Search Results (4,017)

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

In this study, TiO₂ nanoparticles (NPs), CdS NPs and TiO₂/CdS nanocomposite were synthesized via the sol–gel, hydrothermal and ex situ method, respectively. The synthesized materials were characterized using XRD, UV–vis DRS, FTIR, SEM, and EDX analysis. XRD analysis confirmed the crystalline structure of the as-prepared samples, while the bandgap energy of TiO₂ NPs, CdS NPs, and TiO₂/CdS nanocomposite were determined to be 2.98, 1.94, and 2.27 eV, respectively. Photocatalytic efficiency of TiO₂ NPs, CdS NPs, and TiO₂/CdS nanocomposite was systematically evaluated by photocatalytic degradation of crystal violet (CV) dye under visible-light irradiation. Under optimized reaction conditions of [CV concentration] = 20 mg/L, [catalyst dosage] = 0.25 g/L, and pH = 6, TiO₂/CdS nanocomposite achieved 86.3% removal of CV within 180 min, outperforming pure TiO₂ NPs (16.4%) and CdS NPs (66.9%). The enhanced performance of TiO₂/CdS nanocomposite as compared to CdS NPs is attributed to improved charge separation via heterojunction formation, while significantly superior performance over TiO₂ demonstrates successful visible-light activation. Further optimization study revealed that maximum removal efficiency of CV (97.1%) was achieved at lower dye concentration (10 mg/L). Photocatalytic degradation of CV followed pseudo-first-order kinetics. Moreover, scavenger experiments confirmed hydroxyl radicals (^•OH) as dominant reactive species. Furthermore, the TiO₂/CdS nanocomposite demonstrated good reusability with minimal activity loss after five runs. Additionally, the as-prepared nanocomposites showed significant antibacterial activity against Pseudomonas aeruginosa (P. aeruginosa). The present study indicated that TiO₂/CdS nanocomposite could be simultaneously used for degradation of organic pollutants as well as for removal of microorganisms while targeting environmental sustainability and water purification. Full article

Weak-echo ship target detection in GNSS-based bistatic synthetic aperture radar is severely limited by the coupled effects of burst-type strong windows and polarization mismatch, cross-frequency mis-registration, and long-sequence chain drift in dual-frequency dual-polarization observations. To address these issues, this paper proposes DFDP-QuadDiff, a dual-frequency dual-polarization quad-differential framework for weak-echo ship target detection using B1/B3 × horizontal–horizontal (HH)/vertical–vertical (VV) four-channel complex range-time data. The proposed framework integrates polarization-consistency-driven strong-window suppression, intra-band adaptive polarimetric synthesis, joint delay–Doppler–phase cross-frequency registration, segment-wise Jones drift calibration, and quality-aware final fusion in a unified hierarchical processing chain. In this way, multi-source inconsistencies are progressively constrained and suppressed from the polarization level to the segment level before final accumulation and detection are performed. Experimental results on self-developed four-channel GNSS-S demonstrate that, relative to the best raw single-channel result, the proposed framework increases the median SCR from 6.51 dB to 9.04 dB (+2.53 dB), improves the P10 SCR from −1.76 dB to 3.05 dB (+4.81 dB), and raises the track continuity from 0.85 to 0.97. In addition, the standard deviation of segment-wise delay drift is reduced from 0.97 bin to 0.29 bin, and positive multi-scale accumulation gains are maintained up to the second-long integration range. These results indicate that the proposed framework not only substantially enhances the stability, continuity, and long-time integrability of weak-target responses under low-SNR maritime conditions, but also maintains robust gains under weak-visibility, interference-dominant, and mismatch-sensitive local conditions in the stratified evaluation, thereby establishing a physically interpretable and implementation-ready solution for collaborative weak-target detection in dual-band dual-polarization GNSS-S. Full article

Increasing water demand and the rising complexity of wastewater matrices, driven by pharmaceuticals, personal care products, and recalcitrant industrial contaminants, require advanced catalytic solutions capable of efficient mineralization under sustainable conditions. Solar-driven processes have attracted growing attention; however, ultraviolet disinfection, heterogeneous photocatalysis, and photo-Fenton systems are commonly treated as independent approaches without mechanistic integration. This review presents a unified photonic–thermal catalytic framework for solar-driven wastewater treatment, emphasizing the interplay between photon absorption, charge-carrier separation, reactive oxygen species generation, and radical-mediated oxidation pathways. The contributions of ultraviolet, visible, and infrared radiation are analyzed in terms of catalyst activation, persulfate and ozone activation mechanisms, and temperature-enhanced reaction kinetics governed by Arrhenius behavior. Particular attention is given to photothermal effects that modulate surface reaction rates, mass transfer, and catalyst stability. By integrating mechanistic insights with reactor-level considerations, this work provides a rational basis for the design of robust solar catalytic systems with enhanced activity, selectivity, and scalability for real wastewater applications. Full article

