Search Results (252)

Background: There is growing interest in the potential role of manganese (Mn) in the development of Alzheimer’s Disease and related dementias (ADRD). Methods: In this nested pilot study of a ferroalloy worker cohort, we investigated the impact of chronic occupational Mn exposure on cognitive function through β-amyloid (Aβ) deposition and multi-omics profiling. We evaluated six male Mn-exposed workers (median age 63, exposure duration 31 years) and five historical controls (median age: 60 years), all of whom had undergone brain PET scans. Exposed individuals showed significantly higher Aβ deposition in exposed individuals (p < 0.05). The average annual cumulative respirable Mn was 329.23 ± 516.39 µg/m³ (geometric mean 118.59), and plasma Mn levels were significantly elevated in the exposed group (0.704 ± 0.2 ng/mL) compared to controls (0.397 ± 0.18 in controls). Results: LC-MS/MS-based pathway analyses revealed disruptions in olfactory signaling, mitochondrial fatty acid β-oxidation, biogenic amine synthesis, transmembrane transport, and choline metabolism. Simoa analysis showed notable alterations in ADRD-related plasma biomarkers. Protein microarray revealed significant differences (p < 0.05) in antibodies targeting neuronal and autoimmune proteins, including Aβ (25–35), GFAP, serotonin, NOVA1, and Siglec-1/CD169. Conclusion: These findings suggest Mn exposure is associated with neurodegenerative biomarker alterations and disrupted biological pathways relevant to cognitive decline. Full article

This review presents a comprehensive synthesis of recent advances in the tensile performance of adhesively bonded joints, focusing on applied aspects and modeling developments rather than providing a full theoretical analysis. Although many studies have addressed individual joint types or modeling techniques, an integrated review that compares joint configurations, modeling strategies, and performance optimization methods under tensile loading remains lacking. This work addresses that gap by examining the mechanical behavior of key joint types, namely, single-lap, single-strap, and double-strap joints, and highlighting their differences in stress distribution, failure mechanisms, and structural efficiency. Modeling and simulation approaches, including cohesive zone modeling, extended finite element methods, and virtual crack closure techniques, are assessed for their predictive accuracy and applicability to various joint geometries. This review also covers material and geometric enhancements, such as adherend tapering, fillets, notching, bi-adhesives, functionally graded bondlines, and nano-enhanced adhesives. These strategies are evaluated in terms of their ability to reduce stress concentrations and improve damage tolerance. Failure modes, adhesive and adherend defects, and delamination risks are also discussed. Finally, comparative insights into different joint configurations illustrate how geometry and adhesive selection influence strength, energy absorption, and weight efficiency. This review provides design-oriented guidance for optimizing bonded joints in aerospace, automotive, and structural engineering applications. Full article

In this work, we described the synthesis of 4-(methoxycarbonyl)phenyl 5-bromofuran-2-carboxylate by reacting 5-bromofuroic acid with methylparaben in the incorporation of DCC/DMAP (Steglich esterification) as coupling agents. Later on, we subsequently synthesized a series of 4-(methoxycarbonyl)phenyl 5-aryl furan-2-carboxylates (5a–5e) through Suzuki coupling catalyzed by palladium (0) between 4-(methoxycarbonyl)phenyl 5-bromofuran-2-carboxylate (3) with several substituted arylated and heteroaryl boronic acids (4). DFT calculations were computed to elucidate electronic structural features of synthesized molecules (5a–5e) and to validate these findings by correlating with theoretical and experimental spectroscopic analysis. Furthermore, geometrical optimization, thermodynamic features, as FMO orbitals, MESP maps, NLO behavior and reactivity descriptors, were also determined from the PBE0 D3BJ/def2-TZVP/SMD_1,4-dioxane theory level to confirm the structural features of synthesized molecules. Full article

With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

Nature’s principles offer design references for additive manufacturing (AM), enabling structures that achieve remarkable efficiency through hierarchical organization rather than material excess. This perspective article proposes a framework for integrating biomimetic principles into AM beyond morphological mimicry, focusing on functional adaptation and sustainability. By emulating biological systems like nacre, spider silk, and bone, AM utilizes traditional geometric replication to embed multifunctionality, responsiveness, and resource efficiency. Recent advances in the fields of 4D printing, soft robotics, and self-morphing systems demonstrate how time-dependent behaviors and environmental adaptability can be engineered through bioinspired material architectures. However, challenges in scalable fabrication, dynamic material programming, and true functional emulation (beyond morphological mimicry) necessitate interdisciplinary collaboration. In this context, the synthesis of biological intelligence with AM technologies offers sustainable, high-performance solutions for aerospace, biomedical, and smart infrastructure applications, once challenges related to material innovation and standardization are overcome. Full article

