thermal management and heat storage. Through fiber encapsulation, shape-stable phase change materials can be prepared, thereby expanding their applications. In this study, electro-blown spinning was utilized to prepare phase change materials (PCM) using erythritol, with polyethylene oxide (PEO) as the carrier material. Coaxial thermal storage fibers encapsulating the phase change materials were prepared using polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP). The results indicate that the composite fibers have a smooth surface, uniform and smooth morphology, a maximum latent heat of 223.01 J/g, as well as excellent thermal stability. The coaxial fibers exhibit a distinct core–shell structure, with the coaxial fibers encapsulated with PVA as the shell material, demonstrating a high latent heat of 118.62 J/g, a residual rate of 93.81% after heating, and excellent thermal performance. The encapsulation efficiency is 53%, effectively addressing the issue of erythritol leakage. The research results provide valuable guidance for the efficient preparation of erythritol coaxial thermal storage fibers. Full article

Sphingosine 1-phosphate (S1P) is a potent bioactive sphingolipid that plays essential roles in regulating various immune responses, including lymphocyte trafficking, immune cell differentiation, and immunosurveillance. Different immune responses to S1P arise from the diverse Sphingosine 1-phosphate receptors (S1PRs) expressed on the cell surface, shaping unique, context-dependent responses to S1P. Beyond surface receptor engagement, intracellular S1P signaling is also being recognized as a crucial modulator of immune cell responses. Furthermore, the multifaceted S1P signaling axis has emerged as a key regulator of immune responses within the tumor microenvironment (TME), influencing both innate and adaptive immune cell behavior to facilitate tumor progression. A deeper mechanistic understanding of S1P signaling and its impact on immune cell fate is essential for developing novel therapeutic strategies to enhance anti-tumor responses. This review summarizes our current knowledge of how S1P influences immune cell function, with a specific focus on S1PR-dependent and S1PR-independent cellular signaling pathways. We also examine the alterations in immune cell responses that occur within the TME and current therapeutic strategies targeting S1P signaling. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

The molecular chaperone paradigm has shaped modern views of assisted protein folding, yet it does not fully capture the physical context in which de novo folding occurs in cells. A defining feature of the cellular milieu is macromolecular tethering in cis, whereby nascent polypeptides remain physically linked—through covalent or persistent associations—to ribosomes, lipid bilayers, or pre-folded domains of multidomain proteins. Because molecular chaperones have traditionally been defined as reversible binders acting in trans, this cis-acting mode has remained conceptually underappreciated. Cellular macromolecules, by virtue of their steric bulk and surface charges, can suppress aggregation of tethered polypeptides, thereby increasing productive folding yield. By analogy to colloidal stability, this repulsion-mediated control of aggregation suggests that cellular macromolecules can exhibit intrinsic chaperone-like activity largely independent of whether the linkage occurs in cis or in trans. This property provides a conceptual basis for linking cis- and trans-acting chaperoning. Thus, macromolecular tethering in cis may constitute a built-in layer of cellular chaperoning, distinct in physical linkage yet mechanistically related to conventional molecular chaperones. Full article

A major development direction for next-generation civil aircraft is to significantly reduce fuel consumption through the integration of high-bypass-ratio engines. However, the large diameter of high BPR engines will cause traditional aircraft to face the dilemma of ground clearance. The over-the-wing engine mount configuration avoids ground clearance constraints by installing the engines over the wings, which is conducive to the integration of high BPR engines. However, the sensitivity of the flow on the upper surface of the wing makes this configuration more likely to cause strong interference between the engine and the wing than the traditional configuration. During the design, the important interaction of the wing shapes, the wing static elastic deformation, the engine installation position and the engine inlet and exhaust effect should be fully considered, which brings great challenges to the traditional design method. An automatic multidisciplinary coupled optimization method based on the discrete adjoint approach and gradient-based optimization is proposed for this configuration. A corresponding framework is established based on the open-source multidisciplinary optimization platform OpenMDAO; the CFD solution and the adjoint solution are carried out using the open-source CFD solver DAFoam; the structural finite element solution and the structural adjoint solution are carried out using the open-source FEM solver TACS; and the engine power effect is solved by coupling the intake and exhaust boundary conditions into the CFD solver. This method can comprehensively consider the changes in the wing shapes, the static aeroelastic deformation of the wing, the intake and exhaust effects of the engine, and the positional movement of the engine along the spanwise, chordwise and vertical directions of the wing. The optimization results show that the optimized configuration eliminates the strong shock interaction between the nacelle and the wing, enhances the favorable pressure gradient on the upper surface of the wing, and reduces the drag by 9.51%, thereby demonstrating the effectiveness of the proposed multidisciplinary coupled adjoint optimization method for this configuration. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Here, we report a comparative scanning tunneling microscopy study of two brominated dibenzo[c,g]carbazole derivatives on Ag(111): 5,9-dibromo-7H-dibenzo[c,g]carbazole (DBC) and 5,9,7-tribromo-7-(4-bromobutyl)-7H-dibenzo[c,g]carbazole (BrBu-DBC). At room temperature (RT), DBC forms ordered paired-row supramolecular assemblies, whereas annealing to 470 K induces the formation of butterfly-like dimers that further organize into periodic arrays, consistent with adatom-mediated N–Ag–N coordination. In contrast, BrBu-DBC shows disordered adsorption at RT but transforms at 490 K into dumbbell-shaped dimers coupled selectively at the terminal side chains, consistent with C–C linkage formation. We demonstrate how subtle functional modification modulates the competition between supramolecular assembly and surface-mediated transformation pathways. Full article

