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Microbially induced calcite precipitation (MICP) is widely gaining popularity as a bio-mediated technique for soil stabilisation and recycling in an eco-friendly way. In the present study, MICP was employed to enhance the geotechnical properties of coal mine waste, a heterogeneous waste material unsuitable for engineering applications. To ensure the field-scale applicability of MICP and to address the shortcomings of the uniform distribution of calcite precipitations in high-fines-content matrices, a novel bacterial incorporation strategy was developed. This approach employed a native ureolytic bacterium, Sporosarcina pasteurii PS3A cells, for calcium carbonate precipitation through urea hydrolysis, resulting in the formation of mineral bridges that improved particle binding and reduced permeability. The chemical composition of the biogenic precipitate under different treatment conditions was probed by X-ray photoelectron spectroscopy, and its morphology was assessed using field-emission scanning electron microscopy. A ~ 17-fold increase in unconfined compressive strength in samples containing 22% fines was observed post-treatment. Optimisation of bacterial concentrations and cementitious solution molarity enabled precise control over volumetric shrinkage, which was reduced to 1-5%, improving dimensional stability across treated soils. The ultrasonic pulse velocity testing confirmed cementation, with a strong empirical correlation to unconfined compressive strength. These findings establish the necessity and effectiveness of targeted methodologies for applying MICP to fine-grained industrial wastes, advancing its viability as a sustainable alternative to conventional stabilisation techniques within the framework of the circular economy and low-carbon geotechnical engineering.

Fast-acting botulinum neurotoxins (BoNTs) are highly desirable for both medical and aesthetic indications, but the underlying mechanism for the differing onset of BoNTs' action remains unknown. Here, we demonstrate that the "belt" of BoNTs, a largely unstructured loop wrapping around their catalytic light chain (LC), is key to onset of intoxication. The more flexible BoNT/E belt promotes quicker LC translocation into the neuronal cytosol, leading to faster onset of action compared to BoNT/A. Furthermore, we discover a "belt-buckle" checkpoint that regulates this process. By loosening the BoNT/A belt-buckle via protein engineering, we enhance its sensitivity to acidic pH, leading to an accelerated onset of action. Conversely, locking the belt-buckle with an antibody neutralizes BoNT/A. Our findings open avenues for developing fast-acting BoNTs and effective countermeasures.

Compared with water-flooding reservoirs, gas-flooding reservoirs exhibit more complex characteristics, primarily attributed to the significant viscosity difference between the displacing phase and the displaced phase. Under the influence of the equation of state, gas viscosity and deviation factor are binary functions of temperature and pressure. As the reservoir recovery degree increases, regardless of the injection strategy employed, the reservoir pressure will present a complex heterogeneous spatial distribution. When analyzing the variation of gas saturation at different positions during the production process, it is necessary to consider the effects of reservoir pressure at the corresponding location on gas viscosity and compressibility. An extensive literature review reveals that most publicly published studies on fluid saturation in gas-flooding reservoirs are based on numerical reservoir simulation methods, where the gas state equation is introduced for quantitative description. Based on the Buckley-Leverett water-flooding equation, this study proceeds as follows: ① Firstly, a one-dimensional homogeneous radial flow tube model is established. Considering that the fractional flow curve is an S-shaped nonlinear function, a novel fully implicit method is adopted to automatically search and calculate the optimal water saturation value at each spatial step and time step. ② Secondly, considering the heterogeneity of the actual model, a multi-flow tube model accounting for permeability variation is further constructed; the fingering phenomenon of the displacing phase considering heterogeneity can be obtained by averaging the saturation distribution changes of different flow tubes. ③ Thirdly, considering the compressibility and viscosity variation of gas, the volume conservation in the water-flooding mode is converted into the mass conservation in the gas-flooding mode. Subsequently, combined with the Buckley-Leverett equation and the fully implicit solution applied to the multi-flow tube model, the fluid saturation distribution of immiscible gas flooding can be derived. The proposed method in this paper is applied to interpret and analyze the field data of an actual restricted channel gas-flooding case, and the theoretically calculated gas breakthrough time of the oil well is in high agreement with the actual data. This research method features rigorous theoretical derivation and has been verified to be feasible through practical application, thereby providing certain practical guidance and reference significance for reservoir engineering researchers.

