Search Results (1,087)

This paper studies a two-stage oxygen-alkaline treatment and subsequent bleaching of softwood sulfate pulp obtained from healthy and deadwood of spruce and larch. Delignification was carried out at elevated temperature and pressure in an alkaline medium with the addition of hydrogen peroxide, after which the pulp was subjected to classic ECF cycles with chlorine dioxide, hydrogen peroxide and, if necessary, elemental chlorine. The selected and washed mass was ground to a specified degree of grinding and formed into laboratory sheets of standard density on a sheet-forming apparatus. The results showed that oxygen-alkaline pretreatment significantly reduces the residual lignin content, and subsequent bleaching cycles make it possible to obtain high-brightness pulp with minimal losses of cellulose and viscosity. The structural, morphological and mechanical characteristics of the obtained samples were studied. After a full bleaching cycle, the fibers become slightly shorter and thinner, their surface is leveled, the proportion of small fractions decreases, and the homogeneity of the structure improves. The resulting cellulose samples demonstrate mechanical characteristics that meet industrial requirements for high-quality printing and thin-layer paper grades. Full article

The present paper addresses the experimental measurement of vibration frequencies using an earth pressure sensor embedded in a full-scale (1:1) test structure. The vibration frequencies within the tested structure were induced by static load tests carried out at different elevation levels (corresponding to varying thicknesses of the crushed aggregate layer) in accordance with the methodology applied on German railways (DIN 18 134). The aim of the research was to verify the stress state at individual partial levels of the tested structure on the basis of the measured vibration frequencies, and to determine the depth of influence and the load dispersion angle generated by the static load test (SLT). The measured parameters also serve as input data for parallel research focused on the assessment of transition zones between railway embankments and artificial structures along railway lines. The results presented in this paper indicate that the stress induced by the SLT decreases with increasing structural thickness of the tested construction. For a structural layer thickness of 150 mm, the resulting stress corresponds to approximately 63% of the stress value (force effect) induced on a rigid circular plate (σ = 0.50 MPa), whereas for a layer thickness of 900 mm, the stress corresponds to approximately 12% of that value. The force (stress) effects of the SLT cease to act at a depth between 900 and 950 mm (only stress due to the self-weight of the overlying material was recorded), and the load dispersion angle is approximately 40°. Full article

Long-term stability of dam infrastructure is crucial for flood protection, water resource management, and drinking water supply. In many regions, the increasing impact of climate change and structural aging necessitates advanced monitoring approaches for embankment and gravity dams. PSI has emerged as a valuable technique for detecting surface deformation rates with millimeter precision. This study presents a comprehensive monitoring concept that combines satellite-based PSI analyses with the first operational use of ECRs at dam sites in North Rhine-Westphalia (NRW), Germany. Over a period of more than two years, ECRs were observed under real-world conditions using Sentinel-1 data. Compared to traditional passive reflectors, ECRs offer improved signal stability and a compact design, making them particularly suitable for confined or sensitive dam environments. The analysis of displacement time series confirms the suitability of ECRs for long-term deformation monitoring in complex dam settings. Intercomparison of two PSI time series demonstrated high internal consistency (correlation > 0.9, RMSE < 1 mm), while validation against in situ measurements confirmed millimeter-level agreement with RMSE values between 2 and 5 mm and correlations up to 0.7. In addition, a dedicated web-based platform was developed to provide processed ECR-based PSI results to dam operators, offering interactive visualizations, time-series access, and standardized downloads. This integration of advanced interferometric synthetic aperture radar (InSAR) methods, innovative hardware, and user-oriented service delivery marks a significant step toward operational dam monitoring using satellite remote sensing. Full article

Pile-supported geosynthetic-reinforced embankments, as effective foundation improvements, are being used increasingly often in the construction of highway and railway engineering at present. The geosynthetic-reinforced load transfer platform in the horizontal direction was simulated to the thin plate, and then the differential equation of the curved surface and the nonlinear foundation model were used to solve the analytical expression of the post-construction settlement of the reinforced area, and the engineering example was used to verify it. Furthermore, a finite element model was developed to simulate the settlement. The analysis utilized a static general step and incorporated a linear elastic–perfectly plastic model with the Mohr–Coulomb failure criterion. The numerical result of 19.7 mm was consistent with the theoretical prediction of 20.1 mm, demonstrating a mere 2.0% relative error and substantiating the validity and accuracy of the theoretical model. The analysis examined how bending stiffness, the subgrade reaction coefficient, pile spacing, and embankment height affect post-construction settlement. The results demonstrate that the settlement increases with larger pile spacings or lower values of the subgrade reaction coefficient and bending stiffness. Notably, the settlement increases with embankment height only until a critical height—calculated from the bearing capacity of the inter-pile soil—is exceeded. Based on this, it was found that the subgrade reaction coefficient was identified as the most influential parameter, followed by pile spacing and then bending stiffness. These findings lead to practical recommendations for engineering practice. Full article

