Hasil untuk "Property"

P. Camargo, K. Satyanarayana, F. Wypych

Nanocomposites, a high performance material exhibit unusual property combinations and unique design possibilities. With an estimated annual growth rate of about 25% and fastest demand to be in engineering plastics and elastomers, their potential is so striking that they are useful in several areas ranging from packaging to biomedical applications. In this unified overview the three types of matrix nanocomposites are presented underlining the need for these materials, their processing methods and some recent results on structure, properties and potential applications, perspectives including need for such materials in future space mission and other interesting applications together with market and safety aspects. Possible uses of natural materials such as clay based minerals, chrysotile and lignocellulosic fibers are highlighted. Being environmentally friendly, applications of nanocomposites offer new technology and business opportunities for several sectors of the aerospace, automotive, electronics and biotechnology industries.

N. Hüsing, U. Schubert

R. Kee, G. Dixon-Lewis, J. Warnatz et al.

Gang Wu, Yanjing Su, Lijie Qiao et al.

Hydrogen embrittlement critically limits the reliability of high-strength steels, where α-Fe2O3 within passive films serves as the primary barrier against hydrogen ingress. Elemental doping is an effective approach to tune the hydrogen resistance of α-Fe2O3, yet the dopants selection criterion is absent. Here, the spin-polarized density functional theory (DFT) is employed to estimate the doping formation characteristics of 24 types of elements and elucidate how the doping elements influence the vacancies characteristics and hydrogen dissolution behaviors in α-Fe2O3. The 24 types of substitutional doping elements are classified according to their formation energies of dopants, oxygen vacancies, and iron vacancies in α-Fe2O3. The orange-group elements (Al, Cr, Y, Mn, and Ga) are selected as promising dopants to effectively resist the hydrogen and maintain the integrity of oxide film. The effects of strain on the hydrogen dissolution behaviors in doping α-Fe2O3 are also analyzed and the Y doped α-Fe2O3 shows the weakest strain sensitivity. At last, the linear regression models based on seven atomic descriptors are proposed, which could accurately predict the Edoping, EOv, EFev, and Ediss (R2 = 0.70–0.88), respectively. These descriptor-property relationships provide the guidance to design doped α-Fe2O3 passive films with desired hydrogen resistance.

Saharon Shelah, Alexander Usvyatsov

We consider the following property of a first order theory T with a distinguished unary predicate P: every model of the theory of P occurs as the P-part of some model of T. We call this property the Gaifman property. Gaifman conjectured that if T is relatively categorical over P, then it has the Gaifman property. We propose a generalized version of this conjecture: if T fails the Gaifman property, then it exhibits non-structure over P, i.e., has many non-isomorphic models over P in many cardinalities. We address this conjecture for countable theories. Motivated by ideas from Classification Theory, we separate this conjecture into two parts: 1) stability over P (a structure property of theories) implies the Gaifman property, and 2) instability over P implies non-structure. In this paper prove the first part of this conjecture. In fact, we prove a stronger statement: an appropriate version of stability implies higher stable amalgamation properties.

Viktor Horváth

Zhongping Wang, Xiaoying Tian

This study analyses the impact of agricultural greenwashing on financial performance via green innovation. To this end, it employs data from Chinese A-share agribusinesses from 2012 to 2022. The study indicates the following results: (1) the practice of greenwashing (ESG disclosure–performance gap, GW) has a significant negative impact on ROA, particularly in non-state firms; (2) green innovation (patents, GI) partially mediates this relationship, with a percentage of 9.09%, as GW diverts research and development resources toward image management. Robustness checks are employed to confirm the results obtained using ROE and lagged models. Property rights moderate the effects: non-state firms are more adversely affected by innovation dependency, while state firms are protected by policies. The “double-edged” mechanism elucidates GW’s short-term legitimacy gains in contrast to long-term innovation suppression and financial decline. The report calls for the establishment of standardised ESG metrics (for example, the disclosure of pesticide residue) and targeted green incentives (for example, SME R&D subsidies) to be aligned with UN SDGs 9.4 (green tech) and 12.6 (responsible production). The present study offers insights into the governance of environmental, social, and governance (ESG) matters within the context of agriculture in China.

Yingjie Yang, Hao Zhou, Wanliang Liu et al.

