Search Results (239)

The growing demand from the automotive industry for lightweight, high-performance, and advanced manufacturing techniques for efficient and cost-effective production has accelerated the adoption of fiber-reinforced polymer composites. However, considering the manufacturing complexity of these materials, design remains challenging due to the intricate and interdependent relationships between the process conditions, the part geometry, and the resulting microstructure and quality. This research utilized the Autodesk Moldflow Insight software to design an injection molding process for the manufacturing of discontinuous glass fiber-reinforced polymer parts through computer modeling. A geometrically complex car fender was used as a case study. The effects of various process parameters, particularly gate locations, on the injection-molded parts’ properties (such as the fiber orientation, volumetric shrinkage, and shear rate) were investigated. Multiple injection molding process configurations were designed and simulated, including three, four, and five gates at varying locations. Based on the optimal performance (i.e., low shrinkage, a consistent fiber orientation, and a controllable shear rate), an optimal configuration with four gates at appropriate locations (corresponding to the second gate location set) was identified based on multicriteria decision-making analysis, i.e., volumetric shrinkage of ${8.5}_{-2.2}^{+1.4}\%$, a fiber orientation tensor of 0.927 ± 0.011, and a stable shear rate < 74,324 (1/s). A reduced strain closure model (modified Folgar–Tucker model) was used to predict the glass fiber orientation. A multicriteria decision-making technique, based on similarity ranking with an ideal solution, was employed to optimize the gate location. The simulation results clearly demonstrate that the gate placement is crucial for material behavior during molding and for reducing common defects. The simulation-based injection molding process design for the manufacturing of discontinuous fiber-reinforced polymer parts proposed in this paper can improve the production efficiency, reduce trial-and-error rates, and improve part quality. Full article

Human immunodeficiency virus (HIV-1) and SARS-CoV-2 continue to co-burden global health, motivating discovery of broad-spectrum small molecules. Conessine, a steroidal alkaloid, has reported membrane-active and antimicrobial properties but remains underexplored as a dual antiviral chemotype. To interrogate conessine’s multi-target antiviral potential against key enzymatic and entry determinants of HIV-1 and SARS-CoV-2 and to benchmark performance versus approved comparators. Eight targets were modeled: HIV-1 reverse transcriptase (RT, 3V81), protease (PR, 1HVR), integrase (IN, 3LPT), gp120–gp41 trimer (4NCO); and SARS-CoV-2 main protease (M^pro, 6LU7), papain-like protease (PL^pro, 6W9C), RNA-dependent RNA polymerase (RdRp, 7BV2), spike RBD (6M0J). Ligands (conessine; positive controls: dolutegravir for HIV-1, nirmatrelvir for SARS-CoV-2) were prepared with standard protonation, minimized, and docked using AutoDock Vina v 1.2.0exhaustiveness 4; 20 poses). Binding modes were profiled in 2D/3D. Protocol robustness was verified by re-docking co-crystallized ligands (RMSD ≤ 2.0 Å). Atomistic MD (explicit TIP3P, OPLS4, 300 K/1 atm, NPT; 50–100 ns) assessed pose stability (RMSD/RMSF), pocket compaction (Rg, volume), and interaction persistence; MM/GBSA provided qualitative energy decomposition. ADMET was predicted in silico. Conessine showed coherent, hydrophobically anchored binding across both viral panels. Best docking scores (kcal·mol⁻¹) were: HIV-1—PR −6.910, RT −6.672, IN −5.733; SARS-CoV-2—spike RBD −7.025, M^pro −5.745, RdRp −5.737, PL^pro −5.024. Interaction maps were dominated by alkyl/π-alkyl packing to catalytic corridors (e.g., PR Ile50/Val82, RT Tyr181/Val106; M^pro His41/Met49; RBD L455/F486/Y489) with occasional carbon-/water-mediated H-bonds guiding orientation. MD sustained low ligand RMSD (typically ≤1.6–2.2 Å) and damped RMSF at catalytic loops, indicating pocket rigidification; MM/GBSA trends (≈ −30 to −40 kcal·mol⁻¹, dispersion-driven) supported persistent nonpolar stabilization. Benchmarks behaved as expected: dolutegravir bound strongly to IN (−6.070) and PR (−7.319) with stable MD; nirmatrelvir was specific for M^pro and displayed weaker, discontinuous engagement at PL^pro/RdRp/RBD under identical settings. ADMET suggested conessine has excellent permeability/BBB access (high logP), but liabilities include poor aqueous solubility, predicted hERG risk, and CYP2D6 substrate dependence.Conessine operates as a hydrophobic, multi-target wedge with the most favorable computed engagement at HIV-1 PR/RT and the SARS-CoV-2 spike RBD, while maintaining stable poses at M^pro and RdRp. The scaffold merits medicinal-chemistry optimization to improve solubility and de-risk cardiotoxicity/CYP interactions, followed by biochemical and cell-based validation against prioritized targets. Full article