A recurring challenge in science and engineering is the model-reality gap, where trusted legacy simulators lose fidelity due to unresolved physics or structural incompleteness. This challenge has motivated remedies ranging from imperfect mechanistic models to fully data-driven surrogates. Here, we address this gap with Alternating Neural Integrators (ANI), a non-intrusive reuse-and-correct framework for upgrading executable legacy simulators without requiring access to or modification of their internal implementation. Guided by operator-splitting principles, ANI alternates the evolution of a fixed prior simulator with a learned neural correction that targets structured discrepancy between the prior and the supervisory data. We show that ANI can recover missing coupling in chaotic systems and act as an effective subgrid correction in turbulence, improving dynamical fidelity where prior models drift. In selected mechanistically structured settings, post hoc symbolic distillation yields compact hypotheses and, in controlled benchmarks, supports closed-loop refinement of the prior. By combining data-driven flexibility with reusable scientific simulators in a theoretically grounded framework, this work provides a practical gray-box route for systematically upgrading existing computational infrastructure when a callable prior and supervisory data are available.

The complex mechanisms underpinning multiphase turbulent flows with dispersed particles play a crucial role in a wide range of natural and engineering systems. In many scenarios, rotation fundamentally modifies the transport, dispersion, and spatial organization of dispersed particles. Despite its relevance to geophysical, astrophysical, and industrial flows, publicly available datasets that simultaneously resolve carrier-flow dynamics and Lagrangian particle motion under controlled rotation are not available. In this regard, it is presented a high-resolution computational dataset of particle-laden turbulence with and without background rotation, generated using state-of-the-art direct numerical simulations. The data comprise fully resolved three-dimensional Eulerian fields coupled with time-resolved Lagrangian trajectories of sub-Kolmogorov point particles spanning tracer and inertial regimes. Simulations cover a wide range of rotation rates, enabling systematic investigation of rotation-induced anisotropy. For each case, particle positions and velocities are recorded over many integral time scales, together with Eulerian flow fields, allowing detailed physical and spectral analyses. The data are organized to support both Eulerian and Lagrangian perspectives and provide a reusable benchmark for studying particle transport, preferential concentration, and dispersion.

Rising demand for colorectal cancer screening has increased colonoscopy volume, producing backlogs and prolonged wait times. Commonly used metrics such as room utilization provide limited insight into actionable drivers of efficiency; we applied a novel throughput benchmarking framework to quantify performance and identify operational sources of inefficiency. We conducted a pilot study in a four-room outpatient endoscopy unit from July-December 2022. Using routinely recorded timestamps for procedural stages, we derived a realistic daily target throughput (RDTT). Monthly realistic target throughput (RMTT) was calculated from RDTT. Actual monthly throughput (AMT) was benchmarked against RMTT to generate a throughput efficiency ratio (TER = AMT/RMTT) and throughput variance (TV = RMTT - AMT), which was decomposed into seven predefined operational factors. AMT ranged from 567 to 717 procedures per month, whereas RMTT ranged from 1,165 to 1,332, yielding a mean TER of 53%. The largest contributors to variance were rooms closed due to lack of endoscopist assignment (45%), workflow delays (21%), and patient no-shows or late cancellations (11%). In this pilot, we demonstrate that using commonly captured operational metrics, throughput benchmarking provides a practical framework to quantify efficiency and identify specific, actionable drivers of underperformance in endoscopy operations.

Radicals arranged in a two-dimensional (2D) hexagonal network can offer various exotic magnetic, electronic, and optical properties that find application in electronics/spintronics. However, direct synthesis remains challenging due to the scarcity of stable, symmetry-matched radical building blocks. Here, we report the bottom-up synthesis of hexagonal 2D radical covalent organic frameworks (RCOFs) with unpaired electrons at the nodes of the frameworks. A planar verdazyl radical amine (V-NH2) undergoes Schiff-base condensation with aldehydes to afford highly crystalline hexagonal RCOFs (VTPT and VPMT). The spin density was precisely controlled through the selection of building blocks with modulated spin-spin distances. The EPR and SQUID measurements confirmed a high spin concentration with antiferromagnetic interactions at low temperature, which is further tuned by interlayer interactions. Thin films of VTPT exhibited preferential in-plane orientation with enhanced photoconductivity, attributed to improved π-conjugation. These findings establish a direct route to RCOFs and underscore their potential as pseudo 1-dimensional antiferromagnetic materials.

