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Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
To provide scientific, theoretical support for the improvement of medical disaster training, we systematically analyzed the National Disaster Life Support (NDLS) Course and established a training curriculum with feedback based on the current status of disaster medicine in China.
The gray prediction model is applied to long-term forecast research on course effect. In line with the hypothesis, the NDLS course with feedback capability is more scientific and standardized.
The current training NDLS course system is suitable for Chinese medical disasters. After accepting the course training, audiences’ capabilities were enhanced. In the constructed GM (1,1) model prediction, the developing coefficients of the pretest and the posttest are 0.04 and 0.057, respectively. In light of the coefficient, the model is appropriate for the long-term prediction. The predicted results can be used as the basis for constructing training closed-loop optimization feedback. It can indicate that the course system has a good effect as well.
According to the constructed GM model, the NDLS course system is scientific, practical, and operational. The research results can provide reference for relevant departments and be used for the construction of similar training course systems.
A three-wavelength coherent-modulation-imaging (CMI) technique is proposed to simultaneously measure the fundamental, second and third harmonics of a laser driver in one snapshot. Laser beams at three wavelengths (1053 nm, 526.5 nm and 351 nm) were simultaneously incident on a random phase plate to generate hybrid diffraction patterns, and a modified CMI algorithm was adopted to reconstruct the complex amplitude of each wavelength from one diffraction intensity frame. The validity of this proposed technique was verified using both numerical simulation and experimental analyses. Compared to commonly used measurement methods, this proposed method has several advantages, including a compact structure, convenient operation and high accuracy.
We investigate the dynamic evolution of the price discovery function in Chinese agricultural futures markets using a newly developed rolling window cointegration approach. The results show that, compared with wheat and rice, the futures-spot cointegration relationship in the soybean and corn markets tends to be more durable and frequent. Dynamic cointegration analysis indicates that the recent market-oriented reforms in China have boosted the price discovery function of soybean and corn futures markets, whereas price stabilization policies tend to weaken the price discovery function of futures markets. The difference in price discovery function is attributed to differences in market mechanisms and Chinese agricultural policies.
A theoretical model is established to describe the thermal dynamics and laser kinetics in a static pulsed exciplex pumped Cs–Ar laser (XPAL). The temporal behaviors of both the laser output power and temperature rise in XPALs with a long-time pulse and multi-pulse operation modes are calculated and analyzed. In the case of long-time pulse pumping, the results show that the initial laser power increases with a rise in the initial operating temperature, but the laser power decreases quickly due to heat accumulation. In the case of multi-pulse operation, simulation results show that the optimal laser output power can be obtained by appropriately increasing the initial temperature and reducing the thermal relaxation time.
Evaluation of Cr, Mn, Fe, Zn and Se in humans is challenged by the potentially high within-individual variability of these elements in biological specimens, which are poorly characterised. This study aimed to evaluate their within-day, between-day and between-month variability in spot samples, first-morning voids and 24-h collections. A total of 529 spot urine samples (including eighty-eight first-morning voids and 24-h collections) were collected from eleven Chinese adult men on days 0, 1, 2, 3, 4, 30, 60 and 90 and analysed for these five elements using inductively coupled plasma-MS. Intraclass correlation coefficients (ICC) were utilised to characterise the reproducibility, and their sensitivity and specificity were analysed to assess how well a single measurement classified individuals’ 3-month average exposures. Serial measurements of Zn in spot samples exhibited fair to good reproducibility (creatinine-adjusted ICC = 0·47) over five consecutive days, which became poor when the samples were gathered months apart (creatinine-adjusted ICC = 0·33). The reproducibility of Cr, Mn, Fe and Se in spot samples was poor over periods ranging from days to months (creatinine-adjusted ICC = 0·01–0·12). Two spot samples were sufficient for classifying 60 % of the men who truly had the highest (top 33 %) 3-month average Zn concentrations; for Cr, Mn, Fe and Se, however, at least three specimens were required to achieve similar sensitivities. In conclusion, urinary Cr, Mn, Fe, Zn and Se concentrations showed a strong within-individual variability, and a single measurement is not enough to efficiently characterise individuals’ long-term exposures.