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This study addresses the mixed fleet Vehicle Routing Problem (MFVRP) for transporting hazardous material (hazmat) goods in port hinterlands, aiming to balance transportation efficiency and sustainability. It refines transportation routes through intermediate bulk containers (IBCs) sharing, incorporating both fuel vehicles (FVs) and electric vehicles (EVs). The main contribution of this study is as follows. First, a mixed fleet strategy is introduced, integrating fuel hazmat vehicles (FHVs) and electric hazmat vehicles (EHVs) to boost efficiency and cut emissions. Second, this study develops a bi-objective Mixed-Integer Nonlinear Programming (MINLP) model with fuzzy time windows for flexible scheduling. Third, an adaptive intuitionistic fuzzy large neighborhood search (AIFLNS) algorithm is proposed, using intuitionistic fuzzy sets (IFSs) to refine scoring mechanisms and operation selection. In this way, this study not only enhances the efficiency of hazmat transportation, but also promotes green logistics in port hinterlands. Finally, experimental results show that the AIFLNS reduces transportation costs and enhances service satisfaction, outperforming adaptive large neighborhood search (ALNS), and ant colony optimization (ACO) algorithms.

As a kind of newly proposed fuzzy set, dynamic neutrosophic cubic set (DNCS) has varying information on elements of truth, indeterminacy, and falsity in different time intervals, and has been widely used. Considering that normal distribution is the most widely used distribution in real world, this study proposes a kind of parameterized normal similarity measures (NSM) on DNCSs to depict the similarity characteristic between different elements in DNCS. Moreover, this study also proposes a kind of soil recognition model. The main innovation of this study is as follows. Firstly, this study presents NSMs on fuzzy sets, especially on DNCSs for the first time. Secondly, this study proposes a kind of hesitant neutrosophic cubic set with probability vector. Thirdly, a parameter in the proposed parameterized NSMs has ability to adjust weight indirectly and adaptively. Fourthly, this study introduces a soil recognition model using NSMs on DNCSs. After that, a soil recognition example illustrates the reliability of the proposed parameterized NSMs and the soil recognition model.

The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flows. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.

The paper aimed to explore the clinical value of chorionic gonadotropin and progesterone combined with transvaginal ultrasound in the diagnosis of threatened abortion.

Well-defined graphene quantum dots (GQDs) are crucial for their biological applications and the construction of nanoscaled optoelectronic and electronic devices. However, as-synthesized GQDs reported in many works assume a very wide lateral size distribution; thus, their apparent properties cannot truthfully reflect intrinsic properties of the well-defined GQDs, and more importantly, the applications of GQDs will be affected and limited as well. In this work, we demonstrated that different sized GQDs with a narrow size distribution could be obtained via gel electrophoresis of the crude GQDs prepared through a photo-Fenton reaction of graphene oxide (GO). It is illustrated that the photoluminesce (PL) emissions of the well-defined GQDs originated mainly from the peripheral carboxylic groups and conjugated carbon backbone planes through fluorescence and UV–vis spectroscopies. More importantly, our findings challenge the notion that the excitation wavelength dependent PL property of the as-synthesized GQDs is the intrinsic property of the size-defined GQDs. Preliminary data at the cellular level indicated that the small sized GQDs exhibit weaker quenching DNA dye ability but higher toxicity to the cells compared to that of the as-synthesized GQDs. This discovery is essential to explore applications of the GQDs in pharmaceutics and to understand the origin of the optoelectronic properties of GQDs.

In the classical simulated annealing algorithm (SAA), the iteration feasible solution is mainly based on a certain random probability. In the process of iteration, there is a lack of comparison between individuals and the whole population of feasible solutions, and the indicators to measure the change of state of individuals are too absolute to achieve the overall control of the algorithm. To deal with uncertain information that individuals encounter in iteration, this study introduces the idea of neutrosophic decision-making and establishes a kind of neutrosophic fuzzy set (NFS) to describe the time-varying iterative state of individuals according to the change of individual state, the change of population state, and the number of iteration. The biggest feature of this study is to propose a neutrosophic simulated annealing algorithm that combines the idea of NFS with simulated annealing. The biggest contribution of this study is to propose a novel entropy measurement method using the NFS and combine it with the simulated annealing algorithm to handle the optimization process. Finally, the effectiveness of the novel algorithm is verified by an example of warehouse optimization.

Multidrug resistance (MDR) is the major factor in the failure of many forms of chemotherapy, mostly due to the increased efflux of anticancer drugs that mediated by ATP‐binding cassette (ABC) transporters. Therefore, inhibiting ABC transporters is one of effective methods of overcoming MDR. However, high enrichment of ABC transporters in cells and their broad substrate spectra made to circumvent MDR are almost insurmountable by a single specific ABC transporter inhibitor. Here, this study demonstrates that graphene quantum dots (GQDs) could downregulate the expressions of P‐glycoprotein, multidrug resistance protein MRP1, and breast cancer resistance protein genes via interacting with C‐rich regions of their promoters. This is the first example that a single reagent could suppress multiple MDR genes, suggesting that it will be possible to target multiple ABC transporters simultaneously with a single reagent. The inhibitory ability of the GQDs to these drug‐resistant genes is validated further by reversing the doxorubicin resistance of MCF‐7/ADR cells. Notably, GQDs have superb chemical and physical properties, unique structure, low toxicity, and high biocompatibility; hence, their capability of inhibiting multiple drug‐resistant genes holds great potential in cancer therapy. Graphene quantum dots (GQDs)downregulate multiple multidrug‐resistant (MDR) genes via interacting with C‐rich regions of their promoters. The inhibitory ability of the GQDs to MDR genes is validated by reversing doxorubicin resistance of MCF‐7/ADR cells.