Search Results (1,077)

Wildfires, which are distinguished by their destructive nature and challenging suppression, present a significant threat to ecological environments and socioeconomic systems. In order to address this issue, the development of efficient and accurate fire detection technologies for early warning and timely response is essential. This paper addresses the complexity of forest and mountain fire detection by proposing YOLO-CSQ, a drone-based fire detection method built upon an improved YOLOv8 algorithm. Firstly, we introduce the CBAM attention mechanism, which enhances the model’s multi-scale fire feature extraction capabilities by adaptively adjusting weights in both the channel and spatial dimensions of feature maps, thereby improving detection accuracy. Secondly, we propose an improved ShuffleNetV2 backbone network structure, which significantly reduces the model’s parameter count and computational complexity while maintaining feature extraction capabilities. This results in a more lightweight and efficient model. Thirdly, to address the challenges of varying fire scales and numerous weak emission targets in mountain fires, we propose a Quadrupled-ASFF detection head for weighted feature fusion. This enhances the model’s robustness in detecting targets of different scales. Finally, we introduce the WIoU loss function to replace the traditional CIoU object detection loss function, thereby enhancing the model’s localization accuracy. The experimental results demonstrate that the improved model achieves an mAP@50 of 96.87%, which is superior to the original YOLOV8, YOLOV9, and YOLOV10 by 10.9, 11.66, and 13.33 percentage points, respectively. Moreover, it exhibits significant advantages over other classic algorithms in key evaluation metrics such as precision, recall, and F1 score. These findings validate the effectiveness of the improved model in mountain fire detection scenarios, offering a novel solution for early warning and intelligent monitoring of mountain wildfires. Full article

One-stage synthesis technology for preparing carbon adsorbents with tailored porosity from agricultural waste is worthwhile due to their extensive application value. Thermal gravimetric analysis, low-temperature N₂ adsorption, X-ray diffraction (XRD), small-angle X-ray scattering (SAXS), and Raman spectroscopy were used to record the structure transformations of carbon materials, namely pore development, proceeding in the course of the step-wise pyrolysis of renewable and low-cost raw materials such as walnut shells (WNSs), which was carried out within a temperature range of 240–950 °C in a CO₂ flow. The minimum threshold carbonization temperature for preparing nanoporous carbon materials from WNSs, determined by the examination of the N₂ adsorption data, was 500 °C. The maximum specific micropore volume and BET surface achieved in the process without holding a material at a specified temperature were only 0.19 cm³/g and 440 m²/g, respectively. The pyrolysis at 400–600 °C produced amorphous sp² carbon. At a temperature as high as 750 °C, an increase in the X-ray reflection intensity indicated the ordering of graphite-like crystallites. At high burn-off degrees, the size of coherently scattering domains becomes smaller, and an increased background in X-ray patterns indicates the destruction of cellulose nanofibrils, the disordering of graphene stacks, and an increase in the amount of disordered carbon. At this stage, pores develop in the crystallites. They are tentatively assigned to crystallites with sizes of 15–20 nm and to micropores. According to the Raman spectra combined with the XRD and SAXS data, the structure of all the pyrolysis products is influenced by the complex structure of the walnut shell precursor, which comprises cellulose nanofibrils embedded in lignin. This structure was preserved in the initial stage of pyrolysis, and the graphitization of cellulose fibrils and lignin proceeds at different rates. Most of the pores accessible for gas molecules in the resulting carbon materials are associated with former cellulose fibrils. Full article

