Search Results (552)

The long-distance high-voltage transmission tower-line system, frequently traversing active fault zones, is vulnerable to severe symmetry-breaking damage during earthquakes due to asymmetric permanent ground displacements. However, the seismic performance of such systems, particularly concerning symmetry-breaking effects caused by asymmetric fault displacements, remains inadequately studied. This study investigates the symmetry degradation mechanisms in a 1:40 scaled 500 kV tower-line system subjected to cross-fault ground motions via shaking table tests. The testing protocol incorporates representative fault mechanisms—strike-slip and normal/reverse faults—to systematically evaluate their differential impacts on symmetry response. Measurements of acceleration, strain, and displacement reveal that while acceleration responses are spectrally controlled, structural damage is highly fault-type dependent and markedly asymmetric. The acceleration of towers without permanent displacement was 35–50% lower than that of towers with permanent displacement. Under identical permanent displacement conditions, peak displacements caused by normal/reverse motions exceeded those from strike-slip motions by 50–100%. Accordingly, a fault-type-specific amplification factor of 1.5 is proposed for the design of towers in dip-slip fault zones. These results offer novel experimental insights into symmetry violation under fault ruptures, including fault-specific correction factors and asymmetry-resistant design strategies. However, the conclusions are subject to limitations such as scale effects and the exclusion of vertical ground motion components. Full article

Accelerated artificial aging of zinc oxide (ZnO)-based acrylic artists’ paint, filled with calcium carbonate (CaCO₃) as an extender, was carried out for a total of 1963 h (~8 × 10⁷ lux·h), with assessments at specific intervals. The total color difference ΔE* was <2 (CIELab-76 system) over 1725 h of aging, while the human eye notices color change at ΔE* > 2. Oxidative degradation of organic components in the paint to form volatile products was revealed by attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, micro-Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). It appears that deep oxidation of organic intermediates and volatilization of organic matter may be responsible for the relatively small value of ΔE* color difference during aging of the samples. To elucidate the degradation pathways, principal component analysis (PCA) was applied to the spectral data, revealing: (1) the catalytic role of ZnO in accelerating photodegradation, (2) the Kolbe photoreaction, (3) the decomposition of the binder to form volatile degradation products, and (4) the relative photoinactivity of CaCO₃ compared with ZnO, showing slower degradation in areas with a higher CaCO₃ content compared with those dominated by ZnO. These results provide fundamental insights into formulation-specific degradation processes, offering practical guidance for the development of more durable artist paints and conservation strategies for acrylic artworks. Full article

Alpha-synuclein (αsyn) misfolding and aggregation underlie several neurodegenerative disorders, including Parkinson’s disease. Early oligomeric intermediates are particularly toxic yet remain challenging to detect and characterize within cellular systems. Here, we employed the luminescent conjugated oligothiophene h-FTAA to investigate early aggregation events of human wildtype (huWT) and A53T-mutated αsyn (huA53T) both in vitro and in HEK293 cells stably expressing native human-αsyn. Comparative fibrillation assays revealed that h-FTAA detected αsyn aggregation with higher sensitivity and earlier onset than Thioflavin T, with the A53T variant displaying accelerated fibrillation. HEK293 cells stably expressing huWT- or huA53T-αsyn were exposed to respective pre-formed fibrils (PFFs), assessed via immunocytochemistry, h-FTAA staining, spectral emission profiling, and fluorescence lifetime imaging microscopy (FLIM). Notably, huA53T PFFs promoted earlier aggregation patterns and yielded narrower fluorescence lifetime distributions compared with huWT PFFs. Spectral imaging showed h-FTAA emission maxima (~550–580 nm) red-shifted and broadened in cells along with variable lifetimes (0.68–0.87 ns), indicating heterogeneous aggregate conformations influenced by cellular milieu. These findings demonstrate that h-FTAA is useful for distinguishing early αsyn conformers in living systems and, together with stable αsyn-expressing HEK293 cells, offers a platform for probing early αsyn morphotypes. Taken together, this opens for further discovery of biomarkers and drugs that can interfere with αsyn aggregation. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

This study proposes the first ground motion prediction equation (GMPE) for the parameter I_Np, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. I_Np also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T₁). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of I_Np for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity. Full article

Evaluation of seismic risk by capturing the influences of strong motion duration and frequency contents of ground motion through probabilistic approaches is the main element of this study. Unlike most existing studies that mainly focus on intensity measures such as peak ground acceleration or spectral acceleration, this work highlights how duration and frequency characteristics critically influence dam response. To achieve this, a total of 45 ground motion records, categorized by strong motion duration (long, medium, and short) and frequency content (low, medium, and high), were selected from the PEER database. Nonlinear numerical dynamic analysis was performed by scaling each ground motion from 0.05 g to 0.5 g, with the drift ratio at the dam crest used as the Engineering Demand Parameter. It is revealed that long-duration and low-frequency ground motions induced significantly higher drift demands. The fragility analysis was conducted using a lognormal distribution considering extensive damage threshold drift ratio. Finally, the probabilistic seismic risk was carried out by integrating the site-specific hazard curve and fragility curves which yield the height risk for long durations and low frequencies. The outcomes emphasize the importance of ground motion strong duration and frequency in seismic performance and these findings can be utilized in the dam safety evaluation. Full article

