Search Results (3,504)

The Xi’an Beilin Museum preserves a large collection of archeological photographic negatives and films dating from the 1950s to the early 1980s. These images document significant archeological discoveries, including Tang dynasty imperial tomb murals, the excavation of the terracotta warriors, and various historical grottoes and stone carvings. As unique visual records of cultural heritage, these materials provide valuable references for studying environmental deterioration processes and for guiding conservation and restoration practices. However, long-term storage under uncontrolled environmental conditions has resulted in severe degradation of the negatives, including mold contamination, emulsion layer powdering, deformation, and partial detachment. Among these deterioration phenomena, microbial growth is particularly destructive because fungal hyphae cause light scattering and image obscuration, preventing scanning and digital archiving. In this study, mold species present on the negatives were isolated and identified using morphological observation and ITS rDNA sequence analysis. Based on the characteristics of the microbial contamination, targeted removal and restoration treatments were applied to recover the original image information. Furthermore, preventive protection strategies were implemented through the development of antifungal storage materials and protective containers. The results establish an integrated conservation approach combining microbial identification, restoration treatment, risk elimination, and preventive protection, providing a scientific basis for the long-term preservation of historical photographic archives. Full article

The incorporation of plastic waste into cement-based materials offers a promising strategy for improving sustainability; however, it is often associated with reduced mechanical performance due to weak interfacial bonding. This study investigates the effect of metakaolin on the interfacial transition zone (ITZ) and mechanical properties of cement mortars modified with polyethylene terephthalate (PET) flakes used for the partial replacement of natural sand. Mortars containing 10 and 50 wt% metakaolin (as cement replacement) and 5 vol.% PET flakes (as sand replacement) were prepared and tested after 28 days of curing. Compressive and flexural strength were evaluated, and microstructural analysis was conducted using scanning electron microscopy (SEM) with a focus on the ITZ. The results indicate that the incorporation of PET flakes leads to a reduction in mechanical properties due to the formation of a porous and weak ITZ. However, the addition of 10 wt% metakaolin significantly improved mechanical properties, enabling PET-modified mortars to achieve strength comparable to the reference mix. SEM observations revealed that metakaolin contributed to the refinement of the microstructure and reduction in ITZ porosity, which enhanced interfacial bonding and improved stress transfer between PET particles and the cement matrix. These findings demonstrate that metakaolin can effectively mitigate the negative effects associated with PET incorporation by improving the microstructural characteristics of the ITZ, thereby enhancing the performance of sustainable cement-based composites. Full article

Elastic fibers are key components of the skin extracellular matrix and are essential for maintaining skin integrity and elasticity. During skin aging, particularly photoaging, elastic fiber integrity is progressively compromised by increased elastase activity and the downregulation of elastin and scaffold-related gene expression. Therefore, effective strategies to preserve elastic fiber function should address not only elastin synthesis but also enzymatic degradation and scaffold integrity. In this study, we investigated a multitarget approach to restoring the elastic fiber network by modulating elastin production, elastase activity, and scaffold protein expression. We found that Copper Tripeptide-1 enhanced elastin expression and secretion, ethyl ferulate inhibited elastase activity, and cedrol promoted scaffold-related gene expression and microfibrillar protein restoration in dermal fibroblasts. To assess the biological relevance of this approach, the combined treatment was evaluated using UV-damaged human skin biopsy samples. This combination effectively mitigated UV-induced elastic fiber disruption and significantly improved fiber architecture, as confirmed by immunofluorescence staining and scanning electron microscopy. These findings indicate that coordinated modulation of elastin production, proteolytic protection, and scaffold reinforcement is essential for maintaining elastic fiber integrity and represents a promising approach for preserving skin elasticity during aging. Full article

Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov–Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems. Full article