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing (scRNA-seq) data from LUAD patients, we categorized the cells into two subclusters: dormant and active cells. Utilizing high-density Weighted Gene Co-expression Network Analysis (hdWGCNA) and pseudo-time cell trajectory, aberrant expression of genes involved in protein O-glycosylation was detected in dormant cells, suggesting a crucial role for O-glycosylation in maintaining the dormant state. Intercellular communication analysis highlighted the interaction between fibroblasts and dormant cells, where the Insulin-like Growth Factor (IGF) signaling pathway regulated by O-glycosylation was crucial. By employing Gene Set Variation Analysis (GSVA) and machine learning, a risk score model was developed using hub genes, which showed high accuracy in determining LUAD prognosis. The model also demonstrated robust performance on the training dataset and excellent predictive capability, providing a reliable basis for predicting patient clinical outcomes. The group with a higher risk score exhibited a propensity for adverse outcomes in the tumor microenvironment (TME) and tumor mutational burden (TMB). Additionally, the 50% inhibitory concentration (IC50) values for chemotherapy exhibited significant variations among the different risk groups. In vitro experiments demonstrated that EFNB2, PTTG1IP, and TNFRSF11A were upregulated in dormant tumor cells, which also contributed greatly to the diagnosis of LUAD. In conclusion, this study highlighted the crucial role of O-glycosylation in the dormancy state of LUAD tumors and developed a predictive model for the prognosis of LUAD patients. Full article

Glioblastoma multiforme (GBM) is the most severe form of brain cancer in adults, characterized by its complex vascular network that contributes to resistance to conventional therapies. Thermal therapies, such as magnetic hyperthermia (MHT), emerge as promising alternatives, using heat to selectively target tumor cells while minimizing damage to healthy tissues. The organ-on-a-chip can replicate this complex vascular network of GBM, allowing for detailed investigations of heat dissipation in MHT, while computational simulations refine treatment parameters. In this in silico study, tumor-on-a-chip models were used to optimize MHT therapy by comparing heat dissipation in normal and abnormal vascular networks, considering geometries, flow rates, and concentrations of magnetic nanoparticles (MNPs). In the high vascular complexity model, the maximum velocity was 19 times lower than in the normal vasculature model and 4 times lower than in the low-complexity tumor model, highlighting the influence of vascular complexity on velocity and temperature distribution. The MHT simulation showed greater heat intensity in the central region, with a flow rate of 1 µL/min and 0.5 mg/mL of MNPs being the best conditions to achieve the therapeutic temperature. The complex vasculature model had the lowest heat dissipation, reaching 44.15 °C, compared to 42.01 °C in the low-complexity model and 37.80 °C in the normal model. These results show that greater vascular complexity improves heat retention, making it essential to consider this heterogeneity to optimize MHT treatment. Therefore, for an efficient MHT process, it is necessary to simulate ideal blood flow and MNP conditions to ensure heat retention at the tumor site, considering its irregular vascularization and heat dissipation for effective destruction. Full article

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management. Full article

Fusarium head blight (FHB), caused by the Fusarium graminearum species complex, is a destructive disease in wheat worldwide. The lack of FHB-resistant germplasm is a barrier in wheat breeding for resistance to FHB. Thinopyrum elongatum is an important relative that has been successfully used for the genetic improvement of wheat. In this study, a translocation line, YNM158, with the YM158 genetic background carrying a fragment of diploid Th. elongatum 7EL chromosome created using ⁶⁰Co-γ radiation, showed high resistance to FHB under both field and greenhouse conditions. Transcriptome analysis confirmed that the horizontal transfer gene, encoding glutathione S-transferase (GST), is an important contributor to FHB resistance in the pathogen infection stage, whereas the 7EL chromosome fragment carries other genes regulated by F. graminearum during the colonization stage. Introgression of the 7EL fragment affected the expression of wheat genes that were enriched in resistance pathways, including the phosphatidylinositol signaling system, protein processing in the endoplasmic reticulum, plant–pathogen interaction, and the mitogen-activated protein kinase (MAPK) signaling pathway at different stages after F. graminearium infection. This study provides a novel germplasm for wheat resistance to FHB and new insights into the molecular mechanisms of wheat resistance to FHB. Full article