This study explains how strategy–execution gaps become self-reinforcing during digital process transformation in layered manufacturing organizations. Drawing on an embedded qualitative process study of a large Chinese brewery’s transformation (2020–2024), we triangulate 10 semi-structured interviews across hierarchical levels with longitudinal public disclosures to reconstruct the initiative timeline and trace mechanisms across change phases. The analysis shows that platform-based process governance can scale faster than shared meaning and dialog, producing frontline sensemaking gaps and formalistic, top-down communication. These conditions thin employee voice and weaken feedback closure, which in turn erodes the legitimacy of organizational diagnosis and fragments implementation support. As interface problems are handled through local workarounds, management intensifies visibility-based monitoring, further suppressing voice and reinforcing the execution gap. We develop an organizational development process model that centers feedback closure and diagnosis legitimacy as bridging mechanisms linking soft change dynamics (meaning, trust, voice) with hard digital governance (process standards, data infrastructures, monitoring). The model offers actionable implications for leaders to build closure and legitimate diagnosis as operational capabilities throughout transformation. Full article

This study investigated injury severity in 18,528 police-reported vehicle-to-pedestrian crashes involving elderly pedestrians in legally classified local areas of South Korea during 2012–2021. Injury severity was coded into four ordered categories: fatal, serious, minor, and reported injury. To stabilize scenario extraction from a categorical crash database, an integrated screening workflow was applied, including near-zero-variance filtering, redundancy control among overlapping roadway encodings, representative-variable selection within redundant groups, and chi-square association checks. Classification and regression tree (CART) modeling was then used to identify rule-based combinations of environmental, roadway, driver, pedestrian, and vehicle factors associated with elevated severity, while tree complexity was controlled through cost-complexity pruning and 10-fold cross-validation. A scenario-based sensitivity analysis was further conducted to evaluate counterfactual shifts in severity distributions under targeted control of key conditions within representative high-risk scenarios. The results showed that severe outcomes were concentrated in stacked-risk combinations rather than in single factors alone. A dominant pathway involved nighttime conditions combined with maneuver-related driving contexts and speeding-related violations. High-fatality scenarios persisted even when speed-related predictors were excluded, underscoring the roles of nighttime exposure, visibility limitations, conflict-prone roadway settings, heavy-vehicle involvement, and pedestrian exposure behaviors. The proposed framework translates administrative crash records into concise, operationally interpretable scenarios and intervention-relevant evidence for local-area safety. Full article

Low temperature critically influences cellular metabolism by impairing processes such as membrane fluidity, enzyme activity, and protein folding. However, the comprehensive genetic landscape and regulatory mechanisms governing cold acclimation remain poorly understood. Here, we performed high-throughput, pooled genetic screening in the model alga Chlamydomonas reinhardtii (C. reinhardtii) to identify genes essential for cold acclimation. Our screening revealed numerous candidate genes implicated not only in early cold response pathways but also in core cellular processes, including DNA dynamics, protein homeostasis, metabolic regulation, and substrate transport. Notably, we identified a member of the RUS (ROOT UVB SENSITIVE) family, encoding a conserved DUF647 domain protein, designated CrRUS1. CRISPR-generated rus1 mutant alleles in C. reinhardtii display a phenotype consistent with our screening: the mutants did not exhibit any visible growth defects, but show severe growth defects at low temperature. Interestingly, the cold-induced phenotypic changes in rus1 can be reversed by dark conditions, suggesting that CrRUS1 likely promotes cold acclimation in C. reinhardtii through a light-dependent pathway. Our work provides novel genetic resources and mechanistic insights into cold acclimation in C. reinhardtii, with potential translational relevance for enhancing cold tolerance in crop species. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain $G_{\mathrm{c}\mathrm{a}\mathrm{p}}$ is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article