The present study investigates the structural performance of uniformly tapered hollow roller (UTHR) and uniformly tapered layered hollow roller (UTLHR) bearings with varying hollowness levels using finite element analysis (FEA) and experimental validation. A comprehensive numerical investigation was conducted for hollowness levels ranging from 30% to 80% to evaluate maximum deflection, bending stress, von Mises stress, contact pressure, endurance-limit loading, and radial stiffness. The finite element results identified an optimum hollowness range of approximately 31-40%, where stress redistribution was achieved without excessive loss of stiffness. The analytical study predicted applied radial load of 30 kN, with a deviation of 0.1412%, which is validating the theoretical formulations. Moreover, simulations results realized reduced stress concentrations and optimized stiffness at optimum hollowness. The layered hollow roller configurations dominate over single hollow roller design in terms of lower contact pressure and improved stress distribution. The experimentally measured static failure loads were 53.01 kN and 76.17 kN for the UTHR and UTLHR bearings, respectively. The experimental results showed excellent agreement with the finite element predictions, with deviations below 2.5%. Accordingly, the effective similarities between the predicted and experimental results shows the reliability of the adopted modelling approach and highlights the structural advantages of layered hollow rollers within an optimized hollowness range.

Improving seed germination is essential for enhancing crop establishment under increasingly variable environmental conditions associated with climate change. Magnetic field (MF) treatment represents a clean, non-chemical, and sustainable seed-priming approach; however, frequency-dependent biological responses remain insufficiently understood. This study investigates the responses of aniseed (Pimpinella anisum L.) to static (DC) and low-frequency alternating magnetic fields (5, 10, and 15 Hz) across different exposure durations. Germination parameters (percentage, speed, vigor index), physiological traits, and activities of key hydrolytic and antioxidant enzymes (α-amylase, protease, catalase) were assessed. Furthermore, the molecular expression of the stress-responsive superoxide dismutase (SOD) and the cytoskeletal actin genes was analysed. MF significantly enhanced germination percentage (up to a 25% increase), mean germination time, and vigor indices compared to the untreated controls. Physiologically, treated seedlings exhibited higher antioxidant defense levels. At moderate frequencies, catalase activity increased, while α-amylase and protease were markedly elevated at higher frequencies, enabling reserve mobilization and stress tolerance. At the molecular level, sod transcripts were down-regulated across all MF treatments compared to the control, indicating a functioning oxidative stress response. These findings demonstrate that MF frequency modulates the integration of physiological (enzyme-driven metabolism) and molecular (antioxidant gene regulation) pathways to optimize during aniseed germination. This research provides mechanistic insights and presents low-frequency MF as a viable seed priming for sustainable crop improvement under dynamic environments.

Precise control and a clear understanding of the interfaces between 2D and 3D perovskites remain limited by structural disorder, interfacial defects and poorly defined growth pathways. Here we report a strategy for coherent van der Waals cross-dimensional epitaxy, in which 3D MASnI3 (MA: methylammonium) single crystals are grown directly on 2D (3T)2SnI4 (3T: tri-thiophenylethylammonium) templates. This approach achieves deterministic control over domain orientation, density and coverage, yielding planar heterostructures with atomically sharp and structurally coherent interfaces. Low-dose aberration-corrected transmission electron microscopy and ptychographic imaging resolve the heterointerface in real space, revealing ordered spacer ligands and uniform interfacial passivation. The epitaxial growth tolerates topological defects in the 2D templates, giving rise to spiral heterostructures with pronounced chiroptical responses. The method further extends from microscale domains to macroscopic single-crystal heterostructure thin films, bridging fundamental epitaxy and device-relevant architectures. These heterostructures support efficient charge separation and transport, exhibiting gate-tunable rectification ratios exceeding 106 with robust operational stability. These results define a general route to coherent cross-dimensional epitaxy and establish a versatile platform for scalable perovskite optoelectronics.

This paper proposes a tropical cyclone formation prediction network based on pyramid attention feature extraction and multi-scale feature fusion, aiming to enhance the prediction of whether tropical cloud cluster (TCC) precursors intensify to tropical storm (TS) strength by integrating multi-source reanalysis data. First, reanalysis data from ERA5 and NCEP/NCAR are represented as images at different scales and labeled according to tropical cyclone (TC) formation events derived from the TCC and IBTrACS datasets. The labels include information on whether TC formation occurred and its location. Then, a feature extraction module based on a Pyramid Attention Mechanism (PAM) is designed to extract features related to TC formation. Next, the features at different scales are input into a PAM-based feature fusion module that dynamically generates weights for different features to perform weighted fusion and unify the feature scales. Finally, a lightweight Convolutional Neural Network (CNN) is designed as the prediction module to predict TC formation occurrence and location. Experimental results over five independent data splits show that at a lead time of 24 hours, the proposed method achieves a Probability of Detection (POD) of [Formula: see text], a False Alarm Ratio (FAR) of [Formula: see text], and a location prediction Root Mean Square Error (RMSE) of [Formula: see text] grid units (≈437 km), demonstrating competitive performance in balancing POD and FAR. Ablation studies confirm the contribution of each proposed module to overall performance.

