Abstract The limited design strategy of three‐dimensional covalent organic frameworks (3D COFs) greatly restricts their structural diversification and potential applications. Herein, we propose an inwardly directed linker propagation strategy for the targeted assembly of 3D COFs (COF‐IN‐1 and COF‐IN‐2) and compare them with outwardly directed expanded COFs (COF‐OUT‐1 and COF‐OUT‐2). COF‐OUTs exhibit planar heteroporous 2D frameworks with cpt topology, while COF‐INs engineer controlled triple entanglements into networks, forming 3D frameworks with acs topology. Moreover, the COFs assembled via inwardly directed linker propagation effectively enhanced the production of H 2 O 2 photosynthesis. To demonstrate the application potential, a biphasic fluid system was constructed for continuous H 2 O 2 photosynthesis and extraction. This work not only expands the design strategy for achieving 3D COFs but also demonstrates that the dimensional regulation of frameworks and tuning of applications can arise from different expanding directions of the linkers.
This collection of articles aims to provide a pioneering introduction in this journal to the use of artificial intelligence in ground improvement research and applications, coupled with experimental testing and computational methods. Using optimisation algorithms, probabilistic modelling, and machine learning (ML), they advance prediction of settlement, strength, and failure behaviour across soil–cement systems, fibre-reinforced composites, jet-grouting, alkali-activated binders, and embankments.The paper by Nima et al. (2025) evaluates the performance of soil–cement (SC) columns in improving the seismic stability of soft soils through combined experimental and numerical analyses. The study demonstrates that SC columns substantially reduce settlement under both rigid and ductile overburdens and that a grey wolf optimisation framework, supported by artificial neural networks, can reliably identify optimal design parameters. While the proposed model provides a comprehensive tool for seismic soil improvement, further field validation is recommended to enhance its practical applicability.Collico et al. (2025) present a probabilistic Bayesian framework for predicting unconfined compressive strength (UCS) and diameter properties of jet-grouted columns during preliminary design. Drawing on a regional data set of soil and system parameters, the method combines local and most-similar site information to improve prediction accuracy under data-limited conditions. The approach offers a more reliable alternative to conventional empirical or theoretical correlations, supporting cost-effective and informed decision-making in early project phases, although further data enrichment is needed to enhance prediction robustness.Martins et al. (2025) apply a novel design of experiments (DOE) methodology to evaluate fibre-reinforced cement-stabilised soils, enabling prediction of both strength and failure mode while quantifying the influence of key parameters. Results show that binder content is the dominant factor governing UCS, while polypropylene fibres outperform sisal in improving ductility. The brittleness index was validated as a reliable criterion for distinguishing failure modes, and the DOE model demonstrated high accuracy with reduced testing effort, offering a robust framework for predictive analysis, though further validation across wider soil, binder, and fibre conditions is recommended.Tinoco et al. (2025) investigate the use of ML to predict the UCS of soils stabilised with one-part alkali-activated binders, offering a sustainable alternative to Portland cement. Despite a limited data set, random forest, neural networks, and support vector machines achieved high predictive accuracy, with water and soil content identified as the most influential parameters. The study demonstrates the potential of ML as a pre-design tool to optimise soil stabilisation, reduce reliance on laboratory testing, and support sustainable construction, while underscoring the need for broader data sets and further model validation.Jones et al. (2025) apply Bayesian updating to assess embankment performance on soft ground, comparing models with different soil layering and random variables. Results show that while both four- and nine-layer models can reproduce monitoring data, the nine-layer model yields more realistic posterior parameters, especially when only surface settlement data are available. Incorporating magnetic extensometer and piezometer data significantly improves prediction quality, underscoring the importance of high-quality field monitoring, while highlighting that computational efficiency depends more on hardware and software optimisation than on model simplification.These contributions reveal an emerging direction in ground improvement, where artificial intelligence takes a central role, enhanced by experimental studies and computational modelling. Together, they point to smarter, data-driven design tools that enhance sustainability and bridge laboratory insights with field performance, although broader data sets and large-scale validation are still needed.
