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Sapphire crystals, owing to their outstanding mechanical and optical properties, which are widely used in advanced optics, microelectronic devices, and medical instruments. The manufacturing precision of sapphire optical components critically affects the performance of advanced optical systems. However, the extremely high hardness and low fracture toughness of sapphire make it a typical hard-to-machine material, prone to brittle surface fractures and subsurface damage during material removal. Improving the machinability of sapphire remains a pressing challenge in advanced manufacturing. In this study, surface modification and enhanced ductility of C-plane sapphire were achieved via ion implantation, and the machinability of the modified sapphire was further improved through laser-assisted diamond machining (LADM). Monte Carlo simulations were employed to investigate the interaction mechanisms between incident ions and the target material. Based on the simulation results, phosphorus ion implantation experiments were conducted, and transmission electron microscopy observation was used to characterize the microstructural evolution of the modified layer, while the optical properties of the samples before and after modification were analyzed. Finally, groove cutting experiments verified the enhancement in ductile machinability of the modified sapphire under LADM. At a laser power of 16 W, the ductile–brittle transition depth of the modified sapphire increased to 450.67 nm, representing a 51.57% improvement over conventional cutting. The findings of this study provide valuable insights for improving the ductile machining performance of hard and brittle materials. Full article

Soil contamination by microplastics (MPs) is a global problem, exacerbated by the growing production of plastics and low levels of recycling. Considering that agricultural lands constitute a significant part of the land surface (37.7%), the study of the influence of MPs on the carbon cycle in such ecosystems is extremely important for understanding the global carbon balance. This work aims to develop a standardized protocol for determining the effects of microplastics (MPs) on the carbon cycle in agricultural soils. Differences in existing research protocols hinder comparability and limit conclusions about real-world impacts. A preliminary proposal for standardizing the protocol for the determination of MPs influence on the CO₂ and/or CH₄ emission in agricultural seeks to improve reproducibility and transparency in future studies. The protocol incorporates a wide variety of MPs characteristic in agricultural soils and allows experiments at realistic contamination levels, reflecting both current and projected future scenarios. Key recommendations include several points. Stringent contamination control during sample collection and preparation is of paramount importance. The selection of microplastic types and concentrations specific to agricultural environments is also recommended. Furthermore, maintaining consistent experiment durations is crucial, and the utilization of gas chromatography for analysis is highly desirable. Full article

Aluminum powder is the most commonly used metal fuel in the industry of explosives and propellants. The research progress in preparation technology, reactivity and application of nano-aluminum in explosives and propellants is systematically reviewed in this paper. The preparation technology of nano-aluminum powder includes mechanical pulverization technology (such as the ball milling method and ultrasonic ablation method, etc.), evaporation condensation technology (such as the laser induction composite heating method, high-frequency induction method, arc method, pulsed laser ablation method, resistance heating condensation method, gas-phase pyrolysis method, wire explosion pulverization method, etc.), chemical reduction technology (such as the solid-phase reduction method, solution reduction method, etc.) and the ionic liquid electrodeposition method, each of which has its own advantages. Some new preparation methods have emerged, providing important reference value for the large-scale production of high-purity, high-quality nano-aluminum powder. The reactivity differences between nano-aluminum powder and micro-aluminum powder are compared in the thesis. It is clear that the reactivity of nano-aluminum powder is much higher than that of micro-aluminum powder in terms of ignition performance, combustion performance and reaction completeness, and it has a stronger influence on the detonation performance of mixed explosives and the combustion performance of propellants. Nano-aluminum powder is highly prone to oxidation, which seriously affects its application efficiency. In addition, when aluminum powder oxidizes or burns, a surface oxide layer will be formed, which hinders the continued reaction of internal aluminum powder. In addition, nano-aluminum powder may deteriorate the preparation process of explosives or propellants. To improve these shortcomings, appropriate coating or modification treatment is required. The application of nano-aluminum powder in mixed explosives can improve many properties of mixed explosives, such as detonation velocity, detonation heat, peak value of shock wave overpressure, etc. Applying nano-aluminum powder to propellants can significantly increase the burning rate and improve the properties of combustion products. It is pointed out that the high reactivity of nano-aluminum powder makes the preparation and storage of high-purity nano-aluminum powder extremely difficult. It is recommended to increase research on the preparation and storage technology of high-purity nano-aluminum powder. Full article