Multi-Robot Systems (MRS) demand optimal spatial resource configuration to ensure systemic efficiency in mission-critical applications. Conventional paradigms rely on rigid coverage-first principles, prioritizing exhaustive spatial scanning over rapid target discovery, thereby compromising systemic responsiveness. To bridge this gap, this study proposes the Attraction of Unknown area Centroid for Exploration (AUCE) architecture, a centralized framework designed to simultaneously optimize global exploration efficiency and early-stage target discovery rates. The control framework incorporates a dynamic region planning strategy that adaptively modulates the systemic search focus based on the specific field of view of autonomous agents, alongside an optimized S-shaped trajectory pattern to establish a rigorous balance between localized path simplicity and global coverage. A versatile profit function synthesizing constant and time-varying coefficient strategies explicitly regulates the systemic trade-off between accelerated early-stage target discovery and global path cost minimization. Quantitative simulations demonstrate that AUCE significantly outperforms established methods by mitigating redundant path costs and generating a distinct front-loading effect to accelerate target localization. Subsequent evaluations confirm the framework’s computational scalability in expanded swarms and its systemic adaptability when navigating static obstacles. Full article

This paper documents the preliminary observations and applications of a Ka-band cloud radar newly installed at the Hong Kong International Airport. A special scanning strategy of the cloud radar was developed and is described in detail. The radar provides reasonable cloud base height data as compared with a co-located laser ceilometer, by identifying the lowest vertical layer with reflectivity > −30 dBZ and at least 150 m thick, filtering measurements influenced by rainfall, and removing noise with differential reflectivity thresholds. As demonstrated in a heavy rain case study, the radar provides good estimates of the cloud top height as well, consistent with the cloud liquid water content profiles from a microwave radiometer. The various applications of the cloud radar are then explored, including (1) observations of supercooled liquid water in clouds associated with a late-season tropical cyclone in the South China Sea, (2) monitoring of low visibility in light rain or mist at the airport region using reflectivity as well as Doppler velocity data, and (3) monitoring severe weather such as windshear and turbulence to be encountered by departing aircraft due to low-level jets and initiation of heavy rain, using the Doppler velocity and spectrum data. These observations demonstrated the robustness in the cloud radar in the observation of high clouds and the applicability of the radar’s Doppler velocity in plan position indicator scans under light rain situations. Potential research with the radar, such as visibility maps, turbulence intensity maps, and automatic cloud observations, is also discussed. Full article

Accurate and real-time localization is a fundamental prerequisite for the autonomous navigation of mobile robots. LiDAR–Inertial Odometry (LIO) achieves high-precision state estimation and scene reconstruction in unknown environments by effectively fusing data from LiDAR and Inertial Measurement Units (IMU). However, conventional LIO methods typically rely solely on geometric features during point cloud registration. In complex scenarios, such as outdoor unstructured or dynamic environments, these methods are often susceptible to reduced localization accuracy due to geometric degeneration or mismatches. To address these challenges, we propose SV-LIO, A Probabilistic Adaptive Semantic Voxel Map for LiDAR–Inertial Odometry, which leverages point-wise semantic information from semantic segmentation to enhance registration accuracy and system robustness. Specifically, we construct a probabilistic adaptive semantic voxel map that extracts multi-scale spatial planes attached with semantic information. Building on this representation, we employ a semantic-guided strategy for nearest-neighbor plane association between LiDAR scans and the local map, and construct semantic-weighted point-to-plane residuals to constrain pose estimation. By jointly optimizing the IMU-propagated pose prior and semantic-guided LiDAR observation constraints, SV-LIO realizes high-precision real-time state estimation and semantic scene reconstruction. Extensive experiments on the KITTI dataset demonstrate that SV-LIO achieves significant improvements in both localization accuracy compared to state-of-the-art (SOTA) LIO methods, while also constructing semantic maps capable of providing rich environmental information. Full article