The Karnak Temples complex, a monumental site dating back to approximately 1970 BC, faces significant preservation challenges due to a confluence of mechanical, environmental, and anthropogenic factors impacting its stone blocks. This study provides a comprehensive evaluation of the deterioration affecting the northeast corner of the complex, revealing that the primary forms of damage include split cracking and fracturing. Seismic activities have induced out-of-plane displacements, fractures, and chipping, while flooding has worsened structural instability through uplift and prolonged water exposure. Soil liquefaction and fluctuating groundwater levels have exacerbated the misalignment and embedding of stone blocks. Thermal stress and wind erosion have caused microstructural decay and surface degradation and contaminated water sources have led to salt weathering and chemical alterations. Multi-temporal satellite imagery has revealed the influence of vegetation, particularly invasive plant species, on physical and biochemical damage to the stone. This study utilized in situ assessments to document damage patterns and employed satellite imagery to assess environmental impacts, providing a multi-proxy approach to understanding the current state of the stone blocks. This analysis highlights the urgent need for a multi-faceted conservation strategy. Recommendations include constructing elevated platforms from durable materials to reduce soil and water contact, implementing non-invasive cleaning and consolidation techniques, and developing effective water management and contamination prevention measures. Restoration should focus on repairing severely affected blocks with historically accurate materials and establishing an open museum setting will enhance public engagement. Long-term preservation will benefit from regular monitoring using 3D scanning and a preventive conservation schedule. Future research should explore non-destructive testing and interdisciplinary collaboration to refine conservation strategies and ensure the sustained protection of this invaluable historical heritage. Full article

When water pipelines undergo scenarios such as valve closure or leakage, they often operate in a gas-liquid two-phase flow state, which can easily cause abnormal pressure fluctuations, exacerbating the destructiveness of water hammer and affecting the safe operation of the pipeline. To study the problem of abnormal fluctuations in complex water pipelines, this paper establishes a transient flow model for gas-containing pipelines, considering unsteady friction, and solves it using the discrete gas cavity model (DGCM). It also studies the influence of factors such as valve closing time, initial flow rate, gas content rate, leakage location, and leakage amount on the end-of-valve pressure. Furthermore, it locates the leakage position using a genetic algorithm-backpropagation neural network (GA-BP neural network). The results show that increasing the valve closing time, increasing the gas content rate, decreasing the initial flow rate, and increasing the leakage amount all reduce the pressure peak inside the pipeline. The model constructed using the GA-BP neural network effectively predicts the leakage location with a mean absolute percentage error (MAPE) of 9.26%. The research results provide a reference for studies related to the safety protection of water conveyance projects. Full article

Amyloidosis diagnosis relies on Congo red staining with immunohistochemistry and immunofluorescence for subtyping but lacks sensitivity and specificity. Laser-microdissection mass spectroscopy offers better accuracy but is complex and requires extensive sample preparation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy offers a promising alternative for amyloidosis characterization. Cardiac tissue sections from nine patients with amyloidosis and 20 heart transplant recipients were analyzed using ATR-FTIR spectroscopy. Partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) models were used to differentiate healthy post-transplant cardiac tissue from amyloidosis samples and identify amyloidosis subtypes [κ light chain (n = 1), λ light chain (n = 3), and transthyretin (n = 5)]. Leave-one-out cross-validation (LOOCV) was employed to assess the performance of the PLS-DA model. Significant spectral differences were found in the 1700–1500 cm⁻¹ and 1300–1200 cm⁻¹ regions, primarily related to proteins. The PLS-DA model explained 85.8% of the variance, showing clear clustering between groups. PCA in the 1712–1711 cm⁻¹, 1666–1646 cm⁻¹, and 1385–1383 cm⁻¹ regions also identified two clear clusters. The PCA and the HCA model in the 1646–1642 cm⁻¹ region distinguished κ light chain, λ light chain, and transthyretin cases. This pilot study suggests ATR-FTIR spectroscopy as a novel, non-destructive, rapid, and inexpensive tool for diagnosing and subtyping amyloidosis. This study was limited by a small dataset and variability in measurements across different instruments and laboratories. The PLS-DA model’s performance may suffer from overfitting and class imbalance. Larger, more diverse datasets are needed for validation. Full article

