All-dielectric absorber based on nano-graphite sheets/ionogels with configurable absorbing band.

With the increasing complexity of the electromagnetic environment, there is a growing demand for the manipulation of electromagnetic waves, leading to the rapid development in configurable microwave absorbers. All-dielectric absorbers offer broadband and high-intensity absorption effects in microwave absorption and shielding. However, they face a significant challenge: their performance is not adjustable once the design is completed. In this study, we propose a solution to this problem by creating all-dielectric absorbers with flexibly configurable absorbing properties. We achieve this by designing a composite material of ionogels/nano-graphite sheets into compressible deformable absorbing units that can be molded into different shapes using 3D printing modes. The plasticity allows us to change the performance of the all-dielectric absorber, including the microwave absorption intensity, absorption peak, frequency bandwidths, and wide-angle absorption performance. With this approach, we can flexibly manipulate electromagnetic waves using all-dielectric absorbers through different plasticity models.

Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method.

In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise increases with measurement accuracy and the signal-to-noise ratio (SNR). In compound digital holography microscopy (CDHM)-based measurements, precise identification of the tracking marker is critical to ensuring measurement precision. The reconstruction centering method (RCM) was proposed to suppress the drawbacks caused by installation errors and, at the same time, improve the correct identification of the tracking marker. The reconstructed center is considered to be the center of the microsphere, rather than the center of imaging in conventional digital holographic microscopy. This method was verified by simulation of rays tracing through microspheres and axial moving experiments. The axial displacements of silica microspheres with diameters of 5 µm and 10 µm were tested by CDHM in combination with the RCM. As a result, the SNR of the proposed method was improved by around 30%. In addition, the method was successfully applied to axial displacement measurements of overlapped microspheres with a resolution of 2 nm.

Association between common vaginal and HPV infections and results of cytology test in the Zhoupu District, Shanghai City, China, from 2014 to 2019.

BACKGROUND: HPV (human papillomavirus) is an important cause of cervical cancer. Cervical-vaginal infection with pathogens, such as herpes simplex virus (HSV), bacterial vaginosis Trichomonas vaginalis and vaginal candidiasis could be a cofactor. This study aimed to assess the relationship between vaginal infection with HPV genotype and cytology test results and analyze the relationship between vaginal and HPV infections and cervical cancer. METHODS: We performed a district-based study to elucidate the relationship among the vaginal and HPV infections and cervical cancer. We collected the cervical exfoliation data of 23,724 women admitted to the Shanghai Zhoupu Hospital and received ThinPrep cytology test (TCT) and HPV detection between 2014 and 2019. RESULTS: Total vaginal infection rate was 5.3%, and the HPV-positive group had a slightly higher vaginal infection rate than the HPV-negative group (P < 0.01). The incidence rate of cervical intraepithelial neoplasia or cervical cancer with vaginal infection was higher than without vaginal infection (P < 0.001). CONCLUSION: HPV/vaginal infection-positive women tended to have abnormal results of TCT. Women with vaginal infection were more likely to develop HPV infection. HSV combined with HPV infection was noted as a causal factor for HSIL.

Propagation of partially coherent beams with convex-shaped spatial coherence modulation in vertical turbulent links.

The convex partially coherent beam (CPCB) is a special type of nonuniformly correlated beam with a convex-shaped complex degree of coherence (DoC) distributions. Previously our research has illustrated the potential of CPCBs with super-Gaussian DoCs in free-space optical communications (FSOC), mainly manifested as self-focusing which can be transferred into extra scintillation reduction and SNR gain. In this study, the effects of the DoC transition slopes are analyzed and more details about the turbulence propagation of CPCBs with super-Gaussian shaped DoC are revealed. By means of wave optics simulation, the longitudinal intensity evolution of the CPCB is explored, showing that the DoC slope has a profound influence on the self-focusing features such as the focusing plane and the peak intensity. Aperture scintillation and mean SNR at the receiver end of some short-range vertical turbulent links are numerically computed. The obtained results show that, with CPCBs, an ~2 dB SNR gain can be achieved as compared to conventional Gaussian Schell-modal (GSM) beams. However, CPCBs are preferred only in shorter links, which is found to be relevant to the power-in-the-bucket of the receiving aperture. Furthermore, the impacts of the ratio of the source coherence time to the detector integration time are investigated, implying that the CPCB is less susceptible than the GSM. We have also examined the off-axis scintillation of the CPCB. Due to its convex-shaped DoC, the CPCB has significantly reduced off-axis scintillation, which can be beneficial in the presence of pointing errors.

C-Myc regulates radiation-induced G2/M cell cycle arrest and cell death in human cervical cancer cells.

AIM: The study was conducted to investigate the role of c-Myc in the regulation of ionizing radiation-induced cell cycle arrest and cell death in human cervical cancer cells. METHODS: Control and c-Myc-silenced Hela cells were collected at different time points after 60 Co γ-ray radiation. Flow cytometry was used to measure cell cycle distribution and apoptosis. Immunofluorescence was applied to determine the percentage of cells in M phase. Transmission electron microscopy and immunoblotting were used to detect the induction of autophagy after radiation. Immunoblotting was also used to measure the expression levels of apoptosis-related proteins. RESULTS: In c-Myc-silenced cells, radiation induced delayed but long-lasting G2/M arrest and an abnormal M phase compared with the control. In addition, c-Myc knockdown significantly inhibited apoptotic cell death induced by radiation. Meanwhile, radiation-induced autophagy appeared stronger in c-Myc-silenced cells. Mechanically, we found that Caspase 8 and survivin expression was decreased in c-Myc-silenced Hela-630 cells. CONCLUSIONS: These data showed that c-Myc serves as a co-regulator in radiation-induced G2/M cell cycle arrest and cell death in human cervical cancer cells.