The extensive use of chemical preservatives in the food industry has raised concerns over their association with gut microbiota imbalance, allergenic reactions, and potential carcinogenicity. Growing consumer demand for “clean label” products, coupled with regulatory pressures, has accelerated the search for safer and more sustainable alternatives. In this study, it is reported for the first time that the synthesis of AIEE-type Supra-CDs using p-phenylenediamine (p-PA) and thiourea (TU), a breakthrough that provides a new class of nanomaterials with superior optical and antimicrobial properties. More importantly, the study demonstrates a quantitative improvement of spectral overlap through controllable inner filter effect (IFE), establishing a reliable strategy to enhance detection sensitivity and broaden applicability in food safety monitoring. Beyond their intrinsic antimicrobial potential, these Supra-CDs integrate seamlessly with intelligent detection platforms such as biosensors, CRISPR-based assays, and AI-assisted analytics, enabling real-time evaluation of probiotic- and postbiotic-based preservation systems. By combining novel material synthesis with precision monitoring technologies, this work offers a dual innovation: reducing reliance on synthetic additives while providing scalable tools for sustainable food preservation. The findings not only advance the frontier of biopreservation research but also align with global initiatives for consumer health and environmental sustainability. Full article

It was previously demonstrated that timolol (TIM), naphazoline (NAPH), and diflunisal (DIF) are susceptible to degradation when exposed to extreme pH conditions and UV/Vis light. However, their stability in the presence of pharmaceutical excipients remains largely unexplored. Thus, their binary mixtures (1:1 ratio, w/w) with five excipients, hydroxyethyl cellulose (HCA), mannitol (MAN), poly(vinyl alcohol) (PVA), poly(vinylpyrrolidone) (PVP), and Tris HCl (TRIS), were subjected to forced degradation (70 °C/80% RH and UV/Vis light in the dose 94.510 kJ/m²). Forced degradation was designed to accelerate potential interactions between these compounds, allowing the earlier identification of degradation risk compared to formal stability studies. FT-IR/ATR and NIR spectroscopy, along with chemometric evaluation using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), was applied to assess changes in the spectra, compared to individual compounds and the non-stressed mixtures. A hybrid approach, combining visual assessment with chemometric evaluation of the spectral data, enabled the detection of changes that were not clearly observable using a single analytical method. In particular, interactions of TIM, NAPH, and DIF with MAN and TRIS were clearly identified, while the mixtures of NAPH with excipients proved to be the least sensitive to forced degradation. Full article

Deep learning-based water body extraction methods generally focus on maximizing accuracy while neglecting inference speed, which can make them challenging to apply in real-time applications. To address this problem, this paper proposes a lightweight u-shaped network (LU-Net), which improves inference speed while maintaining comparable accuracy. To reduce inference latency, a lightweight decoder block (LDB) is designed, which employs a depthwise separable convolution structure to accelerate the decoding process. To enhance accuracy, a lightweight convolutional block attention module (LCBAM) is designed, which effectively captures water-specific spectral and spatial characteristics through a dual-attention mechanism. To improve multi-scale water boundary extraction, a structurally re-parameterized multi-scale fusion prediction module (SRMFPM) is designed, which integrates multi-scale water boundary information through convolutions of different sizes. Comparative experiments are conducted on the GID and LoveDA datasets, with model performance assessed using the MIoU metric and inference latency. The results demonstrate that LU-Net achieves the lowest GPU latency of 3.1 MS and the second-lowest CPU latency of 36 MS in the experiments. On the GID, LU-Net achieves the MIoU of 91.36%, outperforming other tested methods. On the LoveDA datasets, LU-Net achieves the second-highest MIoU of 86.32% among the evaluated models, which is 0.08% lower than the top-performing CGNet. Considering both latency and MIoU, LU-Net demonstrates commendable efficiency on the GID and LoveDA datasets across all compared networks. Full article