Background: Wolfram syndrome (WFS) is a rare neurodegenerative disease that is genetically determined and inherited in an autosomal recessive manner. Although the first clinical symptom appearing in early childhood is diabetes mellitus, subsequent symptoms are associated with optic nerve atrophy, followed by central nervous system atrophy. Methods: The aim of the study was to analyse magnetic resonance images (MRI) of the brain in combination with single-voxel magnetic resonance spectroscopy (MRS) and to assess the copy number of mitochondrial DNA (mtDNA-CN) in 10 patients with WFS compared with a control group of 17 healthy individuals. Results: A significant decrease in the amount of selected metabolites was observed in WFS patients compared to controls in all assessed brain regions (pons, cerebellum, white matter, thalamus, and hippocampus). For three metabolites, Glutamate (Glu), Glutamate + Glutamine (Glx) and total N-acetylaspartate (TNAA), significant differences in concentrations were found between the study groups in almost all matrices evaluating specific areas of the brain (p < 0.011), with the exception of a trend toward reduced TNAA in the hippocampus (p = 0.065). In addition, patients with WFS had a significant decrease in the mitochondrial-to-nuclear DNA ratio compared to controls (p < 0.0003). Some metabolites, such as N-acetylaspartate and total N-acetylaspartate, showed strong correlations with specific regions of the visual pathway on MRI scans in patients with WFS. Conclusions: Selected brain metabolites and mtDNA-CN may become potential markers of WFS, and the results of this study may be used to define indicators for future therapeutic strategies. Full article

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

Despite the growing adoption of Ti6Al4V-ELI made by Laser Powder Bed Fusion (LPBF) in tribologically demanding applications, the influence of hybrid nanoparticle additives on its lubrication behavior under starved contact conditions remains insufficiently explored. The tribological performance of Ti6Al4V was investigated under starved boundary-to-mixed lubrication conditions using engine oil modified with Ag-doped TiO₂ nanoparticles. Double-scan LPBF-fabricated discs were tested in a ball-on-disc configuration against AISI 52100 bearing steel using a TRB³ tribometer. Nanolubricants were prepared by dispersing TiO₂ and Ag–TiO₂ nanopowders with different Ag⁺/Ti⁴⁺ ratios (0.5%, 1.5%, and 2.5%) in SAE 10W-40 engine oil at a constant nanoparticle concentration of 0.05 wt%. Comprehensive physicochemical characterization of the nanopowders and nanolubricants was performed through structural, chemical, optical, morphological, rheological, and stability analyses. Tribological experiments were conducted following a full-factorial design combining three normal loads (5–15 N), three sliding speeds (0.10–0.20 m·s⁻¹), and four lubricant formulations. The steady-state coefficient of friction ranged between 0.281 and 0.359, while the specific wear rate varied from 2.81 × 10⁻⁴ to 4.83 × 10⁻⁴ mm³·N⁻¹·m⁻¹. The contact temperature rise remained relatively moderate, within the interval of 1.9–9.4 °C. Among the investigated formulations, the lubricant containing 1.5% Ag–TiO₂ exhibited the lowest friction coefficient, whereas the formulation with the highest Ag content showed improved stability of tribological performance across the investigated operating domain. These results indicate that Ag-modified TiO₂ nanoparticles are consistent with the formation of protective tribofilms and contribute to the stabilization of friction, wear, and thermal behavior under starved lubrication conditions. ANOVA confirmed that sliding speed and the load–lubricant interaction are the dominant factors governing friction and wear, while normal load controls the thermal response. These findings support the use of Ag–TiO₂ nanolubricants as a viable strategy for stabilizing interfacial behavior in LPBF-fabricated titanium components operating under starved lubrication conditions. Full article

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

To address the demand for wide-swath, high-resolution short-wave infrared (SWIR) imaging on resource-constrained spaceborne platforms, this study presents the design and on-orbit validation of a compact dual-channel push-broom (line-scanning) imaging system. The system adopts a transmissive optical architecture and a centralized, compact electronic control unit (ECU) configuration. By interleaving and mosaicking sixteen InGaAs linear array detectors, the system achieves an imaging swath of approximately 187 km and a nominal ground sampling distance of about 24 m, while maintaining a total instrument mass of 10.62 kg and a power consumption of approximately 12 W, thereby demonstrating a high level of integration and efficient resource utilization. To address focal plane consistency issues arising from multi-detector mosaicking, a closed-loop leveling method was developed using the modulation transfer function (MTF) as the primary performance metric. Through defocus estimation and quantitative correction of protrusions on a SiC substrate, convergence toward a unified confocal focal plane among multiple detectors was achieved. On-orbit image quality assessment indicates that the full width at half maximum (FWHM) of the line spread function (LSF) for both channels is approximately 1.38 pixels, with favorable signal-to-noise ratio (SNR) performance. These results validate the effectiveness of the proposed focal plane leveling strategy as well as the opto-mechanical-thermal design of the system. The proposed approach provides a practical pathway for the engineering implementation and consistency control of multi-detector mosaicked SWIR payloads under stringent resource constraints. Full article