Background: Balamuthia mandrillaris is a free-living amoebic parasite that primarily causes rare opportunistic infections in immunocompromised hosts. Balamuthia amoebic encephalitis (BAE) is a rare yet severe parasitic infection affecting the central nervous system. It has an extremely low incidence in China but can have a mortality rate as high as 98%. The clinical manifestations of amebic infections are similar to those of bacterial and tuberculous meningitis, lacking specificity, which makes accurate diagnosis challenging in the clinical setting. Case Presentation: A 61-year-old immunocompetent woman experienced worsening headache and a moderate fever over the course of five days, initially treated as a common cold. On 25 February 2025, she exhibited behavioral abnormalities, dysphagia, and a high fever of 40.2 °C, which progressed to a coma. On 26 February, her cranial CT scan revealed multifocal hemorrhagic lesions in the right frontotemporoparietal lobes. The MRI revealed similar lesions with slight enhancement and herniation. She underwent an emergency decompressive craniectomy, yet her condition continued to deteriorate following the surgery. On 27 February, serum targeted next-generation sequencing (tNGS) detected B. mandrillaris. Additionally, metagenomic NGS (mNGS) of the cerebrospinal fluid (CSF) sample confirmed the presence on 28 February. Finally, B. mandrillaris was identified through a brain tissue biopsy on 3 March. However, due to the delayed diagnosis and lack of effective drugs, her condition rapidly deteriorated and became irreversible. Her family ultimately chose to withdraw treatment. Conclusions: This study highlights the application of NGS for early diagnosis of patients with severe CNS infection. Both tNGS and mNGS can be considered for the rapid detection of rare or novel pathogens and for facilitating diagnosis. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we develop an interpretable framework by combining grey relational analysis (GRA) with three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVR), and eXtreme Gradient Boosting (XGBoost). Utilizing small-sample data from 87 shale gas wells in the study area, eight key controlling factors were identified, namely, total fracturing fluid volume, proppant intensity, average tubing head pressure, pipeline transfer pressure, casing head pressure, ceramic proppant fraction, fluid placement intensity, and flowback recovery ratio. These factors were used to train, optimize, and validate a productivity prediction model tailored for deep shale gas horizontal wells. The results demonstrate that XGBoost delivers the highest predictive accuracy and generalization capability, achieving an R² of 0.907 for productivity prediction—surpassing RF and SVR by 12.11% and 131.38%, respectively. Integrating SHapley Additive exPlanations (SHAP) interpretability analysis further enabled immediate post-fracturing productivity assessment and engineering parameter optimization. This research provides a reliable, data-driven strategy for predicting productivity and optimizing operations within the studied block, offering a valuable template for development in geologically similar areas. Full article

Achieving carbon peaking and neutrality is critical for global sustainability efforts and addressing climate change, yet improving land use carbon emission efficiency (LUCE) remains a challenge, especially in resource-dependent regions like Shanxi Province. Existing studies often overlook the spatial heterogeneity of LUCE and the mechanisms behind its driving factors. This study assesses LUCE disparities and explores low-carbon land use pathways in Shanxi to support its sustainable transition. Based on county-level land use data from 1990 to 2022, carbon emissions were estimated, and LUCE was measured using a three-stage super-efficient SBM-DEA model, with stochastic frontier analysis (SFA) to control for external noise. eXtreme Gradient Boosting (XGBoost) with SHAP values was used to identify key socio-economic and environmental drivers. The results show the following: (1) emissions rose 2.46-fold, mainly due to expanding construction land and shrinking cultivated land, with hotspots in Taiyuan, Jinzhong, and Linfen; (2) LUCE improved due to gains in technical and scale efficiency, while pure technical efficiency stayed stable; (3) urbanization and government intervention promoted LUCE, whereas higher per capita GDP constrained it; and (4) population density, economic growth, urbanization, and green technology were the dominant, interacting drivers of land use carbon emissions. This study integrates LUCE assessment with interpretable machine learning, demonstrating a framework that links efficiency evaluation with driver analysis. The findings provide critical insights for formulating regionally adaptive low-carbon land use policies, which are essential for achieving ecological sustainability and supporting the sustainable development of resource-based regions. Full article

