Database Of Properties Research Articles

Design of modelling and online simulation for energy systems in papermaking mill

This paper presents a general framework of modelling and online simulation for papermaking mill’s energy systems. The energy flow models of thermal power plant, DIP and paper machine for a papermaking mill was designed and implemented an online simulation. The traditional offline, closed and scattered modelling and simulation methods were changed into online, open and integrated forms. Through the analysis of energy systems of papermaking mills, the framework has implemented the followings. The unit (equipment) models were established, it can analyse the energy of energy flow material. The corresponding material flow models library was developed. The craftwork models were designed and the energy flow models library was developed of thermal power plant, DIP and paper machine. The mathematical models of unit (equipment), energy flow, exergy flow, thermal power plant, DIP and paper machine were built. The material properties database was developed. General simulation software architecture and online simulation software architecture were designed. The framework implemented online simulation, analysis, diagnosis and optimization for energy systems of whole papermaking mill. The practical application results are fine when the framework was applied in Guangzhou Paper Group Co., Ltd., and Gold East Paper (Jiangsu) Co., Ltd. The practical application results indicate that the emulation, simulation, analysis, diagnosis and optimization of material flow and energy flow for papermaking mill whole production process can be implemented by online simulation, and the purposes of maximizing the utility efficiency of material and energy resource, reducing resource, reusing and recycling can be achieved.

Applications of Thermochemical Modeling in Molten Salt Reactors.

The extensively evaluated and consistent thermodynamic database, the Molten Salt Thermal Properties Database-Thermochemical (MSTDB-TC), was used along with additional thermodynamic values from other sources as examples of ways to examine molten salt reactor (MSR) fuel behavior. Relative stability with respect to halide potential and temperature for likely fuel and fission product components were mapped in Ellingham diagrams for the chloride and fluoride systems. The Ellingham diagrams provide a rich, visual means for identifying halide-forming components in proposed fuel/solvent salt systems. Thermochemical models and values from MSTDB-TC and ancillary sources were used in global equilibrium calculations to provide compositions for a close analysis of the behavior of a possible Molten Chloride Salt Fast Reactor and a Molten Salt Reactor Experiment-type system at high burnup (100 GWd/t). The results illustrated the oxidative nature of burnup in MSRs and provided information about redox behavior and possible control.

An automotive steel wheel digital twin for failure identification under accelerated fatigue tests

In this paper, a digital twin to predict failure in automotive steel wheels during experimental fatigue tests is proposed. The methodology involves a wheel finite element model that incorporates assembling and mounting processes, and CDTire/3D simulation package, employed to calculate tyre to rim generalised internal forces in several loading conditions. The two models are decoupled under small deformation assumption, which allows to reduce computational effort for batch simulations. The Digital Twin (DT) is designed to meet the standard requirements and the working principles of test benches for dynamic cornering, radial, and biaxial fatigue tests. The overall DT architecture is described with emphasis on different test bench logic and the introduction of simplifications to efficiently estimate failure under mean stress variation. Then, a simplified formulation of the critical plane McDiarmid multi-axial failure criterion is presented to face the non-proportionality of stress path under loading conditions. Finally, the methodology is validated against an industrial case study: the predicted failures are compared to experimental test result database for wheel topologies, material properties, test benches and loading conditions.

Development of thermo-physical property database and code of fusion materials for CFETR blankets

The conceptual design of China Fusion Engineering Test Reactor (CFETR) has been completed and is currently in the engineering design phase. In this paper, focusing on three categories of candidate blanket designs and divertor designs, the selection schemes for materials of plasma-facing components, solid structure, tritium breeding materials, neutron multiplier, coolant, and divertor were extracted. Thermo-physical property data was compiled to form a material thermo-physical property database, named Thermo-physical Property Database (ThePD), which is the first advanced thermo-physical database for materials of CFETR blankets. Besides, the callable code was generated, which can be directly applied in the system code for the blanket. Based on internationally available database from National Institute of Standards and Technology (NIST) and similar software AP1700, the correctness of the data and the transitional analysis of regions designed by International Association for the Properties of Water and Steam (IAPWS) were verified. Using the thermal-hydraulic system analysis code, integrated validation was performed for the validated thermo-physical property data and code, demonstrating the applicability and accuracy of the data. ThePD is conducive to addressing the issue of incomplete thermo-physical property data faced by blanket design and safety analysis software for CFETR, and it can fill this gap.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

Customizing PBE exchange-correlation functionals: a comprehensive approach for band gap prediction in diverse semiconductors.