Extraction temperature is one of the basic factors for alginate-like exopolymers (ALE) recovery from waste activated sludge (WAS). Given the rising interest in sustainable resource recovery and the promising industrial applications of ALE, this study systematically evaluated the effects of extraction temperatures (50–95 °C) on the ALE yield, profit, compositions, structural properties and sludge reduction. The increasing extraction temperature significantly enhanced ALE yield (from 148.3 mg/g VSS at 80 °C to 218.6 mg/g VSS at 95 °C) and net profit (from 0.441 to 1.046 CNY/kg SS). The elevated temperatures notably increased protein yields compared to polysaccharides. Fluorescence spectroscopy also indicated a pronounced increase in aromatic protein-like substances (C1), whereas polysaccharides showed a comparatively modest increase. Meanwhile, UV–Vis analysis demonstrated decreased E2/E3 and E2/E4 ratios at higher temperatures, suggesting increased humification and reduced molecular weight. Structural analysis showed ALE gels extracted at higher temperatures became denser with decreased mechanical strength (compressive modulus declined from 1.45 MPa at 50 °C to 0.11 MPa at 95 °C). Furthermore, sludge reduction reached 19.8% at 95 °C, significantly alleviating disposal cost of the sludge. These findings in this study provided critical insights for optimizing ALE extraction processes, promoting sludge resource recovery for practical applications.

Junwei Wang, Fuqiang Zhao, Zihan Guo et al.

Composite rubber bearing is an important supporting component in bridge structure system, its aging and shear performance will affect the safety of the whole structure. However, due to the complexity of LRB specifications and sizes, the shear properties of aging LRB under different parameters were studied. In this study, the thermal aging and shear tests of 12 LRBs of the same specifications were first carried out, and the test results were taken as a reference, and the finite element model was established to select the constitutive model and determine the parameters, and finally the constitutive model and parameters consistent with the test were determined. Then, LRBs with different shape coefficient, diameter and number of layers were established, and shear simulation was carried out respectively to compare with the shear performance of the test supports, and the changes of parameters such as maximum shear force, energy dissipation, equivalent shear stiffness, initial sliding displacement and sliding distance generated by LRBs of different specifications at different shear stages were studied. The results show that for LRB of the same specifications, aging does not affect the maximum shear force, but the hardness and energy dissipation of rubber material increase with the aging time, and the initial sliding distance decreases with the aging time. For LRB with different parameters, under the same aging time, the maximum shear force and energy dissipation increase with the increase of shear deformation, and the equivalent shear stiffness decreases with the increase of shear degree. The maximum shear force, energy dissipation and initial shear stiffness of LRB increase with the increase of shape coefficient and diameter. The number of layers of the LRB does not affect the maximum shear force, but the energy dissipation increases with the increase of the number of layers, and the equivalent shear stiffness decreases with the increase of the number of layers. The larger the shape factor, diameter and layer number of LRB, the more likely it is to slip. Therefore, the influence of bearing parameters on the shear performance of LRB should be considered comprehensively when designing LRB in actual engineering.

Xiaotong Liu, Yiyu Liang

Signal reconstruction is a crucial aspect of compressive sensing. In weighted cases, there are two common types of weights. In order to establish a unified framework for handling various types of weights, the sparse function is introduced. By employing this sparse function, a generalized form of the weighted null space property is developed, which is sufficient and necessary to exact recovery through weighted $\ell_1$ minimization. This paper will provide a new recovery guarantee called $ω$-RIP-NSP with the weighted $\ell_1$ minimization, combining the weighted null space property and the weighted restricted isometry property. The new recovery guarantee only depends on the kernel of matrices and provides robust and stable error bounds. The third aim is to explain the relationships between $ω$-RIP, $ω$-RIP-NSP and $ω$-NSP. $ω$-RIP is obviously stronger than $ω$-RIP-NSP by definition. We show that $ω$-RIP-NSP is stronger than the weighted null space property by constructing a matrix that satisfies the weighted null space property but not $ω$-RIP-NSP.

Javier Alejandro Chávez-Domínguez, Verónica Dimant, Daniel Galicer

We study a notion analogous to the $p$-Approximation Property ($p$-AP) for Banach spaces, within the noncommutative context of operator spaces. Referred to as the $p$-Operator Approximation Property ($p$-OAP), this concept is linked to the ideal of operator $p$-compact mappings. We present several equivalent characterizations based on the density of finite-rank mappings within specific spaces for different topologies, and also one in terms of a slice mapping property. Additionally, we investigate how this property transfers from the dual or bidual to the original space. As an application, the $p$-OAP for the reduced $C^*$-algebra of a discrete group implies that operator $p$-compact Herz-Schur multipliers can be approximated in $\mbox{cb}$-norm by finitely supported multipliers.

Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam

Abstract A pathway to overcome limited data availability in materials science is to use the framework of transfer learning, where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (smaller) dataset. We systematically explore the effectiveness of various PT/FT strategies to learn and predict material properties and create generalizable models by PT on multiple properties (MPT) simultaneously. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, with sizes ranging from 941 to 132,752. Besides identifying optimal PT/FT strategies and hyperparameters, we find our pair-wise PT-FT models to consistently outperform models trained from scratch on target datasets. Importantly, our MPT models outperform pair-wise models on several datasets and, more significantly, on a 2D material band gap dataset that is completely out-of-domain. Finally, we expect our PT/FT and MPT frameworks to accelerate materials design and discovery for various applications.

Maryna Bochkarova

The article is devoted to stimulating the development of housing construction through mortgage lending. The purpose of the article is to determine the role of mortgage lending in the development of the housing sector, in particular through its impact on supply and demand in the real estate market and pricing in this sector. In the course of the research, data analysis, correlation analysis, and methods of forecasting economic trends were used. Graphical methods were also used to provide a clear understanding of how changes in the mortgage market affect the development of the real estate sector. The results of the study show that mortgage lending is a key element in stimulating the development of the housing sector, as it not only directly facilitates access to finance for potential property buyers, but also indirectly affects the pricing and investment attractiveness of the housing sector. The paper shows that fluctuations in mortgage rates have a significant impact on the dynamics of supply and demand in the residential real estate market, as well as on price trends. In particular, rising mortgage rates tend to reduce demand for housing, as households expect better investment opportunities. There is also a strong correlation between mortgage rate increases and slower price growth. The expansion of the mortgage lending market and, consequently, a reduction in mortgage rates boosts supply in the real estate market. Nevertheless, it is found that such changes can have complex and ambiguous consequences, including the risks of market overheating and the formation of price "bubbles" that threaten the stability of the sector in the long run. In addition, the study found that mortgage lending facilitates the implementation of new construction projects and the expansion of the housing stock, which is an important factor in stimulating economic growth. At the same time, the analysis showed that the impact of mortgage rates on the real estate market depends on a wide range of factors, including the economic situation, central bank policy, consumer confidence and other macroeconomic indicators. The practical significance of the publication is to provide recommendations for the development of a balanced policy in the field of mortgage lending aimed at supporting the stable development of the housing sector and preventing potential destabilising factors in the real estate market.

Wen Qian, Jing Huang, Shi-tai Guo et al.

In this work, a high-throughput computation (HTC) and machine learning (ML) combined method was applied to identify the determining factors of the detonation velocity (vd) and detonation pressure (pd) of energetic molecules and screen potential high-energy molecules with acceptable stability in a high-throughput way. The HTC was performed based on 1725 sample molecules abstracted from a dataset of over 106 linear nitroaliphatics with 1- to 6-membered C backbones and three types of substituents, namely single nitro group (-NO2), nitroamine (-NNO2), and nitrate ester (-ONO2). ML models were established based on the HTC results to screen high-energy molecules and to identify the determining factors of vd and pd. Compared with quantum chemistry calculation results, the absolute relative errors of vd and pd obtained using the ML models were less than 3.63% and 5%, respectively. Furthermore, eight molecules with high energy and acceptable stability were selected as potential candidates. This study shows the high efficiency of the combination of HTC and ML in high-throughput screening.

Jakub Wyczik

This article addresses the lack of comprehensive studies on Web3 technologies, primarily due to lawyers' reluctance to explore technical intricacies. Understanding the underlying technological foundations is crucial to enhance the credibility of legal opinions. This article aims to illuminate these foundations, debunk myths, and concentrate on determining the legal status of crypto-assets in the context of property rights within the distributed economy. In addition, this article notes that the intangible nature of crypto-assets that derive value from distributed registries, and their resistance to deletion, makes crypto-assets more akin to the autonomy of intellectual property than physical media. The article presents illustrative examples from common law (United States, United Kingdom, New Zealand) and civil law (Germany, Austria, Poland) systems. Proposing a universal solution, it advocates a comprehensive framework safeguarding digital property - data ownership - extending beyond the confines of Web3. This article presents a comprehensive, multi-layered approach to the analysis of tokens as digital content and virtual goods. The approach, universally applicable to various of such goods, scrutinizes property on three distinct layers: first, the rights to the virtual good itself; second, the rights to the assets linked to the virtual good; and third, the rights to the intellectual property intricately associated with the token. Additionally, the paper provides concise analysis of the conflict of laws rules applicable to virtual goods. It also delves into issues concerning formal requirements for the transfer of intellectual property rights, licensing, the first sale (exhaustion) doctrine, the concept of the lawful acquirer, and other crucial aspects of intellectual property in the realm of virtual goods, particularly within the emerging metaverse.