Keyword data analysis aims to extract and interpret meaningful relationships from large collections of text documents. A major challenge in this process arises from the extreme sparsity of document–keyword matrices, where the majority of elements are zeros due to zero inflation. To address this issue, this study proposes a probabilistic framework called Bayesian Pattern Mining (BPM), which integrates Bayesian inference into association rule mining (ARM). The proposed method estimates both the expected values and credible intervals of interestingness measures such as confidence and lift, providing a probabilistic evaluation of keyword associations. Experiments conducted on 9436 quantum computing patent documents, from which 175 representative keywords were extracted, demonstrate that BPM yields more stable and interpretable associations than conventional ARM. By incorporating credible intervals, BPM reduces the risk of biased decisions under sparsity and enhances the reliability of keyword-based technology analysis, offering a rigorous approach for knowledge discovery in zero-inflated text data. Full article

Rhamnolipids are a class of glycolipids known for their surface and emulsifying activity. These molecules, produced by a few Gram-negative genera, mostly Pseudomonas, offer natural alternatives to synthetic surfactants in different industrial fields. This study examines the emulsifying and encapsulation performance of Rhamnolipids derived from the marine Antarctic bacterium Pseudomonas gessardii M15, comparing its emulsification ability and stability with those of commercial surfactants, Sodium dodecyl sulfate (SDS) and sucrose esters (SE), under extreme conditions of temperature and pH. The Rhamolipids were used to encapsulate Coenzyme Q10 with Arabic gum as the carrier matrix. Rhamnolipids exhibited surface and emulsifying activity comparable to that of SDS and superior to SE at neutral and basic pH levels. Their performance declined under acidic conditions, whereas exposure to 90 °C had no significant effects. The encapsulation efficiency of Coenzyme Q10 was significantly higher in the case of Rhamnolipids, with a percentage of encapsulated compound of 99.6 ± 0.2%, compared to the 38.2 ± 7.1% found when SDS was used. Rhamnolipids extracted from Pseudomonas gessardii M15 exhibit strong potential as a natural surfactant, particularly in formulations that require thermal stability and effective encapsulation. These findings support its use as a sustainable alternative to synthetic agents in diverse industrial settings. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

The increasing frequency of extreme high-temperature events has led to deteriorating thermal stability in power transmission lines and accelerated life of transformers. Conventional unit commitment (UC) employs static line rating (SLR) and neglects transformer lifetime degradation, posing hidden risks to system security in high-temperature and heavy-load scenarios. To address this challenge, this paper proposes a dispatch method that incorporates dynamic line rating (DLR) and transformer life loss under extreme high-temperature conditions. First, the conductor temperature-rise mechanism is formulated using the thermal balance theory, upon which a temperature-dependent DLR calculation model is developed. Second, the coupling relationship between transformer hot-spot temperature, load ratio, and ambient temperature is quantified, and an ambient temperature-driven transformer life cost function is formulated using linear damage accumulation theory. Finally, a unit commitment (UC) optimization model is established to minimize unit generation costs, transformer lifetime loss costs, and wind curtailment penalties costs, while satisfying power balance, transmission capacity, and other operational constraints. Simulation results on the IEEE 39-bus system demonstrate that, compared to conventional models, the proposed method improves transmission capacity utilization in high-temperature conditions by 12%, reduces transformer life loss costs by 69%, and lowers total operating costs by 4.9%. Full article