As a crucial agricultural crop in China, litchi exhibits a biennial bearing pattern with alternating high-yield and low-yield cycles, known as on-year and off-year respectively. Research has identified unstable floral initiation as the primary cause of irregular fruiting in mid-to-late maturing cultivars. Rapid and accurate quantification of female to male flower ratios during the flowering phase enables targeted management strategies to optimize floral development and enhance fruit-setting rates. This study proposes Flower Quantification and Gender Recognition Network (FQGR-Net), a three-branch neural network architecture for simultaneous classification and counting of female and male flowers. Through module-level optimization, FQGR-Net improves both counting accuracy and computational efficiency, achieving average MAE of and RMSE of across categories in experiments conducted on the self-constructed dataset. Comparative experiments with other deep neural network models on public datasets show the proposed method achieves optimal performance. A regression analysis between predictions and ground truth produces values of and for female and male flower quantification respectively. A dedicated litchi flower phenotyping analyzer was developed to address the technological gap in automated floral census systems. Field trials demonstrated over accuracy in female/male flower counting.
Aim To observe the effect of Vitamin B 6 on reduction of the main side effects of Praziquantel for schistosomiasis.Methods Villagers aged 6-60 in 328 endemic villages were selected and divided into experimental and control groups.The experimental group were given single oral dose of both praziquantel and Vitamin B 6,while the control group were given single oral dose of praziquantel and placebo.The dosages of praziquantel were,body weight limit being below 60 kg.45mg /kg for those of 6-14 years old and 40mg/kg for those aged 15-60.Dosages of Vitamin B 6 were 20mg per capita for those of 6-14 years old and 30mg/kg per capita for those aged 15-60.Results Occurrence rate of praziquantel side effects in the experimental group and in the control group were 73 78% and 81 10% respectively,showing significant difference (P0.05).The Occurrence rates of main side effects on the nervous and digestive systems were 72.86% in the experimental group and 79.57% in the control group respectively,also indicating significant difference (P0.05).By contrast of disappearance time of dizziness within 2,4,6,8,and 10 hours,the accumulated disappearance rate of side effects of the experimental group was obviously higher than that of the control group.Conclusion Vitamin B 6 is effective in reducing occurrence rate of the main side effects of Praziquantel for schistosomiasis and speeding up the disappearance time of dizziness.
Read moreAbstract The promising results exhibited by industrial waste, notably fly ash (FA) and blast furnace slag (BFS), position them as potential supplementary materials for reducing cement usage in concrete to reduce environmental impact. This study presents a novel stacking‐based approach to enhance the accuracy of compressive strength (CS). The stacking model integrates ensemble learning methods, including Random Forest (RF), Gradient Boosting (GBR), Extreme Gradient Boosting (XGB), and a Bi‐directional Long Short‐Term Memory (Bi‐LSTM) as the base model, and a Catboost regressor as the meta‐estimator. The dataset is subjected to the grid search method and 10‐fold cross‐validation to assess the model for all base models, resulting in an R 2 of over 0.9 and R 2 of 0.9676 in the stacking model. The study utilizes Shapley Additive Explanations (SHAP) analysis to enhance model explainability, revealing how features like cement, BFS, and FA interact to influence CS. Further, SHAP interaction plots confirm that BFS in the 200–350 kg/m 3 range, FA in the 180–200 kg/m 3 , and SP of 20–30 kg/m 3 can be ideal for developing sustainable concrete. Additionally, the research highlights that concrete age, up to 200 days, correlates with increased CS. A composition‐based relationship between the input features, mainly industrial waste, and the target features is explained using the reverse design method, which relies on SHAP results. These findings suggest that the stacking model outperformed all employed base models, providing a comprehensive and robust methodology for adopting sustainable construction practices.
Read moreThe frequency data is generated through ANSYS modeling. The model is created through an eigenvalue analysis of a cantilever beam with different distributed damages.