I. S. Sushilnikov

The relevance of the study is due to the importance of the question of the legal nature of such a young institution as an experimental legal regime. The author presents theoretical developments in the field of studying the constitutionality of experimental legal regimes, their relationship with the norms of the Constitution of Russia, dedicated to regulating the principles of a legal, democratic, federal state, support for competition, free use of one’s abilities and property for entrepreneurial and other economic activities not prohibited by law, the unity of economic space on the territory of the Russian Federation, the inadmissibility of restricting constitutional rights and citizens’ freedoms, legality and equality of all before the law. The article analyzes the relationship between the experimental legal regime and the partnership of entrepreneurs and authorities. An attempt is made to analyze the correlation between the experimental legal regime and the delegation of public powers to private individuals. The author, appealing to the judicial practice of the Constitutional Court of the Russian Federation, concludes that the experimental legal regime is based on the articles 1, 3, 8, 19, 29, 34, 55 and 75.1 of the Constitution of Russia.

Justin Chen, Abolfazl Tarizadeh

In this article, we investigate the avoidance property of ideals and rings. Among the main results, a general version of the avoidance lemma is formulated. It is shown that every idempotent ideal (and hence every pure ideal) has avoidance. The avoidance property of arbitrary direct products of avoidance rings is characterized. It is shown that every overring of an avoidance domain is an avoidance domain. Next, we show that every avoidance $\mathbb{N}$-graded ring whose base subring is a finite field is a PIR. It is also proved that the avoidance property is preserved under flat ring epimorphisms. Dually, we formulate a notion of strong avoidance, and show that it is reflected by pure morphisms.

Thirumurugan Velayutham, M. Ramesh Kumar, Paramasivam Sundararajan et al.

In this world, unnoticed sound that disturbs frequently is called as noise. It is one of the main reasons for reducing the environment quality of human being. This effect can be minimized by using acoustic absorbent material in different areas where the sound absorption is required. In textile, acoustic property of non-woven is better than the woven fabric. The non-woven fabrics were developed using the needle punching technique using kapok, jute, flax, viscose, polyester, polypropylene, and recycled polyethylene terephthalate (R-Pet) fibers. Sound absorbency parameter was tested by sound absorption tester specially designed for this purpose. The effects of fiber type, number of layers in the fabric, thickness of the sample, areal density, air permeability, and pore size on sound absorption capacity were investigated in this research work. Experimental results show that 100% kapok non-woven fabric with its high compact structure, higher thickness, lesser air permeability, and less average flow diameter exhibit good sound absorption property than other samples, and it is best-suitable for interior in automotive applications for noise control.

Yuji Sano, Tomoharu Kato, Yoshio Mizuta et al.

Laser peening (LP) is a well-established technique for introducing compressive residual stress (RS) near the surface of metal components, to improve their high-cycle fatigue properties. The authors have developed a compact LP device with a thumb-sized Nd:YAG microchip laser mounted on a collaborative robot arm. The device was applied to 9-mm-thick HT780 high-strength steel plate samples with irradiated pulse energies of 7.5−8.0 mJ, spot sizes of 0.42−0.58 mm and pulse densities of 100−1,600 pulses/mm2. X-ray diffraction showed that the maximum compressive RS was over 500 MPa near the surface, and the LP effect reached a depth of approximately 0.1 mm from the surface. Butt-welded HT780 samples were laser-peened with a pulse energy of 7.7 mJ, spot size of 0.49 mm and pulse density of 800 pulses/mm2. Then, the samples were subjected to a uniaxial fatigue test with a stress ratio of 0.1. The results showed that the fatigue strength at 107 cycles was improved by at least 50 MPa, comparable to the improvement attained by LP in a previous study with a pulse energy of 200 mJ from a conventional Nd:YAG laser.

Naifeng Wen, Guanqun Liu, Jie Zhang et al.

Abstract Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep learning-based MPP models capture molecular property-related features from various molecule representations. In this paper, we propose a molecule sequence embedding and prediction model facing with MPP task. We pre-trained a bi-directional encoder representations from Transformers (BERT) encoder to obtain the semantic representation of compound fingerprints, called Fingerprints-BERT (FP-BERT), in a self-supervised learning manner. Then, the encoded molecular representation by the FP-BERT is input to the convolutional neural network (CNN) to extract higher-level abstract features, and the predicted properties of the molecule are finally obtained through fully connected layer for distinct classification or regression MPP tasks. Comparison with the baselines shows that the proposed model achieves high prediction performance on all of the classification tasks and regression tasks.