Accurate band gap prediction in semiconductors is crucial for materials science and semiconductor technology advancements. This paper extends the Perdew-Burke-Ernzerhof (PBE) functional for a wide range of semiconductors, tackling the exchange and correlation enhancement factor complexities within density functional theory (DFT). Our customized functionals offer a clearer and more realistic alternative to DFT+U methods, which demand large negative U values for elements like sulfur (S), selenium (Se), and phosphorus (P). Moreover, these functionals are more cost-effective than GW or Heyd-Scuseria-Ernzerhof (HSE) hybrid functional methods, therefore, significantly facilitating the way for unified workflows in analyzing electronic structure, dielectric constants, effective masses, and further transport and elastic properties, allowing for seamless calculations across various properties. We point out that such development could be helpful in the creation of comprehensive databases of band gap and dielectric properties of the materials without expensive calculations. Furthermore, for the semiconductors studied, we show that these customized functionals and the strongly constrained and appropriately normed semilocal density functional (SCAN) perform similarly in terms of the band gap.

DESignSolvents: an open platform for the search and prediction of the physicochemical properties of deep eutectic solvents

A web-resource, DESignSolvents, to provide users worldwide with the database of DES properties and the corresponding prediction models.

A catalogue of observed geo-effective CME/ICME characteristics

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), to improve our ability to forecast their propagation characteristics and mitigate the risks to our technologically driven society. The essential part of achieving this goal is to assess the performance of forecasting models. To this end, the quality and availability of suitable data are of paramount importance. In this work, we merged publicly available data of CMEs from both in-situ and remote observations in order to build a dataset of CME properties. To evaluate the accuracy of the dataset and confirm the relationship between in-situ and remote observations, we have employed the Drag-Based Model (DBM) due to its simplicity and modest consumption of computational resources. In this study, we have also explored the parameter space for the drag parameter and solar wind speed using a Monte Carlo approach to evaluate how efficiently the DBM determines the propagation of CMEs for the events in the dataset. The geoeffective CMEs selected as a result of this work are compliant with the hypothesis of DBM (isolated CME, constant solar wind speed beyond 20 R⊙) and also yield further insight into CME features such as arrival time and arrival speed at L1 point, lift-off time, speed at 20 R⊙ and other similar quantities. Our analysis based on the acceptance rate in the DBM inversion procedure shows that almost 50% of the CME events in the dataset are well described by DBM as they propagate in the heliosphere. The dataset includes statistical metrics for the DBM model parameters. The probability distribution functions (PDFs) for the free parameters of DBM have been derived through a Monte Carlo-like inversion procedure. Probability distribution functions obtained from this work are comparable to PDFs employed in previous works. The analysis showed that there exist two different most probable values (median values) of solar wind speed for DBM input based on slow (wslow ≈ 386 km/s) and fast (wfast ≈ 547 km/s) solar wind type. The most probable value for the drag parameter (γ ≈ 0.687 × 10−7 km−1) in our study is somewhat higher than the values reported in previous studies. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. Additionally, possible CME events are identified where the DBM prediction is not valid due to model limitations and higher uncertainties in the input parameters. These events require further thorough investigation in the future.

