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Hydroxyapatite and β-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: ‘Model 1′, which predicts material properties from their fabrication conditions, and ‘Model 2′, which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability.

This paper investigates the accuracy of Reynolds-averaged Navier-Stokes (RANS) turbulence modelling applied to complex industrial applications. In the context of the increasing instability of the energy market, hydropower plants are frequently working at off-design parameters. Such operation conditions have a strong impact on the efficiency and life span of hydraulic turbines. Therefore, research is currently focused on improving the design and increasing the operating range of the turbines. Numerical simulations represent an accessible and cost efficient alternative to model testing. The presented test case is the Porjus U9 Kaplan turbine model operated at best efficiency point (BEP). Both steady and unsteady numerical simulations are carried out using different turbulence models: k-epsilon, RNG k-epsilon and k-omega Shear Stress Transport (SST). The curvature correction method applied to the SST turbulence model is also evaluated showing nearly no sensitivity to the different values of the production correction coefficient Cscale. The simulations are validated against measurements performed in the turbine runner and draft tube. The numerical results are in good agreement with the experimental time-dependent velocity profiles. The advantages and limitations of RANS modelling are discussed. The most accurate results were provided by the simulations using the k-epsilon and the SST-CC turbulence models but very small differences were obtained between the different tested models. The precision of the numerical simulations decreased towards the outlet of the computational domain. In a companion paper, the pressure profiles obtained numerically are investigated and compared to experimental data.

Background Diabetic kidney disease (DKD) has been the leading cause of chronic kidney disease in developed countries. Evidence of the benefits of resveratrol (RES) for the treatment of DKD is accumulating. However, comprehensive therapeutic targets and underlying mechanisms through which RES exerts its effects against DKD are limited. Methods Drug targets of RES were obtained from Drugbank and SwissTargetPrediction Databases. Disease targets of DKD were obtained from DisGeNET, Genecards, and Therapeutic Target Database. Therapeutic targets for RES against DKD were identified by intersecting the drug targets and disease targets. GO functional enrichment analysis, KEGG pathway analysis, and disease association analysis were performed using the DAVID database and visualized by Cytoscape software. Molecular docking validation of the binding capacity between RES and targets was performed by UCSF Chimera software and SwissDock webserver. The high glucose (HG)-induced podocyte injury model, RT-qPCR, and western blot were used to verify the reliability of the effects of RES on target proteins. Results After the intersection of the 86 drug targets and 566 disease targets, 25 therapeutic targets for RES against DKD were obtained. And the target proteins were classified into 6 functional categories. A total of 11 cellular components terms and 27 diseases, and the top 20 enriched biological processes, molecular functions, and KEGG pathways potentially involved in the RES action against DKD were recorded. Molecular docking studies showed that RES had a strong binding affinity toward PPARA, ESR1, SLC2A1, SHBG, AR, AKR1B1, PPARG, IGF1R, RELA, PIK3CA, MMP9, AKT1, INSR, MMP2, TTR, and CYP2C9 domains. The HG-induced podocyte injury model was successfully constructed and validated by RT-qPCR and western blot. RES treatment was able to reverse the abnormal gene expression of PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR. Conclusions RES may target PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR domains to act as a therapeutic agent for DKD. These findings comprehensively reveal the potential therapeutic targets for RES against DKD and provide theoretical bases for the clinical application of RES in the treatment of DKD.