As a heterogeneous rock cemented by gravel and matrix, understanding the mechanical behaviour and failure mechanism of conglomerate is of great significance for engineering projects. A three-dimensional grain-based model (3D-GBM) incorporating both microstructural and material heterogeneity of conglomerate is developed based on particle flow code (PFC3D). With the model’s rationality and microscopic parameters validated, the failure process and fracture mechanism of conglomerate under uniaxial and triaxial compression are numerically investigated. The numerical results reveal that the established 3D-GBM can simulate the mechanical behaviour and fracture characteristics of conglomerate. As the confining pressure increases, the failure mode of the specimen transitions from matrix tensile cracking to matrix shear cracking. During the loading process, the microcrack evolution and contact force distribution in the gravel, matrix, and cementation area exhibit pronounced heterogeneity. Confining pressure promotes the fragmentation of gravel and the initiation of shear microcracks. In addition, the effect of gravel size and content on the mechanical behaviour and microcracking characteristics of conglomerate is quantitatively investigated. Variations in gravel size and content influence the distribution of inter-particle contact forces, thereby altering the failure characteristics and mechanical properties of the specimen. Full article

Hyperspectral (HS) analysis was used to measure the dynamics of Fusarium head blight (FHB) disease severity on panicles of three oat cultivars, ‘Husky’, ‘Ivory’, and ‘Lelde’, under greenhouse conditions. Inoculation with Fusarium spp. spore material was conducted (i) on the seeds and (ii) plants at the mid-flowering stage (BBCH 65). Disease development on oat panicles was assessed visually, and imaged with an HS camera from the end of the flowering stage (BBCH 69) to the early–middle ripe stage (BBCH 83–85). To verify that FHB symptoms were caused by Fusarium spp. pathogens, a microbiological test was performed. At the end of the trial, mycotoxin analysis of the kernels was conducted. The collected HS data from diseased and control plant panicles were used to estimate the head blight index (HBI). A Python-based software was developed to assess HBI at the pixel level. Both visual assessment and HS analysis confirmed statistically significant differences in disease severity between all treatment options. The highest disease severity results were obtained in the last disease assessment run (BBCH 83–85) for the inoculated head treatment. Microbiological test results confirmed that FHB symptoms in oat kernels were mostly caused by F. sporotrichioides. The correlation coefficient between the visually assessed FHB disease severity results and HS analysis results was 0.969. The correlation coefficient between T-2/HT-2 mycotoxins and HS disease severity results was 0.971, which suggests the potential for using HS analysis in field monitoring for mycotoxin content detection. Full article

Accurate spatial identification of check dams is a key prerequisite for evaluating soil and water conservation benefits and optimizing dam system planning on the Loess Plateau. Current deep learning models face severe misclassification and omission issues under complex terrain due to the scarcity of check dam samples and the lack of prior geographic knowledge. This study proposes a recognition method based on Faster R-CNN, constrained by suitable areas and microtopography. The Xiliugou watershed in Inner Mongolia was selected as the study area. Based on Google Earth imagery and field survey data, a check dam sample dataset was constructed, integrating the morphological features of “linear dam body with a trapezoidal slope.” Using the construction suitable area constraints defined by the Technical Specifications for Check Dams and microtopography standard deviation (δ) derived from DEM as dual spatial filtering mechanisms, these were deeply embedded into the Faster R-CNN model to limit the search space and enhance geographic plausibility. Experimental results show that the constrained Faster R-CNN model achieved a precision and recall of 92.86% and 96.89%, compared with the accuracy rate of only deep learning model recognition (60.61%), which significantly increased by 32.25%, indicating that geographical constraints have an enhancing effect. Using this method, a total of 191 embankment dams were identified in the Xiliugou Basin. New 30 unrecorded embankment dams (21 small dams and 9 micro-dams) were discovered. The model’s good generalization ability was verified in the Han Tiechuan geographical isolation area, which contained 153 embankment dam samples, with an accuracy rate of 72.94%. Spatial analysis further revealed the “successive interception along tributaries” distribution pattern and strong spatial aggregation characteristics (box dimension D ≈ 0.36) of check dams in the Xiliugou watershed. This study confirms the critical role of suitable area and microtopography constraints in improving the accuracy and reliability of deep learning models and provides a transferable technical paradigm for automated, high-precision surveys of regional soil and water conservation projects. Full article