Legal Protection for Registered Trademark Holders Against Trademark Infringement

In the era of development aligned with international conventions, the role of brands has become crucial in maintaining business competition and must comply with existing trademark regulations. Legislation regarding trademarks in Indonesia has undergone numerous changes to adapt to the needs and developments of the times, starting with Law No. 14 of 1997 concerning Trademarks, amended by Law No. 15 of 2001 concerning Trademarks, and most recently, Law No. 20 of 2016 concerning Trademarks and Geographical Indications. For consumers, a brand not only facilitates identification but also serves as a symbol of self-esteem. Individuals accustomed to choosing products from a particular brand tend to continue using that brand for various reasons, such as familiarity, trustworthiness, product quality, and more. Thus, the function of a brand as a guarantee of quality becomes increasingly evident, especially concerning reputable products. This research adopts a normative juridical research approach. The findings of this study indicate that legal protection for trademarks is granted, whether foreign or local, famous or not, only to registered trademarks. Therefore, every brand owner is encouraged to register their trademark with the Directorate General of Intellectual Property Rights to obtain legal protection for their brand. Legal protection based on the first-to-file principle is provided to registered trademark holders with ‘good faith’ both in a preventive and repressive manner. Considerations by the judge in deciding trademark infringement cases, as in Decision Number: 1349 K/Pdt.Sus-HKI/2022, are as follows: The plaintiff alleges ownership of the ERG trademark (ERG logo) with Registration Number IDM000540156, dated March 8, 2014, in class 25 to protect footwear and apparel products. The naming of ERG is based on the plaintiff’s idea to design and develop ergonomic products. Therefore, the translation information in the intellectual property database for the ERG trademark is ergonomic fashion products. The plaintiff claims that the defendants have infringed the trademark by using the ERG mark, which is substantially or entirely identical to the plaintiff’s ERG mark, Registration Number IDM000540156 owned by Mr. Muhamad Adi Mulya Pranata without rights and unlawfully

An innovative algorithm based on weighted fuzzy soft multisets and its application in selecting optimal construction materials

<p>Effective decision-making is critical across various domains, including technology, medicine, and engineering. To address the complexities of decision-making, particularly in scenarios involving both positive and negative parameters, this paper introduces an innovative algorithm based on weighted fuzzy soft multisets. This algorithm mitigates the issue of counterintuitive results often encountered in existing methods. By incorporating the concept of uniform fuzzy soft multisets and considering the conditional structure of these sets, our approach advances the theoretical framework of decision-making while providing a practical tool for complex scenarios. To demonstrate its practical applicability, we conduct a case study focused on selecting optimal construction materials for a building project, utilizing data from established engineering standards and a comprehensive wood properties database. The key findings of our sensitivity analysis highlight the algorithm's robustness to weight changes and adaptability to different decision sequences. These findings highlight the algorithm's potential to enhance decision support systems across various fields, such as engineering, healthcare, and environmental management. This potential is particularly valuable in complex, multi-criteria scenarios that demand nuanced, context-aware solutions.</p>

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions.

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

Химико-термодинамические свойства чистых и многокомпонентных растворов сахарозы

To select an optimal mode of evaporation and crystallization, sugar producers need comprehensive databases of chemical and thermodynamic properties of sucrose solutions. This article introduces refined experimental estimates of the chemical and thermodynamic properties of pure and technical multicomponent sucrose solutions. &#x0D; The study involved a modernized ebulliometer with two circulation tubes that measured the true boiling points of concentrated and supersaturated homogeneous solutions, as well as heterogeneous crystallizing systems. The boiling points of pure and multicomponent sucrose solutions were observed for the following variables: 5–93% dry solids, 60–100% purity, 20–100 kPa.&#x0D; In this study, the sucrose solutions did not obey Raoult’s laws for ideal mixtures, while the Ramsay-Young’s equation and Dühring’s rule were approximate. The thermodynamic properties of these solutions fit in the Lewis theory of activity. The study yielded a new thermodynamic equation for the boiling point in pure and technical multicomponent sucrose solutions. The authors revealed the correlation between the constants of Ramsay-Young and Dühring and the concentration and supersaturation of sucrose solutions, as well as the change in the entropy of these solutions. The error of estimate was 2–3%. The supersaturation coefficient was measured by the ratio of the boiling points of the solution and water. &#x0D; The authors used differential and relative ebulliometric criteria to develop some practical methods for monitoring and controlling the process of isobaric evaporative crystallization. The new method can improve the commercial mass sucrose crystallization from boiling solutions.