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

In this paper, a simplified model of a Polymer Electrolyte Membrane (PEM) water electrolysis cell is presented and compared with experimental data at 60 °C and 80 °C. The model utilizes the same modelling approach used in previous work where the electrolyzer cell is divided in four subsections: cathode, anode, membrane and voltage. The model of the electrodes includes key electrochemical reactions and gas transport mechanism (i.e., H2, O2 and H2O) whereas the model of the membrane includes physical mechanisms such as water diffusion, electro osmotic drag and hydraulic pressure. Voltage was modelled including main overpotentials (i.e., activation, ohmic, concentration). First and second law efficiencies were defined. Key empirical parameters depending on temperature were identified in the activation and ohmic overpotentials. The electrodes reference exchange current densities and change transfer coefficients were related to activation overpotentials whereas hydrogen ion diffusion to Ohmic overvoltages. These model parameters were empirically fitted so that polarization curve obtained by the model predicted well the voltage at different current found by the experimental results. Finally, from the efficiency calculation, it was shown that at low current densities the electrolyzer cell absorbs heat from the surroundings. The model is not able to describe the transients involved during the cell electrochemical reactions, however these processes are assumed relatively fast. For this reason, the model can be implemented in system dynamic modelling for hydrogen production and storage where components dynamic is generally slower compared to the cell electrochemical reactions dynamics.

In this work, the phase-field approach to fracture is extended to model fatigue failure in high- and low-cycle regime. The fracture energy degradation due to the repeated externally applied loads is introduced as a function of a local energy accumulation variable, which takes the structural loading history into account. To this end, a novel definition of the energy accumulation variable is proposed, allowing the fracture analysis at monotonic loading without the interference of the fatigue extension, thus making the framework generalised. Moreover, this definition includes the mean load influence of implicitly. The elastoplastic material model with the combined nonlinear isotropic and nonlinear kinematic hardening is introduced to account for cyclic plasticity. The ability of the proposed phenomenological approach to naturally recover main features of fatigue, including Paris law and Wöhler curve under different load ratios is presented through numerical examples and compared with experimental data from the author’s previous work. Physical interpretation of additional fatigue material parameter is explored through the parametric study.

Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

Greenhouses require intensive energy input to maintain stable growing conditions, particularly in extreme climates. In prior research, we proposed a greenhouse covering coated with antimony tin oxide (ATO) nanoparticles and demonstrated through simulation that such coatings could significantly reduce energy demand without reducing photosynthetically active radiation (PAR). The present study experimentally validates those predictions using a controlled lab-scale greenhouse prototype. Polyethylene sheets were coated with ATO nanoparticles to form a spectrally selective photothermal nanocoating and integrated into the greenhouse prototype. The prototype was tested inside a thermal chamber equipped with a full-spectrum solar simulator, enabling replication of seasonal extremes. Two representative dates, December 19th (cold conditions) and July 21st (hot conditions), were selected to represent worst-case seasonal performance. The thermal and energy responses of the greenhouse with ATO-coated sheets were compared with those of an identical greenhouse without coatings with a standard uncoated double-layer polyethylene covering. The experimental findings confirm strong alignment with earlier simulation studies, demonstrating both the accuracy of the simulation models and the practical feasibility of the coating. Energy testing revealed that ATO nanocoating improved total annual energy savings over 10% compared to uncoated double-layer polyethylene films. Notably, these efficiency gains were achieved without reducing PAR transmittance, ensuring no negative impacts on crop growth. This study provides the first experimental validation of nanocoated greenhouse films for energy management, confirming their dual role in reducing operational energy use while sustaining plant productivity. This study advances sustainable agricultural practices by demonstrating the potential of photothermal nanocoatings as scalable, high-impact solutions for greenhouse energy efficiency.

In this paper, we review and connect the three essential conditions needed by the collapse model to achieve a complete and exact formulation, namely the theoretical, the experimental, and the ontological ones. These features correspond to the three parts of the paper. In any empirical science, the first two features are obviously connected but, as is well known, among the different formulations and interpretations of non-relativistic quantum mechanics, only collapse models, as the paper well illustrates with a richness of details, have experimental consequences. Finally, we show that a clarification of the ontological intimations of collapse models is needed for at least three reasons: (1) to respond to the indispensable task of answering the question ’what are collapse models (and in general any physical theory) about?’; (2) to achieve a deeper understanding of their different formulations; (3) to enlarge the panorama of possible readings of a theory, which historically has often played a fundamental heuristic role.