In this study, we report for the first time a one-pot approach for the synthesis of new CdSeTeS quaternary-alloyed quantum dots (QDs) in aqueous phase by microwave irradiation. CdCl2 was used as a Cd precursor during synthesis, NaHTe and NaHSe were used as Te and Se precursors and mercaptopropionic acid (MPA) was used as a stabilizer and source of sulfur. A series of quaternary-alloyed QDs of different sizes were prepared. CdSeTeS QDs exhibited a wide emission range from 549 to 709 nm and high quantum yield (QY) up to 57.7 %. Most importantly, the quaternary-alloyed QDs possessed significantly long fluorescence lifetimes > 100 ns as well as excellent photostability. Results of high-resolution transmission electron microscopy (HRTEM), energy dispersive X-ray spectroscopy (EDX) and powder X-ray diffraction (XRD) spectroscopy showed that the nanocrystals possessed a quaternary alloy structure with good crystallinity. Fluorescence correlation spectroscopy (FCS) showed that QDs possessed good water solubility and monodispersity in aqueous solution. Furthermore, CdSeTeS QDs were modified with alpha-thio-omega-carboxy poly(ethylene glycol) (HS-PEG-COOH) and the modified QDs were linked to anti-epidermal growth factor receptor (EGFR) antibodies. QDs with the EGFR antibodies as labeling probes were successfully applied to targeted imaging for EGFR on the surface of SiHa cervical cancer cells. We believe that CdSeTeS QDs can become useful probes for in vivo targeted imaging and clinical diagnosis.

SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD. 1

In high-speed gas metal arc welding process, weld defects like humping bead and undercutting appear and the weld bead quality worsens when welding speed exceeds a critical value. The previous investigations have proved that the backward flowing molten jet with high momentum in the weld pool is responsible for the formation of humping bead in high-speed gas metal arc welding. In this study, an external electromagnetic field generator is developed to change and control the flow condition in the weld pool and suppress the occurrence tendency of the humping bead. Bead-on-plate welding experiments are performed on mild steel Q235 test-pieces, and the influences of the exerted magnetic strength on the arc behavior, the fluid flow inside the weld pool, the weld bead shape and the weld penetration and width are investigated. The reason why the external electromagnetic field can suppress the humping bead is explained. The results show that external magnetic field can remarkably adjust the weld pool fluid flow field and decrease the momentum of the backward flowing molten jet. Therefore, weld defects like humping bead and undercutting can be effectively suppressed, the quality of weld bead is remarkably improved, and the critical welding speed is notably increased. KeywordsExternal magnetic field, fluid flow in weld pool, high-speed gas metal arc welding, humping bead Date

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In a recent paper published in this journal, Alonso‐Pecina et al. collect several sets of benchmark instances for the label printing problem from the literature and they also propose their own instances. Due to the intractability of the problem, no optimal solutions were declared for most of these instances. In this paper, we propose an integer linear programming model for the problem. We obtain optimal solutions or show that the solutions provided in the literature are already optimal for most of these instances based on the model. For some of the rest instances, we provide better solutions compared to the previous best solutions in the literature.