Spatial variability characterization and modelling of 2.5D woven SiO2f/SiO2 composites

2.5D woven SiO2f/SiO2 composites possess geometric variabilities across scales, such as the spatial fluctuations of fibers and yarns or the random distributions of defects and pores formed in the manufacturing process, which induces a complex stress and strain distribution in material testing. In this study, a multivariate cross-correlated non-Gaussian random field based on Vine Copula is proposed to characterize the spatial variability of mechanical properties. A database of material mechanical properties is acquired by stochastic representative volume element (SRVE) simulation results which contains realistic meso-geometry characteristics and spatially random voids. Based on this database, the cross- and auto-correlations of the mechanical properties are quantified by Vine Copula and non-Gaussian random field model. In a comparison of different numerical models and experimental results, the proposed method is verified, which shows great advantages in characterizing the spatial variability and big potential abilities in damage or reliability analysis.

Fixed wh-expressions in classroom second language acquisition: databases of computational properties or utterance schemas?

Fixed wh-expressions in classroom second language acquisition: databases of computational properties or utterance schemas?

Estimation of Oak Leaf Functional Traits for California Woodland Savannas and Mixed Forests: Comparison between Statistical, Physical, and Hybrid Methods Using Spectroscopy

Key leaf functional traits, such as chlorophyll and carotenoids content (Cab and Cxc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm−2; 1.3 µg·cm−2; 0.0009 cm; and 0.0009 g·cm−2 for Cab, Cxc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for Cab, Cxc, and EWT (RMSE = 7.8 µg·cm−2; 2.0 µg·cm−2; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm−2) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm−2 respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons.

Machine learning and atomistic origin of high dielectric permittivity in oxides

Discovering new stable materials with large dielectric permittivity is important for future energy storage and electronics applications. Theoretical and computational approaches help design new materials by elucidating microscopic mechanisms and establishing structure–property relations. Ab initio methods can be used to reliably predict the dielectric response, but for fast materials screening, machine learning (ML) approaches, which can directly infer properties from the structural information, are needed. Here, random forest and graph convolutional neural network models are trained and tested to predict the dielectric constant from the structural information. We create a database of the dielectric properties of oxides and design, train, and test the two ML models. Both approaches show similar performance and can successfully predict response based on the structure. The analysis of the feature importance allows identification of local geometric features leading to the high dielectric permittivity of the crystal. Dimensionality reduction and clustering further confirms the relevance of descriptors and compositional features for obtaining high dielectric permittivity.

Microstructure evolution of FD-POEM powders during high-temperature plasma spheroidization

The freeze–dry pulsated orifice ejection method (FD-POEM) can be coupled with plasma spheroidization (PS) as an advanced approach for fabricating spherical refractory powders used in laser additive manufacturing. However, the microstructure evolution of FD-POEM powders, which include components of various melting points, during ultrahigh-temperature PS treatment remains unclear. In this work, the morphology, chemical composition, and microstructure of MoSiBTiC alloy powders, as representative PS powders, were systematically investigated. The mesh-structured FD-POEM powders underwent complete densification, resulting in a 52 % reduction in particle size. The PS powders were composed of coarse solid solution Mo (Moss) and fine eutectic Mo5SiB2/TiC phases, consistent with the composition of additively manufactured MoSiBTiC alloys. With an increase in Si content, the volume fraction of fine Mo5SiB2 phases increased, resulting in increased Vickers hardness. Notably, the oxygen content of FD-POEM powders decreased by > 99 %, owing to the effective elimination of surface oxides on the initial elemental powders during ultrahigh-temperature PS treatment. This research provides an innovative approach for reducing the oxygen content in refractory powders to be used for additive manufacturing. Enhanced understanding of the microstructure evolution of PS powders can facilitate the composition optimization of refractory powders and development of a microstructure–property database for additively manufactured materials.

The Legal Certainty of Legitimate Ownership in Copyright Works of Songs or Music, as well as Associated Rights in Non-Declarative Recording in accordance with the Royalty Management System under Government Regulation No. 56 of 2021 (Case Study on Copyrigh

The non-registration of data on song or music creators and related rights in the Directorate General of Intellectual Property (DJKI) database and the Song and Music Information System (SILM) could lead to legal uncertainty regarding the economic rights of song or music creators and related rights in claiming royalty rights. This research aims to explore the legal consequences of a song or music copyright/related rights that are not registered in the SILM and the dispute resolution patterns as efforts to provide legal protection for unregistered song or music copyrights/related rights in the SILM. The applied method is empirical sociology to assess the effectiveness of legal instruments in accordance with applicable regulations by observing the research objects and the level of their implementation in practice. The results indicate that for works of music or songs not recorded in the SILM, royalty payments are made voluntarily by users with the condition of not creating new records, and dispute resolution patterns involve mediation through the PPNS before proceeding to litigation to seek a win-win solution.

