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Arithmetic aggregation operators and geometric aggregation operators of intuitionistic fuzzy values (IFVs) are common aggregation operators in the fields of information fusion and decision making. However, their aggregated values imply some unreasonable results in some cases. To overcome the shortcomings, this paper proposes an intuitionistic fuzzy hybrid weighted arithmetic and geometric aggregation (IFHWAGA) operator and an intuitionistic fuzzy hybrid ordered weighted arithmetic and geometric aggregation (IFHOWAGA) operator and discusses their suitability by numerical examples. Then, we propose a multiple attribute decision-making method of mechanical design schemes based on the IFHWAGA or IFHOWAGA operator under an intuitionistic fuzzy environment. Finally, a decision-making problem regarding the mechanical design schemes of press machine is provided as a case to show the application of the proposed method.

On the basis of the combination of single-valued neutrosophic sets and hesitant fuzzy sets, this article proposes a single-valued neutrosophic hesitant fuzzy set (SVNHFS) as a further generalization of the concepts of fuzzy set, intuitionistic fuzzy set, single-valued neutrosophic set, hesitant fuzzy set, and dual hesitant fuzzy set. Then, we introduce the basic operational relations and cosine measure function of SVNHFSs. Also, we develop a single-valued neutrosophic hesitant fuzzy weighted averaging (SVNHFWA) operator and a single-valued neutrosophic hesitant fuzzy weighted geometric (SVNHFWG) operator and investigate their properties. Furthermore, a multiple-attribute decision-making method is established on the basis of the SVNHFWA and SVNHFWG operators and the cosine measure under a single-valued neutrosophic hesitant fuzzy environment. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.

Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the article proposes a single-valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an SVNMST clustering algorithm for clustering single-value neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach.

[...]many natural products are difficulty meeting the pharmacologic requirements due to low water solubility, poor stability, and poor oral bioavailability. [...]how to find natural products with therapeutic potential and use delivery technology to solve the problem of medicinal properties is very important. [...]due to the inherent physical and chemical properties of natural products, many natural products with good application prospects are difficult to be effectively encapsulated into nanocarriers due to poor lipid solubility and it is difficult to improve the biological characteristics of agents under the scale effect and surface properties of nanocarriers. [...]how to choose polymer materials to construct NDDS to improve the encapsulation efficiency and drug loading capacity is a key step to breaking through the technical barriers of nano-drug research and development. [...]NDDS has great advantages in improving the inadequate physicochemical properties of natural products and overcoming multiple physiological barriers in vivo. [...]combining the remarkable therapeutic potential of natural products with the in vivo delivery advantages of NDDS is the future development direction of natural products, which will greatly enhance the drug-forming properties of natural products and promote the more rapid application of more natural products in the treatment of major diseases.

Clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.

Cooling atoms to ultralow temperatures has produced a wealth of opportunities in fundamental physics, precision metrology, and quantum science. The more recent application of sophisticated cooling techniques to molecules, which has been more challenging to implement owing to the complexity of molecular structures, has now opened the door to the longstanding goal of precisely controlling molecular internal and external degrees of freedom and the resulting interaction processes. This line of research can leverage fundamental insights into how molecules interact and evolve to enable the control of reaction chemistry and the design and realization of a range of advanced quantum materials.

