Search Results (98)

The aim of this paper is to tailor the geometry and material parameters of a three-layer railway track model to achieve favorable properties for the circulation of high-speed trains at very high velocities. The three layers imply that the model should have three critical velocities for resonance. However, in many cases, some of these values are missing and must be replaced by pseudo-critical values. Since no resonance occurs at pseudo-critical velocities, even in the absence of damping, deflections never reach infinity. By using optimization techniques, it is possible to adjust the model’s parameters, so that the increase in vibrations remains minimal and does not pose a real danger. In this way, circulation velocities could be extended beyond the critical value, thereby increasing the network capacity and, consequently, improving the competitiveness of rail transport compared to other modes of transportation, thus contributing to decarbonization. The presented results are preliminary and require further analysis and validation. Several optimization techniques are implemented, leading to the establishment of designs that already have rather high pseudo-critical velocities. Further research will show how these theoretical findings can be utilized in practice.  Full article

Although treeline ecotones are significant components of vulnerable mountain ecosystems and key indicators of climate change, treelines of the Southern Hemisphere remain largely outside of research focus. In this study, we investigate, for the first time, the current and future distribution of the treeline species Nothofagus pumilio in the Southern Andes using a Species Distribution Modelling approach. The lack of modelling studies in this region can be contributed to missing occurrence data for the species. In a preliminary study, both point and raster data were generated using a novel Instagram ground truthing approach and remote sensing. Here we tested the performance of the two datasets: a typical binary species dataset consisting of occurrence points and pseudo-absence points and a continuous dataset where species occurrence was determined by supervised classification. We used a Random Forest (RF) classification and a RF regression approach. RF is applicable for both datasets, has a very good performance, handles multicollinearity and remains largely interpretable. We used bioclimatic variables from CHELSA as predictors. The two models differ in terms of variable importance and spatial prediction. While a temperature variable is the most important variable in the RF classification, the RF regression model was mainly modelled by precipitation variables. Heat deficiency is the most important limiting factor for tree growth at treelines. It is evident, however, that water availability and drought stress will play an increasingly important role for the future competitiveness of treeline species and their distribution. Modelling with binary presence–absence point data in the RF classification model led to an overprediction of the potential distribution of the species in summit regions and in glacier areas, while the RF regression model, trained with continuous raster data, led to a spatial prediction with small-scale details. The time-consuming and costly acquisition of complex species information should be accepted in order to provide better predictions and insights into the potential current and future distribution of a species. Full article

This article introduces a novel approach to decomposing the Blinder–Oaxaca gender wage gap using pseudo-panel data. In many developing countries, panel data are not available; however, understanding the evolution of the gender wage gap over time requires tracking individuals longitudinally. When individuals change across time periods, estimators tend to be inconsistent and inefficient. To address this issue, and building upon the traditional Blinder–Oaxaca methodology, we propose an alternative procedure that follows cohorts over time rather than individuals. This approach enables the estimation of both the explained and unexplained components—“endowment effect” and “remuneration effect”—of the wage gap, along with their respective standard errors, even in the absence of true panel data. We apply this methodology to the case of Colombia, finding a gender wage gap of approximately 15% in favor of male cohorts. This gap comprises a −5.6% explained component and a 20% unexplained component without controls. When we control by informality, size of the firm and sector the gap comprises a −3.5% explained component and a 18.7% unexplained component. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

In this study, the degradation of Nile Blue dye was investigated using homogeneous and heterogeneous photocatalytic methods based on the photo-Fenton reaction. More specifically, for homogeneous photocatalysis, the classical photo-Fenton (UV/Fe²⁺/H₂O₂) and modified photo-Fenton-like (UV/Fe²⁺/S₂O₈²⁻) systems were studied, while for heterogeneous photocatalysis, a commercial MOF catalyst, Basolite F300, and a natural ferrous mineral, geothite, were employed. Various parameters—including the concentrations of the oxidant and catalyst, UV radiation, and pH—were investigated to determine their influence on the reaction rate. In homogeneous systems, an increase in iron concentration led to an enhanced degradation rate of the target compound. Similarly, increasing the oxidant concentration accelerated the reaction rate up to an optimal level, beyond which radical scavenging effects were observed, reducing the overall efficiency. In contrast, heterogeneous systems exhibited negligible degradation in the absence of an oxidant; however, the addition of oxidants significantly improved the process efficiency. Among the tested processes, homogeneous techniques demonstrated a superior efficiency, with the conventional photo-Fenton process achieving complete mineralization within three hours. Kinetic analysis revealed pseudo-first-order behavior, with rate constants ranging from 0.012 to 0.688 min⁻¹ and correlation coefficients (R²) consistently above 0.90, confirming the reliability of the applied model under various experimental conditions. Nevertheless, heterogeneous techniques, despite their lower degradation rates, also achieved high removal efficiencies while offering the advantage of operating at a neutral pH without the need for acidification. Full article