The peregrine falcon, known as the fastest bird in the world, has been studied for its ability to stabilize during high-speed dives, a capability attributed to the configuration of its dorsal feathers. These feathers have inspired the design of vortex generators devices that promote controlled turbulence to delay boundary layer separation on aircraft wings and turbine blades. This study presents an experimental wind tunnel investigation of a bio-inspired peregrine falcon prototype, equipped with movable artificial feathers, a hot-wire anemometer, and a 3D accelerometer. Wake velocity profiles measured behind the prototype revealed fluctuations associated with feather motion. Spectral analysis of the velocity signals, recorded with oscillating feathers at a wind tunnel speed of 10 m/s, showed attenuation of specific frequency components, suggesting that feather dynamics may help mitigate wake fluctuations induced by structural vibrations. Three-dimensional acceleration measurements indicated that prototype vibrations remained below 1 g, with peak differences along the X and Z axes ranging from −0.06 g to 0.06 g, demonstrating the sensitivity of the vibration sensing system. Root Mean Square (RMS) values of velocity signals increased with wind tunnel speed but decreased as the feather inclination angle rose. When the mean value was subtracted from the signal, higher RMS variability was observed, reflecting increased flow disturbance from feather movement. Fast Fourier Transform (FFT) analysis revealed that, for fixed feather angles, spectral magnitudes increased uniformly with wind speed. In contrast, dynamic feather oscillation produced distinctive frequency peaks, highlighting the feather’s influence on the wake structure in the frequency domain. To complement the experimental findings, 3D CFD simulations were conducted on two HAWT-type wind turbines—one with bio-inspired vortex generators and one without. The simulations showed a significant reduction in turbulent kinetic energy contours in the wake of the modified turbine, particularly in the Y-Z plane, compared to the baseline configuration. Full article

This study addresses the critical need for region-specific ground motion selection methods in the Xi’an area by proposing a novel Peak Ground Acceleration (PGA)-based target spectrum, developed through the relationship between PGA and response spectrum attenuation. Based on the relationship between the peak ground acceleration (PGA) and the attenuation of the response spectrum, a PGA target spectrum applicable to the Xi’an area was studied. Using the PGA and code-specified Standard Spectrum as the target spectrum, earthquake records were selected and their amplitudes scaled by employing various methods, including fundamental period scaling, average spectral ratio scaling, equal spectral intensity scaling, minimum squared error scaling, and minimum moving average scaling. Based on the 70 ground motion records obtained from the five scaling methods applied to the two target spectra (grouped into 10 distinct sets), the dynamic time history analysis was conducted for a frame-core tube structure; the effects of different scaling methods and different target spectra were investigated on the dispersion of floor displacement, interstory drift ratio and interstory shear. The results show that the spectral value of the PGA target spectrum at the peak is 54% higher than that of the code-specified spectrum, while in the long-period range, the acceleration values of the PGA target spectrum are comparatively smaller, only about 50% of those of the code spectrum. For structural response, the code target spectrum is more conservative compared with the PGA target spectrum, and the minimum moving average method is the least sensitive to different target spectra. This work offers preliminary insights that may contribute to the optimization of ground motion selection in regionally tailored seismic design practices. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

The Fourier decomposition technique has notable advantages in filtering vibration acceleration signals and enhances the feasibility of frequency-domain mode decomposition. To improve the accuracy of mode extraction, this paper proposed a novel Fourier decomposition technique based on spectral clustering. The methodology comprises three key steps. First, spectral clustering is performed using feature vectors derived from the spectrum envelope, specifically the frequency and amplitude of its maximum value, along with the average amplitude of local spectral peaks. Subsequently, the spectrum is adaptively segmented based on clustering feedback to determine spectral segmentation boundaries. Followed by this, a filter bank is constructed via Fourier decomposition for signal reconstruction. Finally, a harmonic correlation index is computed for all decomposed components to identify fault-sensitive modes exhibiting the highest diagnostic relevance. These selected modes are subsequently subjected to demodulation for fault diagnosis. The effectiveness of the proposed method is validated through both simulated signals and experimental datasets, demonstrating its improved ability to capture critical fault information. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

To accurately assess the seismic damage of high-rise structures under long-period ground motions (LPGMs), a 36-story SRC frame-RC core tube high-rise structure was designed. Twelve groups of LPGMs and twelve groups of ordinary ground motions (OGMs) were selected and bidirectionally input into the structure. The spectral acceleration S_90c considering the effect of higher-order modes was adopted as the intensity measure (IM) of ground motions, and the maximum inter-story drift angle θ_max under bidirectional ground motions was taken as the engineering demand parameter (EDP). Structural Incremental Dynamic Analysis (IDA) was conducted, the structural vulnerability was investigated, and seismic vulnerability curves, damage state probability curves, vulnerability index curves, as well as the probabilities of exceeding performance levels and vulnerability index of the structure during 8-degree frequent, design, and rare earthquakes were obtained, respectively. The results indicate that structural damage is significantly aggravated under LPGMs, and the exceeding probabilities for all performance levels are greater than those under OGMs, failing to meet the seismic fortification target specified in the code. When encountering an 8-degree frequent earthquake, the structure is in a moderate or severe damage state under LPGMs and is basically intact or in a slight damage state under OGMs. When encountering an 8-degree design earthquake, the structure is in a severe damage or near-collapse state under LPGMs and is in a moderate damage state under OGMs. When encountering an 8-degree rare earthquake, the structure is in a near-collapse state under LPGMs and in a severe damage state under OGMs. Full article