MXene‐based thermal camouflage materials have gained increasing attention due to their low emissivity, however, the poor anti‐oxidation restricts their potential applications under complex environments. Various modification methods and strategies, e.g., the addition of antioxidant molecules and fillers have been developed to overcome this, but the realization of long‐term, reliable thermal camouflage using MXene network (coating) with excellent comprehensive performance remains a great challenge. Here, a MXene‐based hybrid network comodified with hyaluronic acid (HA) and hyperbranched polysiloxane (HSi) molecules is designed and fabricated. Notably, the presence of appreciated HA molecules restricts the oxidation of MXene sheets without altering infrared stealth performance, superior to other water‐soluble polymers; while the HSi molecules can act as efficient cross‐linking agents to generate strong interactions between MXene sheets and HA molecules. The optimized MXene/HA/HSi composites exhibit excellent mechanical flexibility (folded into crane structure), good water/solvent resistance, and long‐term stable thermal camouflage capability (with low infrared emissivity of ≈0.29). The long‐term thermal camouflage reliability (≈8 months) under various outdoor weathers and the scalable coating capability of the MXene‐coated textile enable them to disguise the IR signal of various targets in complex environments, indicating the great promise of achieved material for thermal camouflage, IR stealth, and counter surveillance. A high‐performance thermal camouflage material is designed and successfully fabricated by decorating MXene network with hyaluronic acid (HA) and hyperbranched polysiloxane (HSi). Besides excellent mid‐infrared (IR) thermal camouflage, such material also integrates multiple advantages into itself, including being large‐scale, mechanically flexible, weather‐resistant, and thus showing great potential for stealth applications.

TDP-43 is a nuclear factor that functions in promoting pre-mRNA splicing. Deletion of the N-terminal domain (NTD) and nuclear localization signal (NLS) (i.e., TDP-35) results in mislocalization to cytoplasm and formation of inclusions. However, how the NTD functions in TDP-43 activity and proteinopathy remains largely unknown. Here, we studied the structure and function of the NTD in inclusion formation and pre-mRNA splicing of TDP-43 by using biochemical and biophysical approaches. We found that TDP-43 NTD forms a homodimer in solution in a concentration-dependent manner, and formation of intermolecular disulfide results in further tetramerization. Based on the NMR structure of TDP-43 NTD, the dimerization interface centered on Leu71 and Val72 around the β7-strand was defined by mutagenesis and size-exclusion chromatography. Cell experiments revealed that the N-terminal dimerization plays roles in protecting TDP-43 against formation of cytoplasmic inclusions and enhancing pre-mRNA splicing activity of TDP-43 in nucleus. This study may provide mechanistic insights into the physiological function of TDP-43 and its related proteinopathies.

Some molecular and functional properties of albumin (83.6% protein), globulin (95.5% protein), glutelin (81.3% protein) as well as protein isolate (80.7% protein) from cashew nut were investigated. These proteins were subjected to molecular (circular dichroism, gel electrophoresis, scanning electron microscopy) and functional (solubility, emulsification, foaming, water/oil holding capacity) tests. Cashew nut proteins represent an abundant nutrient with well-balanced amino acid composition and could meet the requirements recommended by FAO/WHO. SDS-PAGE pattern indicated cashew nut proteins were mainly composed of a polypeptide with molecular weight (MW) of 53 kDa, which presented two bands with MW of 32 and 21 kDa under reducing conditions. The far-UV CD spectra indicated that cashew proteins were rich in β-sheets. The surface hydrophobicity of the protein isolate was higher than that of the protein fractions. In pH 7.0, the solubility of protein fractions was above 70%, which was higher than protein isolate at any pH. Glutelin had the highest water/oil holding capacity and foaming properties. Protein isolate displayed better emulsifying properties than protein fractions. In summary, cashew nut kernel proteins have potential as valuable nutrition sources and could be used effectively in the food industry.

To describe both certain linguistic neutrosophic information and uncertain linguistic neutrosophic information simultaneously in the real world, this paper originally proposes the concept of a linguistic neutrosophic cubic number (LNCN), including an internal LNCN and external LNCN. In LNCN, its uncertain linguistic neutrosophic number consists of the truth, indeterminacy, and falsity uncertain linguistic variables, and its linguistic neutrosophic number consists of the truth, indeterminacy, and falsity linguistic variables to express their hybrid information. Then, we present the operational laws of LNCNs and the score, accuracy, and certain functions of LNCN for comparing/ranking LNCNs. Next, we propose a LNCN weighted arithmetic averaging (LNCNWAA) operator and a LNCN weighted geometric averaging (LNCNWGA) operator to aggregate linguistic neutrosophic cubic information and discuss their properties. Further, a multiple attribute decision-making method based on the LNCNWAA or LNCNWGA operator is developed under a linguistic neutrosophic cubic environment. Finally, an illustrative example is provided to indicate the application of the developed method.