Osteosarcoma (OS) is the most common primary bone malignancy that affects children and young adults. OS is characterized by a high degree of malignancy, strong invasiveness, rapid disease progression, and extremely high mortality rate; it is considered as a serious threat to the human health globally. The incidence of OS is common in the metaphysis of long tubular bones, but rare in the spine, pelvis, and sacrum areas; moreover, majority of the OS patients present with only a single lesion. OS has a bimodal distribution pattern, that is, its incidence peaks in the second decade of life and in late adulthood. We examine historical and current literature to present a succinct review of OS. In this review, we have discussed the types, clinical diagnosis, and modern and future treatment methods of OS. The purpose of this article is to inspire new ideas to develop more effective therapeutic options.

Nonstoichiometric Ag8–xGeTe6 (x = 0, 0.01, 0.02, 0.04) compounds with complex crystal structure are demonstrated to exhibit very low thermal conductivities of <0.28 W/m K, comparable with the calculated theoretical minimum thermal conductivity κmin. Ag deficiency leads to the improved electrical properties and a maximum thermoelectric figure of merit ZT of 0.85 has been obtained at 623 K for Ag7.99GeTe6, about 30% increase compared to that of stoichiometric Ag8GeTe6. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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Subzero‐temperature Li‐ion batteries (LIBs) are highly important for specific energy storage applications. Although the nickel‐rich layered lithium transition metal oxides(LiNixCoyMnzO2) (LNCM) (x > 0.5, x + y +z = 1) are promising cathode materials for LIBs, their very slow Li‐ion diffusion is a main hurdle on the way to achieve high‐performance subzero‐temperature LIBs. Here, a class of low‐temperature organic/inorganic hybrid cathode materials for LIBs, prepared by grafting a conducting polymer coating on the surface of 3 µm sized LiNi0.6Co0.2Mn0.2O2 (LNCM‐3) material particles via a greener diazonium soft‐chemistry method is reported. Specifically, LNCM‐3 particles are uniformly coated with a thin polyphenylene film via the spontaneous reaction between LNCM‐3 and C6H5N2+BF4−. Compared with the uncoated one, the polyphenylene‐coated LNCM‐3 (polyphenylene/LNCM‐3) has shown much improved low‐temperature discharge capacity (≈148 mAh g−1 at 0.1 C, −20 °C), outstanding rate capability (≈105 mAh g−1 at 1 C, −20 °C), and superior low‐temperature long‐term cycling stability (capacity retention is up to 90% at 0.5 C over 1150 cycles). The low‐temperature performance of polyphenylene/LNCM‐3 is the best among the reported state‐of‐the art cathode materials for LIBs. The present strategy opens up a new avenue to construct advanced cathode materials for wider range applications.

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Grain refinement has been conducted to reduce the thermal conductivity and improve the thermoelectric performance of the (Zr,Hf)NiSn based half-Heusler alloys. Nanoscale in situ precipitates were found embedded in the matrix with submicron grains. The lattice thermal conductivity was decreased due to the enhanced boundary scattering of phonons. The increased carrier concentration and electrical conductivity were observed compared to the coarse-grained alloys, which is discussed in relation to the existence of nanoscale precipitates, the effect of antisite defects, and composition change. It is suggested that the nanoscale precipitates play a significant role in the observed electrical conductivity increase. © 2012 American Institute of Physics. [http://dx.doi.org/l 0.1 063/I.4730436] ·In past decades, thermoelectric (TE) materials have received rejuvenated interest due to their promising applications in direct thermal to electric energy conversion and solidstate refrigeration. The performance of a TE material is represented by the dimensionless figure of merit ZI = r:t.ZaT!K, where IX is the Seebeck coefficient, (J' is the electrical conductivity, K is the thermal conductivity mainly including the electron contribution Ke and the lattice contribution KL, and T is the absolute temperature. 1• 2The MNiSn-based (M = Zr, Hf, and Ti) half-Heusler compounds have recently been identified as potential high temperature TE materials for power generation up to llOOK. -5 Their combination of high Seebeck coefficients and moderate electrical conductivities gives rise to intrinsically high TE power factors. 6 • 7 However, the high thermal conductivity (K::::: 10Wm-1 K-1 ) makes this system less promising for TE applications.7 Several strategies for suppressing the lattice thermal conductivity by increasing phonon scattering via point defects or grain boundary scattering have been implemented. [4][5][6][7][8][9][10] High ZI values of >0.8 have been achieved in the Sb doped (Zr,Hf)NiSn based alloys. • 10Although boundary scattering is generally effective in suppressing the lattice thermal conductivity, the reduction of grain sizes will also induce a decrease in electrical conductivity due to the enhanced scattering for both electrons and phonons. Sharp eta/. proposed that for the MNiSn alloys, the lattice thermal conductivity would decrease faster than the carrier mobility below tens of micrometers of grain sizes, because the mean free path of phonons is larger than that of electrons in the alloys.11 In this work, the MNiSn based alloys with submicron grain sizes were prepared by melt spinning in order to enhance the phonon scattering and keep the electrical properties almost unchanged. Unexpectedly, an electrical conductivity increase was observed, although the lattice thermal conductivity was decreased to some extent •>corresponding author. Electronic mail: zhutj@zju.edu.cn. due to the reduced grain sizes. Possible causes are discussed and it is suggested that the nanoscale in situ precipitates in the matrix play a significan...

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of predictive tools available to the thermal engineering community, the path for an accurate, robust CHF model remains elusive due to lack of consensus on the DNB triggering mechanism. This work aims to apply a physics-informed, machine learning (ML)-aided hybrid framework to achieve superior predictive capabilities. Such a hybrid approach takes advantage of existing understanding in the field of interest (i.e., domain knowledge) and uses ML to capture undiscovered information from the mismatch between the actual and domain knowledge-predicted target. A detailed case study is carried out with an extensive DNB-specific CHF database to demonstrate (1) the improved performance of the hybrid approach as compared to traditional domain knowledge-based models, and (2) the hybrid model's superior generalization capabilities over standalone ML methods across a wide range of flow conditions. The hybrid framework could also readily extend its applicability domain and complexity on the fly, showing an elevated level of flexibility and robustness. Based on the case study conclusions, the window-type extrapolation mapping methodology is further proposed to better inform high-cost experimental work. Keywords: critical heat flux, departure from nucleate boiling, hybrid framework, machine learning, domain knowledge. API application programming interface CHF critical heat flux DK domain knowledge DNB departure from nucleate boiling EPRI Electric Power Research Institute LUT look-up table MAE mean absolute error ML machine learning MSE mean squared error NN (feed-forward) neural network PWR pressurized water reactor ReLU rectified linear unit RF random forest rRMSE relative root-mean-square error