The increasing complexity of material systems requires an extension of conventional non-destructive evaluation methods such as ultrasonic testing. Many publications have worked on extending simulation models to cover novel aspects of ultrasonic transducers, but they do not cover all components of the system. This paper presents a physically motivated, modular model that describes the complete signal flow with the aim of providing a platform for optimizing ultrasonic testing systems from individual components to the whole system level. For this purpose, the ultrasonic testing system is divided into modules, which are described by models. The modules are each parameterized by physical parameters, characteristics of real components as provided by datasheets, or by measurements. In order to validate the model, its performance is presented for three different configurations of a real test system, considering both classical sinusoidal excitation and a chirp signal. The paper demonstrates the modularity of the model, which can be adapted to the different configurations by simply adapting the modified component, thus drastically reducing the complexity of modeling a complex ultrasonic system compared to State-of-the-Art models. Based on this work, ultrasonic inspection systems can be optimized for complex applications, such as operation with coded excitation, which is a major challenge for the system components. Full article

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy. Full article

Downbursts belong to sudden, local, and strong convection weather, which present significant destruction for structures. At any given time, there are approximately 2000 thunderstorms occurring on the Earth. Many studies have investigated the effects of downbursts on different structures. However, the extensive range of varying wind field parameters and the diverse representations of wind speeds render the study of structural wind effects complex and challenging under downbursts. This study firstly reviews the research of wind field properties of downbursts according to four common approaches, and the major findings, advantages, and disadvantages of which are concluded. Then, failure analysis of transmission line systems under stationary and moving downbursts is explored. The article also reviews the wind pressure on the roof of different kinds of low-rise buildings, and some dominant parameters, namely roof slope, distance of building from downburst center, wind direction angle, and so on, are discussed. Moreover, the wind effects caused by downbursts on high-rise buildings and some specialized structures are also considered because more and more wind hazards are related to downbursts. Finally, the limitations of the current study are pointed out, and recommendations for further research are given for the accurate assessment of the effects of wind on buildings, with a view to providing safer and more economical wind-resistant design solutions for structures. Full article

Recent publications on acoustic MEMS transducers present a new three-dimensional folded diaphragm that utilizes buried in-plane vibrating structures to increase the active area from a small chip volume. Characterization of the mechanical properties plays a key role in the development of new MEMS transducers, whereby established measurement methods are usually tailored to structures close to the sample surface. In order to access the lateral vibrations, extensive and destructive sample preparation is required. This work presents a new passive measurement technique that combines acoustic transmission measurements and lumped-element modelling. For diaphragms of different lengths, compliances between 0.08 × 10⁻¹⁵ and 1.04 × 10⁻¹⁵ m³/Pa are determined without using destructive or complex preparations. In particular, for lengths above 1000 µm, the results differ from numerical simulations by only 4% or less. Full article

The formation of defects, due to drying, in robocasted ceramic objects is an important issue arising from non-uniform shrinkage of the material during this step in the process. Common methods for shrinkage measurement are not well suited to the small size of robocasted cords or the complexity of robocasted objects. Innovative methods for shrinkage measurement were developed using non-destructive optical vision techniques with computer-controlled data acquisition, allowing measurement on millimetric cords and on specific zones of a product. The study of a single porcelain cord revealed an anisometric shrinkage related to the orientation of grains during extrusion. A differential shrinkage at the macroscopic scale was also measured on a robocasted object, indicating a moisture content gradient in the material. The methods presented in this paper are of particular relevance to real-time control of the drying process for robocasted objects. Full article

Bioimpedance spectrum (BIS) measurements are highly appreciated in in vivo studies. This non-destructive method, supported by simple and efficient instrumentation, is widely used in clinical applications. The multi-frequency approach allows for the efficient extraction of the most information from the measured data. However, low-frequency implementations are still unexploited in the development of the technique. A self-developed BIS measurement technology is considered the pioneering approach for low (<5 kHz) and ultra-low (<100 Hz) frequency range studies. In this paper, the robustness of ultra-low frequency measurements in the prototypes is examined using specially constructed physical models and a dedicated neural network-based software. The physical models were designed to model the dispersion mainly in the ultra-low frequency range. The first set of models was used in the training of the software environment, while the second set allowed a complete verification of the technology. Further, the Hilbert transformation was employed to adjust the imaginary components of complex signals and for phase determination. The findings showed that the prototypes are capable of efficient and robust data acquisition, regardless of the applied frequency range, minimizing the impact of measurement errors. Consequently, in in vivo applications, these prototypes minimize the variance of the measurement results, allowing the resulting BIS data to provide a maximum representation of biological phenomena. Full article