The European Union is increasingly confronted with a convergence of sustainability, democratic, and security-related challenges that affect the conditions for long-term transformation. While sustainable development and democratic resilience are often discussed separately, their interdependence has become more visible in the context of geopolitical instability, geoeconomic competition, hybrid threats, and growing societal polarization. This article examines the relationship between sustainable development and democratic resilience in the European Union and analyses how external pressures shape both agendas. The study employs a qualitative, concept-driven research design that combines the analysis of EU strategic and policy documents, a structured review of relevant scholarly literature, and triangulation with selected sustainability and governance indicators. The findings suggest that the implementation of sustainable development goals depends not only on regulatory and economic capacity, but also on social cohesion, public trust, and the resilience of democratic institutions, which together shape the legitimacy, continuity, and political feasibility of long-term transformative policies. At the same time, energy dependence, supply-chain vulnerabilities, technological dependencies, and information threats increasingly constrain the EU’s sustainability agenda. In response, the article proposes the concept of Sustainable Democratic Security as an analytical framework linking sustainability governance, democratic resilience, and strategic-security capacity. The article contributes to the literature by conceptualising these dimensions as mutually conditioning components of a common governance framework and by outlining their implications for integrated EU policymaking under conditions of geopolitical and geoeconomic pressure. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

Konjac glucomannan (KGM) is a naturally derived polysaccharide known for its biocompatibility and gel-forming ability and has gained increasing attention in biomaterial and drug delivery research. However, the rheological behavior of KGM gels at clinically relevant concentrations for periodontal use has not been thoroughly investigated. In this study, KGM gels at 0.8%, 1.0%, and 1.2% (w/v) were prepared and evaluated using oscillatory and steady shear rheology. Rheological analysis revealed increased viscoelastic strength with increasing polymer content, with the 1.2% formulation showing the highest storage modulus, viscosity, and shear stress values across strain, frequency, and temperature ranges. All formulations demonstrated pronounced shear-thinning behavior and dominant elastic characteristics (G′ > G″), indicating stable gel network formation and favorable injectability. The viscoelastic profile remained stable near physiological temperature (37 °C), implying that the gel network can preserve mechanical integrity under intraoral conditions. Gamma irradiation at 15 kGy effectively achieved sterility without visible macroscopic instability, although a qualitative reduction in viscosity was observed. Collectively, these findings indicate that increasing KGM concentration improves mechanical robustness and viscoelastic stability, with the 1.2% gel demonstrating the most favorable rheological profile for potential localized periodontal application. Full article

Egyptian papyri are commonly documented using high-resolution two-dimensional imaging, which enhances legibility but does not adequately capture the micrometric surface morphology required for material and conservation studies. To address this limitation, we developed and validated an integrated, fully non-contact imaging workflow combining Ultra-Close-Range Multiband Photogrammetry with Reflectance Transformation Imaging (RTI) and normal map integration. The protocol was tested on six papyrus fragments from the Museo Egizio di Torino (XXI Dynasty–Byzantine period) exhibiting different conservation conditions. Multiband photogrammetry in the visible and visible-induced infrared luminescence bands achieved a Ground Sample Distance of 17 µm/px and a point cloud density of approximately 170 points/mm², enabling detailed analysis of fiber morphology, surface deformation, and the spatial distribution of Egyptian blue. RTI-based normal map integration provided complementary high-frequency surface information with reduced acquisition and processing times. To overcome RTI low-frequency distortions, a revised normal integration strategy was implemented using surface planarization and frequency-domain fusion with photogrammetric data based on Power Spectral Density analysis. The resulting hybrid models combine metric reliability with enhanced surface detail, providing a scalable and non-invasive approach for papyrological documentation and conservation research. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

Sensitive Skin Syndrome (SSS) is a worldwide condition characterized by sensory symptoms such as stinging, burning, and itching, often without visible signs. This pilot study investigated individuals with self-reported SSS, focusing on the specific skin conditions, motivations and barriers for seeking medical attention. SSS individuals were divided into two groups: those who consulted a doctor (n = 16) and those who did not (n = 10). While SSS symptom severity was similar in both groups, those with greater severity were five times more likely to seek medical help. Key symptoms prompting consultations included morphological symptoms (papules, macules), sensory symptoms (itch, discomfort), and inflammatory symptoms (redness, rash). Notably, altered sensation and macules/papules showed the strongest trends towards influencing care-seeking behavior. Differences in anatomical sites affected were significant, with the head and face having the highest odds of doctor visits. Barriers to care included high specialist costs, travel distances, and a lack of remote consultation options, particularly for rural residents. Although treatments recommended by healthcare providers often fell short of expectations, partially effective options were more acceptable when endorsed by doctors. Subjects reported improvements within weeks of starting new treatments, though many remained only partially satisfied. This study highlights important aspects of SSS and its entanglement with other skin conditions, as well as how individuals navigate their symptoms and make treatment decisions amidst their sufferings. Full article