This study presents the synthesis, physicochemical characterization, and biological evaluation of a novel ternary nickel(II) complex with isoleucine and 1,10-phenanthroline ligands, [Ni(Phen)(Ile)₂]∙6H₂O, designed as a potential antitumor agent. Single-crystal X-ray diffraction revealed a monoclinic structure (C2-space group) with an octahedral Ni(II) coordination involving Phen and Ile ligands. A Hirshfeld surface analysis highlighted intermolecular interactions stabilizing the crystal lattice, with hydrogen bonds (H···H and O···H/H···O) dominating (99.1% of contacts). Density functional theory (DFT) calculations, including solvation effects (in water and methanol), demonstrated strong agreement with the experimental geometric parameters and revealed higher affinity to the water solvent. The electronic properties of the complex, such as HOMO−LUMO gaps (3.20–4.26 eV) and electrophilicity (4.54–5.88 eV), indicated a charge-transfer potential suitable for biological applications through interactions with biomolecules. Raman and infrared spectroscopic studies showed vibrational modes associated with Ni–N/O bonds and ligand-specific deformations, with solvation-induced shifts observed. A study using ultraviolet–visible–near-infrared absorption spectroscopy demonstrated that the complex remains stable in solution. In vitro cytotoxicity assays against MCF-7 (breast adenocarcinoma) and HCT-116 (colorectal carcinoma) cells showed dose-dependent activity, achieving 47.6% and 65.3% viability reduction at 100 μM (48 h), respectively, with lower toxicity to non-tumor lung fibroblasts (GM07492A, 39.8%). Supporting the experimental data, we performed computational modeling to examine the pharmacokinetic profile, with particular focus on the absorption, distribution, metabolism, and excretion properties and drug-likeness potential. Full article

Parallel robots, including cable-driven parallel robots (CDPRs), are widely used due to their high stiffness, precision, and high dynamic performance. However, their multi-chain closed-loop architecture brings nonlinear, multi-degree-of-freedom coupled motion and sensitivity to geometric errors, which result in significant challenges in their modeling, error compensation, and control. The rise in machine learning technology has provided a promising approach to address these issues by learning complex relationships from data, enabling real-time prediction, compensation, and adaptation. This paper reviews the progress of typical applications of machine learning methods in parallel robots, covering four main areas: kinematic modeling, error compensation, trajectory tracking control, as well as other emerging applications such as design synthesis, motion planning, and CDPR fault diagnosis. The key technologies used, their implementation architecture, technical difficulties solved, performance advantages and applicable scope are summarized. Finally, the review outlines current challenges and future directions. It is proposed that hybrid learning physics modeling, transfer learning, lightweight deployment, and interdisciplinary collaboration will be the key directions for advancing the integration of machine learning and parallel robotic systems. Full article

Background: Early childhood dental decay remains a pervasive chronic condition, and environmental toxicants—particularly lead—may exacerbate its development. This systematic review was designed to synthesize evidence on how lead exposure correlates with both the occurrence of carious lesions and aesthetic alterations in children’s primary teeth. Methods: A comprehensive search was conducted in PubMed, Scopus, and Web of Science through April 2025, selecting observational investigations that assessed the link between lead levels and primary-tooth decay in pediatric cohorts. Thirteen eligible studies, encompassing 44,846 participants aged 2–19 years, were included for qualitative synthesis. Aesthetics were screened using author-defined enamel-defect or discoloration endpoints; however, only three studies reported compatible metrics, precluding quantitative pooling. Heterogeneity in exposure matrices likewise ruled out meta-analysis. Results: Most studies reported a statistically significant association between higher lead burden and greater prevalence or severity of caries in primary teeth. Blood lead concentrations across studies ranged from means of 1.53 μg/dL to geometric means of 7.2 μg/dL. Notably, elevated lead was linked to increased decayed, missing, or filled surfaces—with an adjusted risk ratio of 1.14 (95% CI: 1.02–1.27) at levels below 5 μg/dL—and adjusted mean ratios of up to 2.14 for decayed or filled teeth when blood lead reached 5–10 μg/dL. Conclusions: Current evidence suggests that children’s exposure to lead may heighten the risk of caries and detract from the aesthetic quality of primary teeth. However, variability in study design, lead quantification methods, and confounder adjustment limit the consistency of findings. Mitigating lead exposure in early life could represent a valuable preventive strategy against dental decay in susceptible pediatric populations. Full article

Three-Dimensional Gaussian Splatting (3DGS) has revolutionized novel-view synthesis, enabling real-time rendering of high-quality scenes. Two-Dimensional Gaussian Splatting (2DGS) improves geometric accuracy by replacing 3D Gaussians with flat 2D Gaussians. However, the flat nature of 2D Gaussians reduces mesh quality on volumetric surfaces and results in over-smoothed reconstruction. To address this, we propose Dual-Dimensional Gaussian Splatting (DDGS), which integrates both 2D and 3D Gaussians. First, we generalize the homogeneous transformation matrix based on 2DGS to initialize all Gaussians in 3D. Subsequently, during training, we selectively convert Gaussians into 2D representations based on their scale. This approach leverages the complementary strengths of 2D and 3D Gaussians, resulting in more accurate surface reconstruction across both flat and volumetric regions. Additionally, to mitigate over-smoothing, we introduce gradient-based regularization terms. Quantitative evaluations on the DTU and TnT datasets demonstrate that DDGS consistently outperforms prior methods, including 3DGS, SuGaR, and 2DGS, achieving the best Chamfer Distance and F1 score across a wide range of scenes. Full article