Background/Objectives: The Wartenberg sign is a diagnostic feature of ulnar nerve neuropathy. It results from unbalanced activity of the abductor digiti minimi (ADM) and extensor digiti minimi (EDM) muscles secondary to weakness of the third palmar interosseous muscle. Rarely, this sign may occur in the absence of an underlying ulnar neuropathy, which we refer to as the “pseudo Wartenberg sign” (PWS). Methods: This is a retrospective review of 10 patients manifesting an inability to adduct the little finger towards the ring finger with no evidence of an ulnar neuropathy. We describe the clinical and electrodiagnostic (EDX) findings in these patients and discuss the pathophysiologic basis of PWS. Results: The most common cause was an injury in five (50.0%) patients: avulsion of the third volar interosseous muscle in two (20.0%), contracture of the ADM muscle in one (10.0%), and trauma-related dystonia in two (20.0%). The most frequent mechanism of PWS was focal dystonia of specific hand muscles in seven (70.0%) patients. Needle electromyography (EMG) demonstrated no denervation changes in ulnar nerve-innervated hand muscles; the motor and sensory conduction was normal in the ulnar nerve in all patients. Four (40.0%) patients underwent ultrasound studies, with a hyperechoic, avulsed third volar interosseous muscle in one, a hyperechoic and atrophic ADM muscle in one, normal hypothenar and extensor muscles in one, and a normal hypothenar muscle in one. Conclusions: Neurologists, neurosurgeons, and hand and orthopedic surgeons should be aware of the rare cases in which the inability to adduct the little finger may occur in the absence of ulnar neuropathy and look for other causes like avulsion of the third palmar interosseus muscle or focal hand dystonia. Full article

Accurate hyperspectral simulations are critical for the vicarious calibration of next-generation space-based sensors and for ensuring the long-term consistency of climate data records. This study presents a refined methodology to generate simulated radiometric calibration references over bright desert pseudo-invariant calibration sites, specifically designed to meet the stringent accuracy requirements of hyperspectral observations. Building on metrology principles, and in the absence of SI-traceable references, the approach leverages simulated reflectance over stable desert targets as a community-accepted calibration reference. Key advancements include improved surface reflectance modelling using the Rahman–Pinty–Verstraete model and the Combined Inversion of Surface and Aerosol (CISAR) algorithm, enhanced atmospheric property characterization from multiple state-of-the-art datasets, and the use of the Eradiate Monte Carlo-based radiative transfer model. These refinements reduce uncertainty in simulated top-of-atmosphere reflectance, achieving an accuracy within ±3% in high-transmittance spectral regions. Validation against both multispectral and hyperspectral satellite data (i.e., EMIT, EnMAP, and PRISMA) confirms the robustness of the methodology. This work establishes a reliable framework for hyperspectral sensor calibration and intercalibration, addressing the pressing need for traceable, high-fidelity reference data in Earth observation. Full article