Despite its potential to enhance construction quality, efficiency, and sustainability, modular construction continues to experience defects that hinder its broader adoption. Understanding and mitigating defects is essential for maximising the sustainability benefits of modular construction by reducing material waste, minimising rework and improving lifecycle performance. Existing research remains fragmented, with limited synthesis integrating defects with their root causes across the project lifecycle. To address this gap, this study investigates defect types, lifecycle-based causes, and mitigation strategies in modular building projects through a PRISMA-guided systematic literature review of 61 peer-reviewed journal articles published between 2015 and 2025 and retrieved from Scopus and Web of Science. Six major defect categories were identified: geometric and dimensional; material and component; joint and connection integrity; envelope performance and durability; structural; and mechanical, electrical, and plumbing (MEP) defects, with geometric and dimensional defects emerging as the most prevalent, accounting for 26.7% of reported cases. Lifecycle root-cause mapping indicates that poor workmanship during on-site assembly is the dominant contributor, accounting for 44.1% of identified root causes, with manufacturing errors (26.8%) and design limitations (13.4%) acting as critical upstream sources. Mitigation strategies cluster into three groups: general recommendations (39% of reported strategies), mainly focusing on low-cost organisational measures such as logistics coordination and workforce training; structured risk-management frameworks (9.1%), including assembly sequencing and tolerance planning; and digital and data-driven technologies (51.9%), such as laser scanning, AI-based inspection, and digital twins, enabling proactive quality assurance across the lifecycle. The study proposes an integrated lifecycle–defect–mitigation framework to strengthen quality governance and advance sustainable modular delivery. Full article

Background/Objectives: Non-sedate magnetic resonance imaging (MRI) can be challenging for young children with neuromotor disabilities, often resulting in motion-degraded images that complicate interpretation in the context of underlying neuropathology. This study aimed to characterize tolerance factors and barriers related to awake MRI of the pediatric brain and to examine additional considerations in analyzing structural scans affected by motion and pathology. Methods: 10 children (mean age 5y9m; 5 girls; GMFCS level IV) with cerebral palsy (CP) underwent non-sedate 3T MRI of the brain. Tolerance factors and challenges were documented. MRI quality and automated structural preprocessing with Freesurfer (FS) v.8.0 were reviewed by a pediatric neuroradiologist and neurologist. To assess the impact of motion, automated basal ganglia segmentation was compared with manual segmentation. Segmentation accuracy was characterized using Dice Coefficient (D). Results: Five participants (50%) tolerated non-sedate structural MRI, although two of them were unable to remain still. Factors affecting MRI tolerance included sensitivity to scanner noise (n = 4), hyperkinetic movement (n = 2), difficulty with positioning/padding (n = 4), fear of clinical environment (n = 2) or confined scanner interior (n = 2), and earbud discomfort (n = 3). Automated structural preprocessing with FS yielded discrepancies in gray-white matter boundaries in motion-degraded scans, necessitating manual correction. Automated segmentation of motion-compromised scans closely agreed with manual delineation of the caudate (D ≥ 0.85) and putamen (D ≥ 0.78), while the pallidum was least reproducible (D = 0.58). Conclusions: Tailored acquisition and processing strategies are necessary to support non-sedate MRI in children with CP, preserve downstream neuroimaging analyses, and promote inclusion of underrepresented populations in research. Full article