Rock mass characterization is crucial for evaluating slope stability and recommending effective prevention mechanisms. This study presents a comparative analysis of three approaches for discontinuity analysis: (1) conventional field survey, (2) digital manual measurement on 3D models generated with UAV-based photogrammetry, and (3) semi-automatic analysis based on clustering algorithms (K-NN) for point cloud segmentation. All three methods were applied to the same slope, allowing their performance to be evaluated in terms of accuracy, efficiency, and replicability. The results showed that the semi-automatic method achieved the highest coverage (81%) and identified 586 discontinuities, with RMSE values of 2.58° for orientation, 0.087 m for spacing, and 2.05 m for persistence, using the conventional method as a reference. The digital manual method, with 19% coverage, yielded very low error (RMSE of 3.27° for orientation, 0.012 m for spacing, and 0.063 m for persistence), validating it as a complementary and reliable alternative. In contrast, the conventional method required the longest execution time (10 h) and achieved only 19% coverage, being the least replicable due to its dependence on expert judgment. Overall, the comparison highlights the advantages of digital methods, especially the semi-automatic approach, in improving efficiency, safety, and replicability, while providing robust information to recommend prevention strategies for rock slope stability. Full article

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, variations in target orientation, and the challenges posed by non-cooperative targets (i.e., targets without cooperative transponders or external markers), limited viewpoint coverage results in a small-sample problem that severely constrains the application of deep learning to SAR image interpretation and target recognition. To address this issue, this paper proposes a multi-target, multi-view SAR image generation method based on conditional information and StyleGAN2, designed to generate high-quality, angle-controllable SAR images of typical targets from limited samples. The proposed framework consists of an angle encoder, a generator, and a discriminator. The angle encoder employs a sinusoidal encoding scheme that combines sine and cosine functions to address the discontinuity inherent in one-hot angle encoding, thereby enabling precise angle control. Moreover, the integration of SimAM and IAAM attention mechanisms enhances image quality, facilitates accurate angle control, and improves the network’s generalization to untrained angles. Experiments conducted on a self-constructed dataset of typical civilian targets and the SAMPLE subset of the MSTAR dataset demonstrate that the proposed method outperforms existing baselines in terms of structural fidelity and feature distribution consistency. The generated images achieve a minimum FID of 6.541 and a maximum MS-SSIM of 0.907, while target recognition accuracy improves by 6.03% and 7.14%, respectively. These results validate the feasibility and effectiveness of the proposed approach for SAR image generation and target recognition tasks. Full article

We introduce a novel technique for computing the periods of $(d,k)$-IETs based on Rauzy induction $\mathcal{R}$. Specifically, we establish a connection between the set of periods of an interval exchange transformation (IET) T and those of the IET $T^{\prime }$ obtained either by applying the Rauzy operator $\mathcal{R}$ to T or by considering the Poincaré first return map. Rauzy matrices play a central role in this correspondence whenever T lies in the domain of $\mathcal{R}$ (Theorem 4). Furthermore, Theorem 6 addresses the case when T is not in the domain of $\mathcal{R}$, while Theorem 5 deals with IETs having associated reducible permutations. As an application, we characterize the set of periods of oriented 3-IETs (Theorem 8), and we also propose a general framework for studying the periods of $(d,k)$-IETs. Our approach provides a systematic method for determining the periods of non-transitive IETs. In general, given an IET with d discontinuities, if Rauzy induction allows us to descend to another IET whose periodic components are already known, then the main theorems of this paper can be applied to recover the set of periods of the original IET. This method has been also applied to obtain the set of periods of all $(2,k)$-IETs and some $(3,k)$-IETs, $k\ge 1$. Several open problems are presented at the end of the paper. Full article

Isothermal hot compression tests were performed on an Al-4.8Cu-0.25Mg-0.32Mn-0.17Si alloy using a Gleeble-3500 thermomechanical simulator within the temperature range of 350–510 °C and strain rate range of 0.001–10 s⁻¹, achieving a true strain of 0.9. The constitutive equation and hot processing maps were established to predict the flow behavior of the alloy. The hot deformation mechanisms were investigated through microstructural characterization using inverse pole figure (IPF), grain boundary (GB), and grain orientation spread (GOS) analysis. The results demonstrate that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur during hot deformation. At high lnZ values (high strain rates and low deformation temperatures), discontinuous dynamic recrystallization (DDRX) dominates. Under middle lnZ conditions (low strain rate or high deformation temperature), both continuous dynamic recrystallization (CDRX) and DDRX are the primary mechanisms. Conversely, at low lnZ values (low strain rates and high temperatures), CDRX and geometric dynamic recrystallization (GDRX) become predominant. The DRX process in the Al-Cu-Mg alloy is controlled by the deformation temperature and strain rate. Full article

