Modulation of Sn concentration in ZnO nanorod array: intensification on the conductivity and humidity sensing properties

Tin (Sn)-doped zinc oxide (ZnO) nanorod arrays (TZO) were synthesized onto aluminum-doped ZnO-coated glass substrate via a facile sonicated sol–gel immersion method for humidity sensor applications. These nanorod arrays were grown at different Sn concentrations ranging from 0.6 to 3 at.%. X-ray diffraction patterns showed that the deposited TZO arrays exhibited a wurtzite structure. The stress/strain condition of the ZnO film metamorphosed from tensile strain/compressive stress to compressive strain/tensile stress when the Sn concentrations increased. Results indicated that 1 at.% Sn doping of TZO, which has the lowest tensile stress of 0.14 GPa, generated the highest conductivity of 1.31 S cm− 1. In addition, 1 at.% Sn doping of TZO possessed superior sensitivity to a humidity of 3.36. These results revealed that the optimum performance of a humidity-sensing device can be obtained mainly by controlling the amount of extrinsic element in a ZnO film

Investigation on heat transfer characteristics and flow performance of methane at supercritical pressures

Heat transfer and flow characteristics of cryogenic methane in regenerative cooling system at supercritical pressures has been determined numerically. Thermo-physical properties of supercritical methane are discussed. In addition to that, previous studies on experiment and simulation are reviewed. All geometry, related equations, boundary conditions and performance parameters are reviewed in detail. For mesh independence test and model validation, simulation results are compared to experimental work by Gu et al. (Gu, Li et al. 2013). It is found that simulation results show good agreement with experimental data. All data deviation lies within 10 % which is generally accepted by former researchers. The effects of four different performance parameters namely inlet pressure, inlet temperature, heat flux and mass flux on heat transfer and flow performance of supercritical methane in horizontal miniature tube are identified. Several results are generated based on experimental conditions which include inlet pressure of 5 to 8 MPa, inlet temperature of 120 to 150 K, heat flux of 2 to 5 MW/m2 and mass flux of 7000 to 15000 kg/m2s. Heat transfer performance factor and friction factor are then computed to obtain Goodness factor which optimum parameters for certain boundary conditions are chosen. Lastly, statistical analysis with Response Surface Method (RSM) is carried out to obtain regression for both heat transfer and pressure drop

The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis

Parhat Yasin,1 Muradil Mardan,2 Dilxat Abliz,3 Tao Xu,1 Nuerbiyan Keyoumu,4 Abasi Aimaiti,4 Xiaoyu Cai,1 Weibin Sheng,1 Mardan Mamat1 1Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China; 2School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 3Department of Orthopedic, The Eighth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China; 4Department of Anesthesiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of ChinaCorrespondence: Mardan Mamat, Tel +86 0991-4365316, Email [email protected]: Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS.Methods: This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations. The region of interest (ROI) was defined by two radiologists using a 3D Slicer open-source platform, utilizing blind analysis of sagittal CT images against histopathological examination results. PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. Machine learning algorithms were trained and evaluated using techniques like the area under the receiver operating characteristic curve (AUC; confusion matrix-related metrics, calibration plot, and decision curve analysis to assess their ability to differentiate PS from BS. Additionally, permutation feature importance (PFI; local interpretable model-agnostic explanations (LIME; and Shapley additive explanation (SHAP) techniques were utilized to gain insights into the interpretabilities of the models that are otherwise considered opaque black-boxes.Results: A total of 15 radiomics features were screened during the analysis. The AUC value and Brier score of best the model were 0.88 and 0.13, respectively. The calibration plot and decision curve analysis displayed higher clinical efficiency in the differential diagnosis. According to the interpretation results, the most impactful features on the model output were wavelet LHL small dependence low gray-level emphasis (GLDN).Conclusion: The CT-based radiomics models that we developed have proven to be useful in reliably differentiating between PS and BS at an early stage and can provide a reliable explanation for the classification results.Keywords: Brucella spondylitis, Pyogenic spondylitis, machine learning, radiomics, model interpretatio

A cross-cultural study investigating body features associated with male adolescents\u27 body dissatisfaction in Australia, China, and Malaysia

This study investigated how dissatisfaction with particular aspects of the body was associated with overall body dissatisfaction among male adolescents in Western and Asian cultures. One hundred and six Malaysian Malays, 55 Malaysian Chinese, 195 Chinese from China, and 45 non-Asian Australians aged 12 to 19 years completed a questionnaire assessing dissatisfaction with their overall body and dissatisfaction with varying aspects of their body. Dissatisfaction with the face, height, and hair was positively correlated with overall body dissatisfaction among Malaysian Malays after body mass index, age and dissatisfaction with body areas typically included in measures (weight/shape, upper, middle, and lower body, and muscles) had been controlled for. Dissatisfaction with the face was positively correlated with overall body dissatisfaction among Malaysian Chinese. These findings demonstrate the differences in body focus for males from different cultures and the importance of using assessment measures that address all possible areas of body focus