This study proposes an improved retinal blood vessel segmentation method to enhance the diagnosis of microvascular retinal complications. The proposed method extracts local shape features from retinal images utilizing a fractional Hessian matrix, which models blood vessels as surface structures characterized by ridges and valleys resulting from variations in curvature. The methodology integrates adaptive principal curvature estimation with a new framework leveraging the fractional Hessian matrix with nonsingular and nonlocal kernels. The effectiveness of the suggested method is assessed using publicly accessible datasets, including DRIVE, HRF, STARE, and some real images obtained from a local hospital. The proposed segmentation achieves 96.77% accuracy and 98.82% specificity on the DRIVE database, 96.91% accuracy and 98.69% specificity on STARE, and 95.90% accuracy and 98.36% specificity on the HRF database. Optimal parameters for the fractional order and Gaussian standard deviation were empirically determined by maximizing segmentation accuracy. Our findings show that the proposed approach achieves competitive performance compared to the listed methods, including several deep learning approaches, while maintaining significant computational efficiency. The output of the suggested method can be further utilized with deep learning techniques, which will be applied in the clinical context of diabetic retinopathy and glaucoma to identify abnormalities likely related to disease progression and different stages. Full article

Optimizing the parameters of the manufacturing process for products in terms of metalworking equipment is one of the key tasks in technological preparation for production. This process is structurally complex, characterized by an ordered set of actions of various types. The basis for improving the efficiency of the technological process is the comprehensive optimization of the parameters of individual elements that form its structure. To solve this problem, an integrated model for comprehensive multi-criteria optimization of a structurally complex process has been developed, establishing a clear hierarchical relationship between its elements. The model is based on the structural decomposition of two processes: the process of forming individual design elements and the technological process of manufacturing a product. Structural hierarchical models have been developed for each process. The structure of the integrated model contains six levels of control. For each level of control, a set of target indicators and control parameters has been formed. The article presents the results of testing the proposed model using the example of optimizing the technological process of mechanical processing for the “Housing” product. As part of the study, structural and parametric optimization of the manufacturing process for this part was carried out. During the study, the structure of the technological processing route was optimized, as well as individual technological operations and technological transitions. Over the course of the work, the technological equipment and processing methods used for shaping a number of surfaces were replaced. As a result of the optimization, the overall labor intensity of the technological process for manufacturing the “Housing” product was reduced by 19.8%, and the manufacturing accuracy of the most critical surfaces was increased by 16.4%. The results confirm the effectiveness of the proposed model for comprehensive optimization of the mechanical processing technological process. Full article

Three-dimensional (3D) epicuticular wax coverage on plant surfaces contributes to multifunctional surface properties, such as enhanced water repellence, reduced pathogen adherence, modified optical properties, and reduced insect adhesion. The diversity in wax projection morphology, size, abundance, and spatial arrangement among plant species results in a broad spectrum of anti-adhesive effects, reflecting both phylogenetic history and ecological function. This study presents a numerical model consisting of 3D tubular-shaped structures randomly deposited on a substrate and forming a highly porous layer. The simulations based on this model demonstrate a strong reduction in adhesion to the contacting insect adhesive pad. It is found that a structure formed by sufficiently long tubes, where the length is enough to support the tubes in space and build a porous 3D structure with a very low density, at relatively weak attraction to the underlying substrate, leads to the weakest adhesion. The model is constructed on the basis of our recent works combining discrete and continuous approaches in biological modeling. It mainly exploits the technique of the movable digital automata, allowing modeling of numerous numerically elastic cylinders that can be moved in 3D space, elastically collide with one another and with boundaries, and build self-consistent surface structures, which can be used to mimic nano- or microscale surface coverages of real plants. Full article

This study aims to investigate the static performance of water-lubricated rubber bearings (WLRBs) with axial grooves. To achieve this objective, an analytical approach is employed that combines a modified Reynolds equation, accounting for surface groove effects and rubber deformation, with a Winkler model and finite element analysis of pressure distribution. By developing a fluid–structure interaction model that incorporates rubber liner deformation, this research reveals the interaction between WLRB geometry and steady-state performance parameters. The investigation evaluates the influence of geometric characteristics, including groove shape, number, and size, on the performance of elastomeric liner WLRBs, while assessing optimal groove depths under various conditions. The study analyzes five distinct groove geometries, including semi-cylindrical, rectangular prism, and three pyramidal types with different apex positions, in a six-groove bearing configuration, presenting their qualitative effects on the behavior of the examined bearings. The key findings indicate that increasing groove size or quantity reduces maximum pressure and load-carrying capacity while elevating friction coefficients. As groove count rises, supporting surfaces diminish, causing pressure distribution to intensify and minimum film thickness to decrease under a specified external load. A notable result reveals that when groove depth exceeds film thickness, performance becomes geometry-independent; however, shallower grooves exhibit significant geometric effects. Additionally, the study identifies groove ends as critical functional zones where film thickness reduction substantially enhances pressure distribution and static performance. Comparative analysis shows that longitudinal grooves with triangular cross sections outperform semi-circular and rectangular variants, with the backward triangular configuration demonstrating superior characteristics due to optimal end-film properties. In conclusion, this research provides a detailed understanding of how groove geometry influences the static performance of WLRBs, highlighting the importance of groove design, particularly at the groove ends, in optimizing bearing functionality. The findings offer valuable insights for the design and selection of groove configurations in water-lubricated rubber bearing applications. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article