The study investigates the disconnect between formal urban planning standards and experiential walkability outcomes in Viranşehir, a planned neighborhood in Mersin, Türkiye. Although the area complies with national regulations on the provision of public services, it exhibits systemic limitations, including car-oriented street layouts, fragmented pedestrian networks, and underutilized public spaces. Employing a mixed-methods case study, the research integrates archival sources (aerial imagery, zoning plans, satellite data) with field observations to assess pedestrian environments. A light coding of sidewalk continuity, crossings, and edge conditions indicates that many streets are bounded by extensive inactive walls, protected crossings are absent along critical routes such as the school–park axis, and sidewalks are frequently narrow, obstructed, or discontinuous. These built-form features undermine safety, comfort, and social interaction despite formal regulatory compliance. The findings demonstrate how grid-pattern street systems prioritize vehicular mobility, while gated developments restrict permeability and diminish everyday encounters. In response, the study proposes a hierarchy of interventions: immediate measures such as school streets, protected crossings, and traffic calming, followed by medium- to long-term strategies including shaded seating, sidewalk widening, and participatory design guidelines. By linking statutory standards with lived experience, the paper conceptualizes walkability not only as a technical planning requirement but also as a socio-cultural right, offering transferable insights for the creation of more inclusive urban environments. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

Generative steganographic text covertly transmits hidden information through readable text that is unrelated to the message. Existing AI-based linguistic steganography primarily focuses on improving text quality to evade detection and therefore only addresses passive attacks. Active attacks, such as text tampering, can disrupt the symmetry between encoding and decoding, which in turn prevents accurate extraction of hidden information. To investigate these threats, we construct two attack models: the in-domain synonym substitution attack (ISSA) and the out-of-domain random tampering attack (ODRTA), with ODRTA further divided into continuous (CODRTA) and discontinuous (DODRTA) types. To enhance robustness, we propose a proactive adaptive-clustering defense against ISSA, and, for CODRTA and DODRTA, a post-hoc repair mechanism based on context-oriented search and the determinism of text generation. Experimental results demonstrate that these mechanisms effectively counter all attack types and significantly improve the integrity and usability of hidden information. The main limitation of our approach is the relatively high computational cost of defending against ISSA. Future work will focus on improving efficiency and expanding practical applicability. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterised by cognitive decline, synaptic loss, and multifaceted pathology involving amyloid-β (Aβ) aggregation, tau hyperphosphorylation, neuroinflammation, and impaired proteostasis. In recent years, biologic therapies, such as monoclonal antibodies, vaccines, antisense oligonucleotides (ASOs), and gene therapies, have gained prominence as promising disease-modifying strategies. In this review, we provide a comprehensive synthesis of current biologic approaches under clinical evaluation for AD. Drawing on data curated from ClinicalTrials.gov (as of 2025), we systematically summarise the molecular targets, therapeutic modalities, mechanisms of action, trial phases, and sponsors of over 60 biologic agents. These include Aβ-directed antibodies targeting distinct conformers such as protofibrils, pyroglutamate-modified species, and soluble oligomers; tau-targeted immunotherapies and RNA-based interventions; and emerging platforms focused on neuroimmune modulation, peptide hormones, and microbiota-based strategies. Gene and RNA therapeutics, particularly ASOs and small interfering RNAs (siRNAs) delivered intrathecally or via lipid nanoparticles, are also reviewed for their potential to modulate intracellular targets with high specificity. We also analyse the historical landscape of biologic candidates that failed to reach approval, discussing key reasons for trial discontinuation, including lack of clinical efficacy, safety concerns (e.g., amyloid-related imaging abnormalities), or inadequate biomarker responses. These cases offer crucial insights for refining future drug design. Looking ahead, we highlight major challenges and evolving perspectives in AD biologic therapy: expanding therapeutic targets beyond Aβ and tau, overcoming delivery barriers to the brain, designing prevention-oriented and genetically stratified trials, and navigating regulatory and ethical considerations. Together, these efforts signal a paradigm shift in AD drug development, from symptomatic treatment to mechanism-based precision biologics. By integrating real-time clinical trial data with mechanistic insight, this review aims to inform both translational research and therapeutic innovation in AD. Full article