To address the deployment accuracy issues of multi-frequency band reflector antennas, this study takes a hexagonal prism modular deployable antenna as an example and proposes an accuracy design method. This paper proposes a screw-theory-based sub-chain precision analysis method. This method constructs a virtual screw model of rod length errors and hinge gap errors. Based on geometric relationships, a multi-loop point position error model is established, and accuracy surfaces considering rod length errors and hinge gap are output using MATLAB R2024b. By outputting the relationship curves of single-rod errors relative to point errors, the linearized influence law of individual rods on precision is further elucidated. Simulation results demonstrate the reliability of the error modeling theory. Based on the established cost-effective precision model and the minimum point error, which is obtained by using the numerical iterative method, the optimal solution for error parameters is obtained. Full article

A well-known and powerful convex optimization strategy is exploited to enhance the electromagnetic performance of the Square Kilometer Array Low-Frequency radio telescope. The proposed method minimizes the peak sidelobe level while ensuring full control of the receiving pattern across the entire angular domain. The approach is validated through full-wave simulations that incorporate realistic embedded element patterns, demonstrating significant improvements in sidelobe suppression despite the geometric constraints of the array structure. The achieved results underscore the method’s potential for high-performance beam synthesis in large-scale radio astronomy arrays. Full article

This paper advances the foundations of tensor and category theories by introducing novel concepts and rigorous constructive proofs. We generalize tensor theory through the innovative notion of a generalised tensor index, a versatile framework that unifies diverse tensor indices, and explore its transformation properties. Using fractional derivatives, we provide a geometrical interpretation of these generalised tensors, revealing new insights into its structure. Additionally, we forge a deep connection between tensor and category theories, integrating sets, tensors, categories, and functors with extensions like partial differentiation and integration. This synthesis yields original constructs—setorial tensors, categorial tensors, and functorial tensors—which open uncharted pathways in mathematical analysis. Our contributions not only extend prior research but also significantly enhance tensor theory, category theory, set theory, logic, topology, algebraic geometry, foundations, and philosophy, with potential applications spanning physics, geometry, and beyond. Full article

One of the quintessential goals of musical instrument acoustics is to improve the perceived sound produced by, e.g., a violin. To achieve this, the connections between physical (mechanical and geometrical) properties and perceived sound output need to be understood. In this article, a single facet of this complex problem will be discussed using experimental results obtained for six violins of varying back arch height. This is the first investigation of its kind to focus on back arch height. It may serve to inform instrument makers and researchers alike about the variation in sound that can be achieved by varying this parameter. The test instruments were constructed using state-of-the-art methodology to best represent the theoretical case of changing back arch height on a single instrument. Three values of back arch height ($12.1$, $14.8$ and $17.5$ mm) were investigated. The subsequent perceptual tests consisted of a free sorting task in the playing situation and three two-alternative forced choice listening tests. The descriptors “round” and “warm” were found to be linked to back arch height. The trend was non-linear, meaning that both low- and high-arch height instruments were rated as possessing more of these descriptors than their medium-arch height counterparts. Additional results were obtained using stimuli created by hybrid synthesis. However, these could not be linked to those using real playing or recordings. The results of this study serve to inform violin makers about the relative importance of back arch height and its specific influence on sound output. The discussion of the applied methodology and interpretation of results may serve to inform researchers about important new directions in the field of musical instrument acoustics. Full article

AgCl microcrystals are used in visible light photocatalysis. However, their properties depend strongly on the morphology of the crystals and the degree of exposure of the crystal planes. Despite extensive research conducted on the synthesis of AgCl microcrystals, the majority of existing studies have focused on the stable growth of crystals. The role of Cl⁻ ions concentration as a key factor controlling the microcrystals morphology has not been fully explored, which limits the precise tuning of the morphology of AgCl microcrystals. In this study, AgCl microcrystals with controllable morphology are successfully synthesized by a facile solvothermal method. During the preparation process, ethylene glycol (EG) is utilized as a solvent, while polyvinylpyrrolidone (PVP) is employed as a surfactant. We systematically investigate the etching mechanism of AgCl microcrystals by analyzing the effect of sodium chloride (NaCl) concentration on their morphology. This investigation involves the integration of diverse characterization methods, including scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and geometrical struc-ture analysis. The results demonstrate that Cl⁻ functions as both a surfactant, thereby promoting the nucleation of cubic microcrystals, and as an etchant, selectively etching the crystal surface. The order of selective etching on the crystal surface follows (100) planes > (110) planes > (111) planes. Based on this new mechanism, AgCl microcrystals with various morphologies, such as cube, octopod and dendrite, are successfully prepared, which provides a new idea for the precise design of noble metal halide microcrystals. Full article