Read moreThis study investigates how second-language (L2) listeners from five first-language (L1) backgrounds—English, Dutch, Mandarin, Spanish, and Korean—perceive English lexical stress, focusing on their use of vowel quality, pitch, and duration cues. Participants completed a cue-weighting perception task (Tremblay et al., 2021) in which two acoustic dimensions were manipulated orthogonally while the third was neutralized. Data for Dutch listeners come from the original study. Predictions about cross-linguistic transfer were based on the functional weight of each cue in the L1. The following L1 effects were predicted: For vowel quality: English, Mandarin > Dutch > Spanish, Korean; for pitch: Mandarin > Korean > Dutch, Spanish > English; for duration: English, Mandarin > Dutch, Spanish > Korean. Bayesian mixed-effects models tested the effects of cues and L1 with L2 proficiency (Lemhöfer & Broersma, 2012) as a covariate. The results aligned broadly with our predictions: for vowel quality, English > Mandarin > Dutch > Korean > Spanish; for pitch: Mandarin > Korean, Dutch > Spanish > English; for duration: English, Mandarin, Dutch > Spanish > Korean. These findings support a cue-weighting typology shaped by L1-specific cue prominence, with implications for theories of transfer and perceptual learning in L2 acquisition.
Read moreDamaged RC structures are typically strengthened in the load-carrying state in practice, which limits the improvement of their mechanical performance due to stress lag in the strengthening components. In this study, the shear performance of damaged thin-walled web RC beams strengthened with an ultrahigh-performance concrete (UHPC) layer and postinstalled adhesive bolts under secondary loading was experimentally and numerically investigated. Direct shear tests were first conducted to study the interfacial shear behavior between UHPC and normal concrete with different interface treatments. Subsequently, the three-point bending tests were performed to assess the performance evolution of the strengthened beams under secondary loading, i.e., strengthening under sustained loading conditions. The results demonstrated that even under secondary loading, the UHPC layer could still effectively constrain crack propagation and improve the shear performance of the damaged RC beams. However, due to the strain lag of UHPC induced by secondary loading, the UHPC strength was not fully utilized, leading to 15.7% and 10.8% reductions in the stirrup yielding load and ultimate load, respectively, compared with the strengthened beam without secondary loading. Finite-element analysis further indicated that the detrimental effect of secondary loading intensified with increasing sustained load levels but was mitigated as the UHPC layer thickness and postinstalled bolt ratio increased. Based on the experimental and numerical results, a combined surface treatment of mechanical chiseling followed by high-pressure water jetting at 80 MPa is recommended for effective UHPC-based shear strengthening of thin-walled web RC beams.
Read moreThe project is to share an database on mechanical and rheological Properties of Regolith Simulants, particularly for 3D printing. More than 1000 data are collected from different resources for different types of regolith simulants, including their mechanical properties such as compression, tensile, bending strength, hardness, different printing conditions, and different types of printing methodologies. Their rheological properties of regolith simulants are also included, such as their mix ratios, their activator solutions, their setting times, and their viscosities. This database will provide an overview for future researchers on how they could select regolith simulants for their research and how their data compared to the existing data. The simulants included are JSC-1AC, JSC-1A, NU-LHT-2M, Basalt, PA12, KLS-1, CLRS-1 + 4.6% ilmenite, CLRS-1 + 28.5% ilmenite, FJS-1, CUG-1A, CUG-MT, CUG-HT, CUG-1A, CUG-MT, CUG-HT, CUG-1A, CUG-MT, CUG-HT, JSC-1A without ilmenite, JSC-1A + 5 weight% ilmenite, JSC-1A + 10 weight% ilmenite, JSC-1A + 5 weight% fine ilmenite, JSC-1A + 20 weight% ilmenite, JSC-2A, DNA. DNA-1 lunar regolith simulant (analog to lunar mare regolith), LHS-1 lunar highlands simulant, LMS-1 lunar