Fiber orientation is an important descriptor of the microstructure for short fiber polymer composite materials where accurate and efficient prediction of the orientation state is crucial when evaluating the bulk thermo-mechanical response of the material. Macroscopic fiber orientation models employ the moment-tensor form in representing the fiber orientation state, and they all require a closure approximation for the higher-order orientation tensors. In addition, various models have more recently been developed to account for rotary diffusion due to fiber-fiber and fiber-matrix interactions which can now more accurately simulate the experimentally observed slow fiber kinematics in polymer composite processing. It is common to use explicit numerical initial value problem-ordinary differential equation (IVP-ODE) solvers such as the 4th- and 5th-order Dormand Prince Runge–Kutta (RK45) method to predict the transient and steady-state fiber orientation response. Here, we propose a computationally efficient method based on the Newton-Raphson (NR) iterative technique for determining steady state orientation tensor values by evaluating exact derivatives of the moment-tensor evolution equation with respect to the independent components of the orientation tensor. We consider various existing macroscopic-fiber orientation models and several closure approximations to ensure the robustness and reliability of the method. The performance and stability of the approach for obtaining physical solutions in various homogeneous flow fields is demonstrated through several examples. Validation of our orientation tensor exact derivatives is performed by benchmarking with results of finite difference techniques. Overall, our results show that the proposed NR method accurately predicts the steady state orientation for all tensor models, closure approximations and flow types considered in this paper and was relatively faster compared to the RK45 method. The NR convergence and stability behavior was seen to be sensitive to the initial orientation tensor guess value, the fiber orientation tensor model type and complexity, the flow type and extension to shear rate ratio. Full article

Spiritual intelligence (SI) is defined as a unique form of hermeneutic–relational intelligence that enables individuals to integrate cognitive, emotional, and symbolic dimensions to guide their thoughts and actions with reflection, aiming for existential coherence rooted in a transcendent system of meaning. It functions as a metacognitive framework that unites affective, cognitive, and symbolic levels in dialog with a sense of meaning that is considered sacred or transcendent, where “sacred,” in this context, refers inclusively to any symbolic reference or value that a person or culture perceives as inviolable, fundamental, or orienting. It can derive from religious traditions but also from ethical, philosophical, or civil visions. It functions as a horizon of meaning from which to draw coherence and guidance and which orients the understanding of oneself, the world, and action. SI appears as the ability to interpret one’s experiences through the lens of values and principles, maintaining a sense of continuity in meaning even during times of ambiguity, conflict, or discontinuity. It therefore functions as a metacognitive ability that brings together various mental functions into a cohesive view of reality, rooted in a dynamic dialog between the self and a value system seen as sacred. Full article

Discontinuous fibers are commonly added to matrix materials in additive manufacturing to enhance properties, but such benefits may be constrained by print and fiber orientation. The additive processes of forming rasters and layers in powder bed fusion inherently cause anisotropy in printed parts. Many print parameters, such as laser, temperature, and hatch pattern, influence the anisotropy of tensile properties. This study characterizes fiber orientation attributed to recoating non-encapsulated fibers and the resulting anisotropic tensile properties. Tensile and fracture properties of polyamide 12 reinforced with 0%, 2.5%, 5%, and 10% discontinuous carbon fibers by volume were characterized in two primary print/tensile loading orientations: tensile loading parallel to the recoater (“horizontal specimens”) and tensile load along the build axis (“vertical specimens”). Density and fractographic analysis indicate a homogeneous mixture with low porosity and primary fiber orientation along the recoating direction for both print orientations. Neat specimens (zero fiber) loaded in either direction have similar tensile properties. However, fiber-reinforced vertical specimens have significantly reduced consistency and tensile strength as fiber content increased, while the opposite is true for horizontal specimens. These datasets and results provide a mechanism to tune material properties and improve the functionality of selectively laser-sintered fiber-reinforced parts through print orientation selection. These datasets could be used to customize functionally graded parts with multi-material selective laser-sintering manufacturing. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

Faults and fracture sets in carbonate reservoirs are key geological features that govern hydrocarbon migration, accumulation, and wellbore stability. Their accurate detection is essential for structural interpretation, reservoir modeling, and drilling risk assessment. In this study, we propose an Optimally Oriented Coherence Attribute (OOCA) method that integrates geological guidance with multi-frequency structural analysis to achieve enhanced sensitivity to faults and fractures across multiple scales. The method is guided by depositional and tectonic principles, constructing model traces along directions with maximal structural variation to amplify responses at geological boundaries. A distance-weighted computation and extended directional model trace strategy are adopted to further enhance the detection of fine-scale discontinuities, overcoming the limitations of traditional attributes in resolving subtle structural features. A Gabor-based multi-frequency fusion framework is employed to simultaneously preserve large-scale continuity and fine-scale detail. Validation using physical modeling and field seismic data confirms the method’s ability to enhance weak fault imaging. Compared to traditional attributes such as C3 coherence, curvature, and instantaneous phase, OOCA delivers significantly improved spatial resolution. In zones with documented lost circulation, the identified structural features align well with drilling observations, demonstrating strong geological adaptability and engineering relevance. Overall, the OOCA method offers a geologically consistent and computationally efficient solution for high-resolution fault interpretation and drilling risk prediction in structurally complex carbonate reservoirs. Full article