The efficient removal of pharmaceuticals and personal care products (PPCPs) from aqueous solutions using conventional adsorbents is hindered by low adsorption capacity, insufficient selectivity, poor regeneration performance, and limited stability. In this study, a multilayer β-CD/GO membrane was successfully prepared via layer-by-layer coating with β-cyclodextrin (β-CD) and graphene oxide (GO). The multilayer β-CD/GO membrane combines the host–guest complexation ability of β-CD with the abundant oxygen-containing functional groups of GO to enhance the targeted removal of PPCPs (CTD, SMZ, and DCF) from aqueous solutions. The prepared multilayer β-CD/GO membrane adsorbent overcomes the separation difficulties and poor regeneration performance of powdered adsorbents, and the multilayer structure can significantly enhance structural stability and increase the number of adsorption sites. Batch adsorption experiments showed that the optimal adsorption performance of the multilayer β-CD/GO membrane for PPCPs occurred at pH 4 and in the absence of coexisting ions. With increasing pH values in the range of 4–9, the adsorption capacities of CTD, SMZ, and DCF slightly decreased to 14.37, 13.69, and 13.01 mg/g, respectively, and the adsorption capacities decreased slowly to 4.88, 3.51, and 3.26 mg/g as the coexisting ion concentrations increased from 0 to 0.20 mol/L. The adsorption mechanism of the multilayer β-CD/GO membrane for PPCPs was systematically investigated through adsorption kinetics, isotherms, and thermodynamics. The adsorption processes of CTD, SMZ, and DCF by the multilayer β-CD/GO membrane were well described by both pseudo-first-order and pseudo-second-order kinetic models (R² > 0.984), suggesting a hybrid adsorption mechanism involving both physisorption and chemisorption. The isotherm results indicated that the adsorption of CTD by the multilayer β-CD/GO membrane followed the Langmuir model (R² = 0.923), whereas the adsorption of SMZ and DCF was better described by the Freundlich model (R² = 0.984–0.988). The multilayer β-CD/GO membrane exhibited high adsorption capacities for CTD, SMZ, and DCF with maximum capacities of 35.56, 43.29, and 39.49 mg/g, respectively. Thermodynamic analyses indicated that the adsorption of PPCPs was exothermic ($\Delta H^0$ = −86.16 to −218.49 J/mol/K) and non-spontaneous ($\Delta G^0$ = 9.84–11.56, 9.50–12.54, and 10.09–14.46 kJ/mol). The multilayer β-CD/GO membrane maintained a removal efficiency of over 58.45–71.73% for CTD, SMZ, and DCF after five consecutive regeneration cycles, demonstrating high reusability for practical applications. The adsorption mechanisms of the multilayer β-CD/GO membrane include electrostatic interactions, hydrogen bonding, hydrophobic interactions, and π-π EDA interactions. This study offers a promising and environmentally friendly adsorbent for the efficient removal of PPCPs from aqueous solutions. Full article

The Philippines has a high diversity of venomous snake species, but there is minimal information on their envenomation effects. This is evidenced by the small number of case reports, the poor reporting of envenomation cases, and the absence of specific antivenoms apart from one against the Philippine cobra (Naja philippinensis). This study sought to profile the action of selected Philippine pit viper venoms on blood coagulation and to investigate whether commercially available non-specific antivenoms can provide adequate protection against these venoms. Venom from the pit vipers Trimeresurus flavomaculatus and Trimeresurus mcgregori were subjected to coagulation assays, antivenom cross-neutralization tests, and thromboelastography. Venoms from both species were able to clot human plasma and isolated human fibrinogen. Consistent with pseudo-procoagulant/thrombin-like activity, the resulting fibrin clots were weak and transient, thereby contributing to net anticoagulation through the depletion of fibrinogen levels. Clotting factors fIXa and fXa were also inhibited by the venoms, further contributing to the net anticoagulant activity. Monovalent and polyvalent antivenoms from the Thai Red Cross Society were effective against both venoms, indicating cross-neutralization of venom toxins; the polyvalent antivenom was able to rescue fibrinogen clotting to a greater degree than the monovalent antivenom. Our findings highlight the coagulopathic effects of these pit viper venoms and suggest the utility of procuring the non-specific antivenoms for areas in the Philippines with a high risk for pit viper envenomation. Full article

The article addresses the current and future potential distribution of Drosophila suzukii (Diptera: Drosophilidae), commonly known as spotted wing Drosophila (SWD). This invasive pest affects various fruit crops worldwide. Native to Southeast Asia, the species has rapidly expanded due to its high adaptability to climates and ability to infest ripe fruits. SWD occurrence data were collected from multiple databases, pseudo-absences were selected from the background area, and climatic variables were downloaded from WorldClim. The Random Forest algorithm was employed to model the current distribution and project future scenarios, categorizing environmental suitability into high, moderate, and low levels. The analysis of bioclimatic variables indicated that factors such as isothermality, maximum temperature of the warmest month, and precipitation of the driest month are the most significant for pest distribution. The results revealed high climatic suitability for the species in North America, Europe, and Asia, with projections indicating expansion under climate change scenarios in the Northern Hemisphere, including new areas in Europe and North America. Regions with higher suitability are expected to require management and monitoring strategies, particularly in vulnerable agricultural areas. Furthermore, the study underscores the importance of climatic data in predicting pest distribution and formulating effective control and mitigation policies. Full article

