A home-made near-infrared laser tweezers Raman spectroscopy (LTRS) system was applied to detect hemoglobin variation in red blood cells (RBCs) from diabetes without exogenous labeling. Results showed significant spectral differences existed between the diabetic and normal RBCs, including the peaks dominated by protein components (e.g. 1003 cm) and heme groups (e.g. 753 cm) in RBCs, and accurate classification results for diabetes detection were obtained by linear discriminant analysis with 100% sensitivity (i.e. no false negatives in the study). This work indicated the great promise of LTRS as a label-free RBC analytical tool for improving the accurate detection of type II diabetes.

Noninvasive and sensitive thermometry is crucial to human health monitoring and applications in disease diagnosis. Despite recent advances in optical temperature detection, the construction of sensitive wearable temperature sensors remains a considerable challenge. Here, a flexible and biocompatible optical temperature sensor is developed by combining plasmonic semiconductor W 18 O 49 enhanced upconversion emission (UCNPs/ WO) with flexible poly(lactic acid) (PLA)-based optical fibers. The UCNPs/WO offers highly thermal-sensitive and obviously enhanced dual-wavelength emissions for ratiometric temperature sensing. The PLA polymer endows the sensor with excellent lighttransmitting ability for laser excitation and emission collection and high biocompatibility. The fabricated UCNPs/WO-PLA sensor exhibits stable and rapid temperature response in the range 298− 368 K, with a high relative sensitivity of 1.53% K −1 and detection limit as low as ±0.4 K. More importantly, this proposed sensor is demonstrated to possess dual function on real-time detection for physiological thermal changes and heat release, exhibiting great potential in wearable health monitoring and biotherapy applications.

In order to manage the construction schedule risk of power supply and distribution engineering, a construction schedule risk evaluation model, namely the Monte Carlo simulation method - Analytic Hierarchy Process (MCS-AHP) model, is proposed. In this model, the Monte Carlo simulation method is adopted to improve the analytic Hierarchy Process (AHP), and the normal distribution interval is used to replace the specific value when constructing the fuzzy complementary judgment matrix, to reduce the risk of fuzzy thinking and incomplete information or scattered data in the process of investigation and judgment and improve the scientific evaluation. This paper takes a power supply and distribution project in Guangdong Province as an example uses the MCS-AHP model to measure the key factors limiting the project progress, and uses the AHP method for comparative analysis, to verify the feasibility of the MCS-AHP model. The analysis shows that the key influencing factors are material and equipment procurement, production and arrival, installation of 10 kv high voltage switchboard, electrical acceptance and single machine commissioning, installation of low-voltage switchboard and DC switchboard, and foundation construction of power station equipment, etc., which are consistent with the actual situation. Therefore, it is feasible to construct the MCS-AHP model, which can provide a new way of thinking for schedule risk management analysis.