Hydroboration of alkenes constitutes an important synthetic tool to assemble valuable alkylboron molecules. Although various protocols have been developed, the regiospecific Markovnikov reactions of unactivated alkenes, in particular for trisubstituted ones, remain formidable challenges. Here, we report a general method for the Markovnikov hydroboration of unactivated mono-, di- and trisubstituted alkenes enabled by dual photoredox/cobalt catalysis. The reaction proceeds via a photoinduced metal-hydride hydrogen atom transfer of unactivated alkenes followed by a versatile radical borylation process. This strategy is characterized by its Markovnikov selectivity, mild reaction conditions, high catalytic efficiency and broad substrate scope. This method demonstrates a reaction pattern that achieves the hydroboration of unactivated alkenes through the combination of reductive metal-hydride hydrogen atom transfer and radical borylation. Moreover, this catalytic system can also be applied to complex substrates derived from natural products. The mechanism presented is supported by control experiments and density functional theory calculations.

The selection of suitable nuclear waste disposal sites is a long and challenging process, involving multiple technical and environmental assessments. For data-integrated simulation models to function as credible decision-support tools in this sensitive context, their data sources must meet rigorous transparency and reproducibility standards. This paper introduces a Smart Data Hub that provides the reproducible data foundation required to address the limits found in existing research, which rarely features both a dataset containing uncertainty information and an effective way of application for specific use cases. The Smart Data Hub is an integrated solution consisting of two main components: a dataset compiled from 50 literature sources covering geological information, structural data, and rock properties for potential repository sites in Germany, and a functional module supporting effective assembly of data compilations for a given geological structure. This approach provides reliable, reproducible data compilation for model-based decision support in the site selection process by providing transparent, uncertainty-informed data coupled with intelligent assembly capabilities.

Global climate crisis and waste disposal costs drive the need for circular industrial models. This study investigates whether industrial symbiosis through co-disposal of papermaking waste and blast furnace slag can convert these materials from waste to resources. Using a system expansion Life Cycle Assessment framework, we assessed alkali-activated mortars based on global warming potential, water footprint, and toxic impacts. Results indicate that high-volume waste substitution significantly improves the material's environmental profile, achieving a net-negative Global Warming Potential of - 7.9 kg CO2 eq/m³ and a 129% net environmental benefit for human health damage compared to the baseline. These results occur because the avoidance of landfill-related greenhouse gas emissions and primary material production outweigh the impacts of chemical activation. This study outlines a structured approach to decarbonizing construction materials. It shows how technological innovation can strengthen competitiveness within circular economic systems. This work verifies the technical feasibility of regenerative material strategies and identifies activator optimization as a critical factor for advancing next-generation sustainable materials, thereby offering practical guidance to help industrial sectors meet global sustainability requirements.

Small object detection in drone aerial photography faces challenges such as small scales, inconspicuous features, and complex backgrounds. To address these issues, this paper proposes an improved detection model based on the YOLO framework. First, a multi-channel feature extraction module-Deconvolutional Network combined with the C3 module (deconv-c3k2)-is designed to enhance feature extraction and multi-scale representation capabilities. Second, an enhanced Auxiliary Head detection module is introduced to improve feature interaction and collaboration across different levels. Concurrently, the NWD-Inner-CIoU loss function is adopted to mitigate the impact of IoU on small target localization offset, thereby boosting detection accuracy. To meet real-time embedded deployment requirements, an L1 pruning strategy is employed to reduce the model parameter size. Experimental results demonstrate that the proposed method significantly outperforms baseline models on the HIT-UAV dataset, achieving 81.9% on mAP@0.5 and 51.4% on mAP@0.5:0.95, respectively. Inference speed increases by 8.32%, while parameter count decreases by 20.8%. Computational load was reduced by 0.36 GFLOPs, and model size shrinks to 79.2% of the original. Furthermore, experiments on the IRay Infrared Dataset validate the method's generalization capability. Overall, the proposed approach demonstrates distinct advantages in detection accuracy, real-time performance, and lightweight design, while exhibiting stability and practical value.

Tropical rainforests, particularly the Amazon, function as the Earth's lungs yet absorb mercury (Hg) emitted worldwide. By introducing climate-driven variations in foliar functional traits into a global model of forest Hg uptake, we uncovered an inter-continental spatial decoupling between Hg sources and sinks. Unexpectedly, the minimally industrialized rainforests of South America and Africa exhibit the world's highest atmospheric Hg accumulation rates and greatest biomass, thus disproportionately sequestering Hg released from industrialized regions. This imbalance arises from climate-specific leaf traits that enhance Hg fixation towards lower latitudes. The model constrains global forest Hg uptake to 1155 ± 422 Mg yr-1, sharply reducing prior uncertainties (320-3138 Mg yr-1) and nearly equilibrating with global litterfall deposition (1180 ± 710 Mg yr-1). These findings urge a re-assessment of the Minamata Convention's effectiveness and highlight the vulnerability of tropical forests to anthropogenic Hg inputs and to climate-induced shifts in vegetation and terrestrial Hg reservoirs.