This study aimed to develop an industrially scalable coating route for enhancing the oxidation resistance of carbon fiber fabrics, a critical requirement for next-generation aerospace and high-temperature composite structures. To achieve this goal, synthesis of hexagonal boron nitride (h-BN) layers was achieved via a single wet step in which the fabric was impregnated with an ammonia–borane/THF solution and subsequently nitrided for 2 h at 1000–1500 °C in flowing nitrogen. Thermogravimetric analysis coupled with X-ray diffraction revealed that amorphous BN formed below ≈1200 °C and crystallized completely into (002)-textured h-BN (with lattice parameters a ≈ 2.50 Å and c ≈ 6.7 Å) once the dwell temperature reached ≥1300 °C. Complementary XPS, FTIR and Raman spectroscopy confirmed a near-stoichiometric B:N ≈ 1:1 composition and the elimination of O–H/N–H residues as crystallinity improved. Low-magnification SEM (100×) confirmed the uniform and large-area coverage of the BN layer on the carbon fiber tows, while high-magnification SEM revealed a progressive densification of the coating from discrete nanospheres to a continuous nanosheet barrier on the fibers. Oxidation tests in flowing air shifted the onset of mass loss from 685 °C for uncoated fibers to 828 °C for the coating produced at 1400 °C; concurrently, the peak oxidation rate moved ≈200 °C higher and declined by ~40%. Treatment at 1500 °C conferred no additional benefit, indicating that 1400 °C provides the optimal balance between full crystallinity and limited grain coarsening. The resulting dense h-BN film, aided by an in situ self-healing B₂O₃ glaze above ~800 °C, delayed carbon fiber oxidation by ≈140 °C. Overall, the process offers a cost-effective, large-area alternative to vapor-phase deposition techniques, positioning BN-coated carbon fiber fabrics for robust service in extreme oxidative environments. Full article

International medical graduates (IMGs) make up a significant proportion of general practitioners (GPs) in high-income countries such as the United Kingdom (UK), the United States of America (USA), Australia, and Canada. This paper compares views about IMGs with their own views in relation to the timing of GP placements in GP specialty training programs in the UK. It presents an inductive thematic analysis of focus groups with GP specialty trainers and trainees (149 participants across 32 focus groups), examining opinions about the ideal timing of GP placements. Trainers and home graduates argued that for home graduates, the ideal sequence depends on the trainee’s previous experience. They also suggested that IMGs should start in a hospital placement to develop familiarity with the healthcare system. In contrast, most IMGs expressed a preference for starting in a GP placement, so that they can gain an understanding of the requirements of their specialty as early as possible. There is a contrast between what IMGs said about themselves and the views shared by trainers and home graduates. This highlights the need to involve IMGs in the design of support programs targeted towards them. Recommendations include tailoring training to account for individual career paths and providing training about the healthcare system before the start of the first placement. This could improve the efficiency of GP training programs at a time of extreme pressure on healthcare systems and training providers. Full article

The mortality rate of first- to second-instar horseshoe crabs during molting is extremely high under culture conditions (pH of 7.6 ± 0.1, salinity of 27 ± 2, temperature of 26–32 °C), and we preliminarily speculate that it is related to disease. Our team found that Ectoplana limuli was attached to the ventral limbs of adult horseshoe crabs during culture. Parasite samples were collected from the external appendages and mouthparts of adult Tachypleus tridentatus for classification and identification. The primary objective of this experiment was to identify the species of this parasite and determine its taxonomic status. To this end, the experiment employed a combination of morphological methods and 18S rDNA gene molecular markers. The obtained sequences showed over 99% homology with Ectoplana limuli. Sequence alignment and phylogenetic tree results indicated that Ectoplana limuli showed a closer genetic relationship with Nerpa fistulata, but more distant relationships with Paucumara and Baikalobia. This is the first time that this parasite has been found in China, providing additional information for the study of horseshoe crab diseases. Full article

Soil contamination by heavy metals from industrial and mining activities poses a significant global threat to both environmental and human health, particularly in brownfields—abandoned or underutilized industrial areas that frequently accumulate pollutants. Climate change exacerbates this issue by intensifying extreme events such as floods, which can enhance contaminant mobility and compromise the reliability of conventional remediation methods. This study evaluated the in situ application of a sustainable soil washing technique based on hydrocycloning at a contaminated site in Canoas (Porto Alegre, Brazil), a flood-prone area heavily impacted by the 2024 climate disaster. The method physically separates heavy metals by concentrating them into a fine, high-contamination fraction for controlled disposal. Approximately 3019 m³ of soil was treated, recovering 93.4% of the material (coarse and fine sand) for potential reuse and isolating only 6.6% (200 m³) as hazardous waste. Chemical analyses confirmed that the recovered fractions complied with regulatory limits for heavy metals, while contaminants were effectively retained in the sludge and safely disposed of through landfills. During the April–May 2024 flood events, although the site was inundated, no significant erosion of the backfilled material was registered. The results support hydrocycloning-based soil washing as a robust and climate-resilient approach to adaptive remediation in contaminated environments. Full article