Open material database for tensile test properties of additive manufacturing materials

In recent years, the investigation of material properties within additive manufacturing, also known as 3D printing, has gained significant research attention. The intricate interplay between numerous fabrication parameters and the resultant material properties of 3D-printed components has become crucial, particularly for enabling effective topology optimization. Considering this, we propose the establishment of an accessible open database. This repository stores a comprehensive collection of fabrication files corresponding to each distinct material and printer combination, accompanied by the outcomes of meticulous tensile testing. To support the research community, our initiative extends to the inclusion of material provider datasheets, facilitating comprehensive result comparisons. A standardized approach utilizing consistently applied strain rates is recommended, focusing on a compact dog bone specimen design. This pioneering attempt encompasses an expansive array of data derived from 25 distinct materials and 9 diverse printers, meticulously capturing the inherent variability within the samples. The database catalogues the complete spectrum of tensile test data, encompassing various essential measurements such as mass, and crucial material properties including Young’s modulus, yield stress, fracture strain, and absorbed energy. These recorded metrics can be seamlessly correlated against density, manufacturing time, or cost parameters, enabling the generation of insightful plots and analysis. Through this collaborative effort, we aim to provide researchers with a robust foundation for informed decision-making and advancements in additive manufacturing.

Composition and micromechanical properties of the femoral neck compact bone in relation to patient age, sex and hip fracture occurrence.

Current clinical methods of bone health assessment depend to a great extent on bone mineral density (BMD) measurements. However, these methods only act as a proxy for bone strength and are often only carried out after the fracture occurs. Besides BMD, composition and tissue-level mechanical properties are expected to affect the whole bone's strength and toughness. While the elastic properties of the bone extracellular matrix (ECM) have been extensively investigated over the past two decades, there is still limited knowledge of the yield properties and their relationship to composition and architecture. In the present study, morphological, compositional and micropillar compression bone data was collected from patients who underwent hip arthroplasty. Femoral neck samples from 42 patients were collected together with anonymous clinical information about age, sex and primary diagnosis (coxarthrosis or hip fracture). The femoral neck cortex from the inferomedial region was analyzed in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-CT and quantitative polarized Raman spectroscopy for both morphological and compositional characterization. Mechanical properties, as well as the sample-level mineral density, were constant over age. Only compositional properties demonstrate weak dependence on patient age: decreasing mineral to matrix ratio (p = 0.02, R2 = 0.13, 2.6 % per decade) and increasing amide I sub-peak ratio I~1660/I~1683 (p = 0.04, R2 = 0.11, 1.5 % per decade). The patient's sex and diagnosis did not seem to influence investigated bone properties. A clear zonal dependence between interstitial and osteonal cortical zones was observed for compositional and elastic bone properties (p < 0.0001). Site-matched microscale analysis confirmed that all investigated mechanical properties except yield strain demonstrate a positive correlation with the mineral fraction of bone. The output database is the first to integrate the experimentally assessed microscale yield properties, local tissue composition and morphology with the available patient clinical information. The final dataset was used for bone fracture risk prediction in-silico through the principal component analysis and the Naïve Bayes classification algorithm. The analysis showed that the mineral to matrix ratio, indentation hardness and micropillar yield stress are the most relevant parameters for bone fracture risk prediction at 70 % model accuracy (0.71 AUC). Due to the low sample number, further studies to build a universal fracture prediction algorithm are anticipated with the higher number of patients (N > 200). The proposed classification algorithm together with the output database of bone tissue properties can be used for the future comparison of existing methods to evaluate bone quality as well as to form a better understanding of the mechanisms through which bone tissue is affected by aging or disease.