Monodisperse ternary ferrite (MFe2O4, M = Co, Ni, Mn, and Fe) nanocrystals have been synthesized through a facile and general route involving thermolysis of an intimately mixed binary metal−oleate complex with similar decomposition temperature of the constituents.

[reaction: see text] The side product from homocoupling reaction of two terminal acetylenes in the Sonogashira reaction can be reduced to about 2% using an atmosphere of hydrogen gas diluted with nitrogen or argon. Terminal arylethynes, diarylethynes, and a few new arylpyridylethynes with donor substituents have been synthesized in very good yields. Comparative control experiments suggest that the homocoupling yield is determined by concentration of both catalyst and oxygen.

This paper presents a micro-scale air flow sensor based on a free-standing cantilever structure. In the fabrication process, MEMS techniques are used to deposit a silicon nitride layer on a silicon wafer. A platinum layer is deposited on the silicon nitride layer to form a piezoresistor, and the resulting structure is then etched to create a freestanding micro-cantilever. When an air flow passes over the surface of the cantilever beam, the beam deflects in the downward direction, resulting in a small variation in the resistance of the piezoelectric layer. The air flow velocity is determined by measuring the change in resistance using an external LCR meter. The experimental results indicate that the flow sensor has a high sensitivity (0.0284 Ω/ms-1), a high velocity measurement limit (45 ms-1) and a rapid response time (0.53 s).

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Micro-electro-mechanical system (MEMS) devices integrate various mechanical elements, sensors, actuators, and electronics on a single silicon substrate in order to accomplish a multitude of different tasks in a diverse range of fields. The potential for device miniaturization made possible by MEMS micro-fabrication techniques has facilitated the development of many new applications, such as highly compact, non-invasive pressure sensors, accelerometers, gas sensors, etc. Besides their small physical footprint, such devices possess many other advantages compared to their macro-scale counterparts, including greater precision, lower power consumption, more rapid response, and the potential for low-cost batch production. One area in which MEMS technology has attracted particular attention is that of flow measurement. Broadly speaking, existing micro-flow sensors can be categorized as either thermal or non-thermal, depending upon their mode of operation. This paper commences by providing a high level overview of the MEMS field and then describes some of the fundamental thermal and nonthermal micro-flow sensors presented in the literature over the past 30 years or so.Keywords Cantilever type flow sensor Á Differential pressure flow sensor Á Lift force flow sensor Á Micro-electro-mechanical systems Á Resonating flow sensor Á Thermal flow sensor Origin and applications of MEMS technologyThe concept of micro-electro-mechanical systems (MEMS) dates back to the early 1960s, but only became an achievable reality when the tools and techniques originally developed for integrated circuit (IC) manufacturing became sufficiently advanced. Whereas electronic semiconductor devices are fabricated using IC process sequences, MEMS devices are produced by selectively etching away parts of the underlying silicon wafer or adding and patterning additional metallic and polymer layers to form the required mechanical and electromechanical devices. Typical MEMs micro-fabrication techniques include deposition [e.g. electroplating, sputtering, chemical vapor deposition (CVD), etc.], photolithography, and etching (e.g. wet etching, reactive ion etching (RIE), etc.]. Although still an emerging technology, MEMS devices are being increasingly deployed for mainstream commercial and industrial applications such as pressure sensors, accelerometers, gas sensors, RF switches, micro-mirrors, etc. (Löfdahl and Gadel-Hak 1999). In addition to the obvious advantages accruing from their diminutive scale (including a greater portability, a more discrete implementation and a reduced power consumption), MEMS devices permit the bulk

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IMPORTANCE A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction. OBJECTIVE To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information. DESIGN, SETTING, AND PARTICIPANTS This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018. MAIN OUTCOMES AND MEASURES Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models. RESULTS A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867;-2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872;-2.32% loss], hypertension [AUROC, 0.879;-1.53% loss], and chronic kidney insufficiency [AUROC, 0.879;-1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; −2.67% reduction; acarbose AUROC, 0.870; −2.50 reduction; and systemic antifungal agents AUROC, 0.875; −1.99 reduction). CONCLUSIONS AND RELEVANCE The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.

Hyperspectral unmixing (HU) is a crucial signal processing procedure to identify the underlying materials (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. A well-known blind HU criterion, advocated by Craig in early 1990's, considers the vertices of the minimum-volume enclosing simplex of the data cloud as good endmember estimates, and it has been empirically and theoretically found effective even in the scenario of no pure pixels. However, such kind of algorithms may suffer from heavy simplex volume computations in numerical optimization, etc. In this work, without involving any simplex volume computations, by exploiting a convex geometry fact that a simplest simplex of N vertices can be defined by N associated hyperplanes, we propose a fast blind HU algorithm, for which each of the N hyperplanes associated with the Craig's simplex of N vertices is constructed from N − 1 affinely independent data pixels, together with an endmember identifiability analysis for its performance support. Without resorting to numerical optimization, the devised algorithm searches for the N (N − 1) active data pixels via simple linear algebraic computations, accounting for its computational efficiency. Monte Carlo simulations and real data experiments are provided to demonstrate its superior efficacy over some benchmark Craig-criterion-based algorithms in both computational efficiency and estimation accuracy.