mare simulant, GVS (Ground Volcanic Scoria) lunar regolith simulant, LRS-1 lunar regolith simulant; targets Apollo-17 samples; mean particle diameter 44 μm; contains plagioclase, olivine and glass, LRS-2 lunar regolith simulant; used as a lunar simulant targeting Apollo-14 samples, LRS-3 lunar regolith simulant; used as a lunar simulant targeting Apollo-12 samples, Lunar regolith simulant (AGK-2010), quartz powder (QP), standard sand Lunar regolith simulant (EAC-1a) + PEEK powder, BH-1 lunar regolith simulant + NaOH solution (alkali activator), GCD-1 lunar regolith simulant (basalt, anorthite, albite, pyroxene, ilmenite, slag, fly ash), BH-1 lunar soil simulant (volcanic scoria, feldspar-rich), LHS-1 (lunar highlands simulant) + LMS-1 (lunar mare simulant), Lunar regolith (highland, mare, simulants) Lunar regolith (various simulants across multiple studies), DNA-1 lunar regolith simulant + NaOH + urea (superplasticizer), LHS-1 lunar highlands simulant; particle size 0.04–1000 μm, median 98 μm; bulk density 1.27 g/cm³, LMS-1 lunar mare simulant; particle size 0.04–300 μm, median 45 μm; bulk density 1.56 g/cm³, TJ-1 simulated lunar soil, Basaltic volcanic slag lunar soil simulant (albite, anorthite, augite, olivine phases) Lunar aggregate simulant (LAS) based on ilmenite rock; reference mix with standardized sand, HIT-L-1 lunar regolith simulant (volcanic scoria) 10 lunar soil simulants (LHS-1, AGK2010, OPRL2N, JSC-1A, CHENOBI, LMS-1, ESA 06-A, ESA 01-E, UoM-B, UoM-W), Lunar regolith simulant BH-1 lunar regolith simulant, HIT-LRS-1 lunar regolith simulant, Lunar regolith simulant, BH-2 lunar regolith simulants (from volcanic scoria, China), Metakaolin-based geopolymer
Read moreABSTRACT This study investigates the effectiveness of steel‐reinforced grout (SRG) for strengthening continuous reinforced concrete (RC) beams. A more sustainable alternative to conventional cementitious mortars, geopolymer mortar was employed in SRG‐strengthened RC beams while maintaining structural efficiency. An extensive experimental program involving 15 two‐span continuous RC beam specimens was conducted. The experimental parameters considered include the overall SRG stiffness (by varying the number of fabric layers and fabric density), the span coverage ratio, and the locations of strengthening. Additionally, the performance of steel fabrics in SRG was compared with other types of fabrics, including carbon, glass, and polyparaphenylene benzobisoxazole (PBO) fabrics. The results revealed that strengthening significantly enhanced the flexural capacity of the beams, with improvements ranging from 24% to 81%. Steel fabrics in SRG outperformed all other fabric types in terms of load‐carrying capacity. Low‐density SRG demonstrated superior bonding with the concrete substrate, leading to enhanced strengthening effects compared to high‐density SRG. Several failure modes were observed, including steel yielding, concrete cover separation, fabric rupture and slippage, and SRG debonding. A theoretical model based on SRG effective strain was utilized to predict the maximum load capacity of the strengthened beams.
Read moreAbstract Reinforcement corrosion is a major cause that leads to the deterioration of reinforced concrete (RC) structures. As a result, quickly assessing its impact on structural components has become a priority in recent research. This study examines the use of machine learning (ML) models, an advanced approach in structural engineering, to predict the performance of corroded RC beams, specifically their load‐carrying capacity and bending moment. A substantial database of 804 beam samples is utilized, consisting of 649 corroded beams and 155 uncorroded beams, to capture the complex relationships between input features and structural responses. Nine ML models are trained and compared to determine their effectiveness for this task. Additionally, advanced optimization techniques are employed to improve the predictive accuracy and feature selection of the best‐performing models. Finally, the optimized models are integrated into graphical user interfaces, offering a practical tool to support future research and facilitate predictions regarding the performance of corroded RC beams.
Read more
.webp){kind=small}
.webp){kind=small}
.webp){kind=small}