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management. Full article

Utilizing excavated waste soil to level gullies offers significant advantages in terms of engineering economy and construction efficiency. However, the stability and deformation risks of high-fill embankments in mountainous gullies under rainfall conditions have attracted significant attention, particularly when such structures are located adjacent to residential areas. This study compares two design schemes for highway high-fill embankments, Scheme 1: high-fill slope supported by stabilizing piles and prestressed anchors, and Scheme 2: ordinary waste soil as the core, foamed lightweight soil (EPS) as the edge band, and reinforcement by a micro-pile retaining wall system. Finite element analysis was used to evaluate the Factor of Safety (FOS), displacements of retaining structures, and characteristic slope points under three conditions (no rainfall, heavy rainfall, and heavy rainfall with soil strength deterioration). The results show that Scheme 2 reduces total costs by 3.5%, shortens the construction period by 14%, and cuts maintenance costs by 65%, with a minimum FOS of 1.56 under extreme rainfall. Further parametric analysis of Scheme 2 optimized key design parameters, and field monitoring data over 6 months verified the reliability of the numerical simulation. This study provides a transferable design-verification pathway for combining lightweight and conventional fills in high embankments, offering technical support for similar projects in complex mountainous areas. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

This study characterizes the multifractal structure of monthly precipitation in the semi-arid state of Guanajuato, Mexico, using Multifractal Detrended Fluctuation Analysis with quadratic detrending (MFDFA-2). We analyze 65 quality-controlled meteorological stations covering the period 1981–2016. All series exhibit multifractality, with generalized Hurst exponents $h\left(2\right)=0.568\pm 0.065$ indicating predominantly persistent dynamics and long-term positive autocorrelation (64.6% of stations). The multifractal spectrum width ($\Delta \alpha$) ranges from 0.15 to 0.72 (mean = 0.2423), revealing substantial spatial variability in scaling complexity. K-means clustering based on multifractal features identifies the following four hydroclimatic groups: one random cluster (29.2% of stations) and three persistence-dominated clusters (70.8%), with coherent spatial organization. These findings provide new insights into the temporal scaling properties of precipitation in semi-arid regions and have important implications for water resource management and regionalized drought-risk assessment. Full article

The new compound Cs₂Cu₂[V₄O₁₂]Br₂ was synthesized by the chemical vapor transport reaction method. Structural data obtained by single-crystal X-ray diffraction in the temperature range 100–700 K revealed three successive (with decreasing temperature) structural phase transitions: from the high-temperature aristotype structure I4/mmm (>550 K) to the polymorph P4/mnc (550–340 K), then to P4/m (340–300 K), and finally to the low-temperature phase I4/m (<300 K). The crystal structure of the new compound is based upon the Cu₂[V₄O₁₂]⁰ layers, consisting of four-membered rings of corner-sharing vanadate tetrahedra linked by CuO₄ squares. Analysis of the structural evolution with increasing temperature shows that the entire sequence of phase transitions is governed by the rotation of the [V₄O₁₂]⁴⁻ rings about the z axis. Full article

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

Ongoing studies on the Lepidoziaceae in East Indochina have yielded new information on the distribution and morphology of a number of family representatives. This study aimed to provide new data in this regard. The latter task looks quite justified, taking into account the fact that East Indochina houses a notable portion of the worldwide Lepidoziaceae diversity, especially in the genus Bazzania. The materials for the paper were 48 specimens collected throughout East Indochina. The cited specimens contain 18 taxa, discussed in respect of their ecology, distribution, and morphology. All the taxa discussed in this paper are supplemented with illustrations, and their morphological descriptions based on the Indochinese materials are also included in most cases. One taxon (Bazzania appendiculata subsp. cambodiana subsp. nov.) is described as new-for-science. Six species are new to Indochina, three species are new to East Indochina, one species is new to Vietnam, and three species are new to Cambodia. A comparison of the currently known taxonomic diversity with that of Malaysia, which borders East Indochina, reveals that the diversity of Lepidoziaceae in East Indochina is still clearly understudied, and further research is likely to yield new discoveries. The final target in this field is the creation of a thorough taxonomic revision of the family in this region in the future. Full article

Fluid-driven fracture processes are central to the development of subsurface energy systems such as geothermal and hydrocarbon reservoirs. Although phase-field formulations have become a widely used tool for describing fracture initiation and growth, the diffuse representation of cracks makes it difficult to resolve flow behavior accurately inside discrete fracture networks (DFNs) and to represent hydro-mechanical coupling in a sharp-interface sense. This study develops a hybrid-dimensional iterative framework for lubrication-flow simulation in deformable fractured geomaterials. By leveraging phase-field point clouds together with non-conforming discretization schemes for both the solid matrix and fracture domains, the proposed framework enables the dynamic reconstruction of evolving fracture networks. The theoretical formulation and numerical implementation of the coupling strategy are presented in detail. Hydraulic benchmark examples verify the performance of the fluid flow solver under various physical conditions. The classical Sneddon problem and Khristianovic–Geertsma–de Klerk (KGD) model are employed to validate the solid deformation solver, confirming accurate predictions of crack opening displacement and mesh independence in fracture width calculation. Additional simulations with complex pre-existing fracture patterns further demonstrate the applicability of the framework to coupled hydro-mechanical analysis in fractured media. Full article