Methylene blue dye, commonly used in various industries, poses significant risks to both human health and the environment due to its persistence, toxicity, and potential to disrupt aquatic ecosystems. Exposure can cause severe health conditions such as methemoglobinemia, while its stability and solubility allow it to persist in natural water systems, reducing oxygen levels and harming aquatic life. In this study, novel analcime/sodium magnesium aluminum silicon silicate nanocomposites (Z1 and Z2) were synthesized via a controlled hydrothermal method, where Z1 and Z2 were synthesized in the absence and presence of polyethylene glycol as a template, respectively. X-ray diffraction (XRD) analysis confirmed the formation of crystalline phases of analcime and sodium magnesium aluminum silicon silicate. The average crystallite size of the Z1 nanocomposite is 75.30 nm, whereas the Z2 nanocomposite exhibits a smaller average crystallite size of 60.27 nm due to the template effect. Field emission scanning electron microscopy (FE-SEM) revealed that Z2 exhibited more uniform and well-dispersed particles compared to Z1. Energy-dispersive X-ray spectroscopy (EDX) confirmed the elemental composition, showing higher sodium content and optimized incorporation of aluminum and silicon in Z2. High-resolution transmission electron microscopy (HR-TEM) demonstrated that Z2 had well-defined spherical particles, indicating improved structural control. The maximum adsorption capacities were 230.95 mg/g for Z1 and 290.69 mg/g for Z2. The adsorption process was exothermic, spontaneous, and chemical in nature, following the pseudo-second-order kinetic model and Langmuir isotherm, confirming monolayer adsorption on homogeneous surfaces. Full article

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model’s performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night. Full article

Chinese word segmentation (CWS), which involves splitting the sequence of Chinese characters into words, is a key task in natural language processing (NLP) for Chinese. However, the complexity and flexibility of geologic terms require that domain-specific knowledge be utilized in CWS for geoscience domains. Previous studies have identified several challenges that have an impact on CWS in the geoscience domain, including the absence of abundant labeled data and difficult-to-delineate complex geological word boundaries. To solve these problems, a novel semi-supervised deep learning framework, GeoCWS, is developed for CWS in the geoscience domain. The framework is designed with domain-enhanced features and an uncertainty-aware self-training strategy. First, n-grams are automatically constructed from the input text as a pseudo-lexicon. Then, a backbone model is suggested that learns domain-enhanced features by introducing a pseudo-lexicon-based memory mechanism to delineate complex geological word boundaries based on BERT. Next, the backbone model is fine-tuned with a small amount of labeled data to obtain the teacher model. Finally, we design a self-training strategy with joint confidence and uncertainty awareness to improve the generalization ability of the backbone model to unlabeled data. Our method outperformed the state-of-the-art baseline methods in extensive experiments, and ablation experiments verified the effectiveness of the proposed backbone model and self-training strategy. Full article

Currently, multi-modal 3D object detection methods have become a key area of research in the field of autonomous driving. Fusion is an essential factor affecting performance in multi-modal object detection. However, previous methods still suffer from the inability to effectively fuse features from LiDAR and RGB images, resulting in a low utilization rate of complementary information between depth and semantic texture features. At the same time, existing methods may not adequately capture the structural information in Region of Interest (RoI) features when extracting them. Structural information plays a crucial role in RoI features. It encompasses the position, size, and orientation of objects, as well as the relative positions and spatial relationships between objects. Its absence can result in false or missed detections. To solve the above problems, we propose a multi-modal sensor fusion network, Bi-Att3DDet, which mainly consists of a Self-Attentive RoI Feature Extraction module (SARoIFE) and a Feature Bidirectional Interactive Fusion module (FBIF). Specifically, SARoIFE captures the relationship between different positions in RoI features to obtain high-quality RoI features through the self-attention mechanism. SARoIFE prepares for the fusion stage. FBIF performs bidirectional interaction between LiDAR and pseudo RoI features to make full use of the complementary information. We perform comprehensive experiments on the KITTI dataset, and our method notably demonstrates a 1.55% improvement in the hard difficulty level and a 0.19% improvement in the mean Average Precision (mAP) metric on the test dataset. Full article

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species. Full article