The Gastrointestinal (GI) tract plays a vital role in digestion by breaking down food into essential nutrients. Disorders such as bleeding lesions, ulcerative colitis, Inflammatory Bowel Disease (IBD), constipation, diarrhea, abdominal pain, nausea, and vomiting may indicate serious chronic conditions, including cancer. GI malignancies are among the leading causes of cancer-related mortality worldwide; however, early and accurate diagnosis can significantly reduce fatality rates. Existing endoscopic AI systems often struggle to generalize across datasets. They also face challenges in capturing both local lesion characteristics and long-range contextual information. To address this, the proposed study explores deep learning-based methods for automated classification of gastrointestinal diseases using endoscopic images. Two models were developed: a modified Inception architecture with four blocks and a customized Vision Transformer (ViT-3) consisting of three transformer encoder blocks. Deep features were extracted from both models, which were fused using a weighted fusion strategy. The redundant features were reduced using the Tree Growth Algorithm (TGA). The optimized feature set was then classified using machine learning classifiers to improve diagnostic performance. To evaluate its effectiveness, the framework was tested on two widely used benchmark datasets, Kvasir v1 and Kvasir v2, which include 4,000 and 8,000 images, respectively across eight GI disease categories. On the Kvasir v1 dataset, the proposed method achieved an accuracy of 98.9%, along with sensitivity, precision, and F1-score values of 98.87%, 98.86%, and 98.86%, respectively. Similar performance was observed on the Kvasir v2 dataset. These findings demonstrate that the proposed method provides a reliable and effective solution for early identification and classification of gastrointestinal diseases from endoscopic images.

Reliable prediction of strata-pressure evolution is essential for intelligent longwall mining, but steeply inclined panels show spatially heterogeneous support loading that challenges short-term warning. Here we analyse two months of hydraulic-support data from Panel II1013 in the Huaibei mining area and develop a local prediction workflow for support-pressure states. Missing and zero values were repaired, random measurement noise was reduced using a one-dimensional Kalman filter, and normalized sliding-window samples were used to compare CNN, LSTM, CNN-LSTM, Transformer and CNN-LSTM-Attention models against persistence and BP neural network baselines. Data analysis revealed a persistent high-pressure concentration from the middle to upper face. Under matched data partitioning, CNN-LSTM-Attention achieved the lowest test RMSE and highest R2 (RMSE 0.8632 ± 0.0615; MAE 0.4233 ± 0.0902; MAPE 2.5194 ± 0.4429%; R2 0.9891 ± 0.0016), reducing RMSE by 1.80% relative to BP and 9.60% relative to persistence. In a held-out 1000-sample window, the model achieved a relative regression accuracy of 98.17% (1-MAPE). A bounded multi-step validation on four representative supports, using a 24-point input window (approximately 2 h), yielded valid forecast horizons of 5-10 h when both horizon-level and farthest-step MAPE were ≤ 10%. These results support CNN-LSTM-Attention as a local single-support prediction module for graded warning assistance. Broader deployment will require multi-support spatiotemporal modelling and field validation of closed-loop support-control decisions.

Coupled mode theory (CMT) is a universal method for studying resonant systems in various disciplines in science. Combined with traditional fitting methods, implicit physical parameters of the resonant systems can be revealed. However, this methodology fails in tackling the scenario of multi-solution for a given resonant system, resembling a fundamental challenge that has not been addressed yet. In this work, we propose and experimentally demonstrate a CMT physics and data co-driven deep neural network (CMT-NN) that can predict the implicit physical parameters of complex resonant systems in a rapid and precise way. More importantly, the challenge of multi-solution is mitigated by incorporating physical eigenvalues and response of the system in evaluating the physics consistency of the neural network. The applicability and generality of CMT-NN are demonstrated by simulations and experiments, where the CMT-NN can capture subtle spectral features and learn the coupling physical properties effectively. Compared with the traditional fitting method, the average computation time has been reduced by three orders of magnitude and the prediction performance is improved by more than two orders of magnitude. Displacement sensing experiments further validate the robustness of CMT-NN. It is anticipated that the CMT-NN can provide a paradigm shift in using the CMT for studying resonant systems and shed new light on the understanding, design and optimization of various coupled resonant systems.