Maximum availability and mineralogical control of chromium released from AOD slag.

AOD (argon oxygen decarburization) slag is the by-product in the stainless steel refining process. Chromium existing in AOD slag can leach out and probably poses a serious threat to the environment. To assess the leaching toxicity of chromium released from AOD slag, the temperature-dependent maximum availability leaching test was performed. To determine the controlling mineralogical phases of chromium released from AOD slag, a Visual MINTEQ simulation was established based on Vminteq30 and the FactSage 7.0 database. The leaching tests indicated that the leaching availability of chromium was slight and mainly consisted of trivalent chromium. Aging of AOD slag under the atmosphere can oxidize trivalent chromium to hexavalent chromium, which could be leached out by rainwater. According to the simulation, the chromium concentration in leachates was controlled by the freely soluble pseudo-binary phases in the pH = 7.0 leaching process and controlled by the Cr2O3 phase in the pH = 4.0 leaching process. Chromium concentrations were underestimated when the controlling phases were determined to be FeCr2O4 and MgCr2O4. Facilitating the generation of the insoluble spinel-like phases during the cooling and disposal process of the molten slag could be an effective approach to decreasing the leaching concentration of chromium and its environmental risk.

A clean and efficient route for extraction of vanadium from vanadium slag by electro-oxidation combined with ultrasound cavitation.

Extracting vanadium (V) from vanadium slag (VS) by the traditional roasting-leaching process has disadvantages of high energy consumption and high poisonous gases emission. In this work, a green and efficient route was developed to extract V from VS without roasting by electro-oxidation combined with ultrasound cavitation (EOUC) intensification in sulfuric acid solution. The leaching parameters (e.g., leaching temperature, sulfuric acid concentration, anodic current density, ultrasound power, liquid to solid ratio, leaching time and particle size) were optimized. The leaching mechanism was explored by comparing the leaching behavior and mineralogical evolution of the direct sulfuric acidic leaching (DSL), electro-oxidation-assisted sulfuric acidic leaching (EOSL), ultrasound cavitation-assisted sulfuric acidic leaching (UCSL) and EOUC methods. The results show that introducing electric field strengthens the ultrasound cavitation effect on slag particles in sulfuric acid solution. Under the optimum parameter of EOUC method, the leaching rate of V from VS is as high as 94.64 %. Using EOUC method can open the silicate-wrapped structure of the spinel, increase pore volume of VS from 0.00127 cm3 g-1 to 0.01124 cm3 g-1, decrease slag particle size from 26.8 µm to 16.4 µm and improve specific surface area from 0.508 m2 g-1 to 10.855 m2 g-1, which significantly accelerate V leaching process. The exposed spinel was oxidized by both electrochemical route and chemical route, forming a mixture of V3+ ion and VO2+ ion after leaching.

Trunk-Inspired SWCNT-Based Wrinkled Films for Highly-Stretchable Electromagnetic Interference Shielding and Wearable Thermotherapy.

Nowadays, the increasing electromagnetic waves generated by wearable devices are becoming an emerging issue for human health, so stretchable electromagnetic interference (EMI) shielding materials are highly demanded. Elephant trunks are capable of grabbing fragile vegetation and tearing trees thanks not only to their muscles but also to their folded skins. Inspired by the wrinkled skin of the elephant trunks, herein, we propose a winkled conductive film based on single-walled carbon nanotubes (SWCNTs) for multifunctional EMI applications. The conductive film has a sandwich structure, which was prepared by coating SWCNTs on both sides of the stretched elastic latex cylindrical substrate. The shrinking-induced winkled conductive network could withstand up to 200% tensile strain. Typically, when the stretching direction is parallel to the polarization direction of the electric field, the total EMI shielding effectiveness could surprisingly increase from 38.4 to 52.7 dB at 200% tensile strain. It is mainly contributed by the increased connection of the SWCNTs. In addition, the film also has good Joule heating performance at several voltages, capable of releasing pains in injured joints. This unique property makes it possible for strain-adjustable multifunctional EMI shielding and wearable thermotherapy applications.

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Application and innovation of artificial intelligence models in wastewater treatment.

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Range accuracy of photon heterodyne detection with laser pulse based on Geiger-mode APD.

In this paper, we propose a combined system of heterodyne detection with laser pulse and photon counting based on Geiger-mode avalanche photodiode (GM-APD) that is designed to achieve the range of remote non-cooperative target. Based on the heterodyne principle and assuming that the creation of primary electrons in GM-APD is Poisson-distributed, the range accuracy model is established. The factors that influence the range accuracy, namely pulse width, echo intensity, local oscillator (LO) intensity, noise, echo position, and beat frequency, are discussed. The results show that these six factors have significant influence on the range accuracy when the echo intensity is extremely weak. In case that the primary electrons of the echo signal are beyond 4, the pulse width and echo intensity are the main influence factors. It is also shown that the stronger echo intensity, narrower pulse width, low noise, large echo position, and small beat frequency produce higher range accuracy in a pulsed photon heterodyne detection system based on GM-APD.

A combined deformable model and medical transformer algorithm for medical image segmentation.

Deep learning-based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance.

Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

Background: Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features. Methods: This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum redundancy maximum relevance (mRMR) feature selection, and the top 10 features were determined by the least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating 3D radiomic signatures and radiological features in the prediction system. The nomogram was evaluated from multiple perspectives and tested on the validation cohort. Results: The model combined 3 radiological features and seven 3D radiomic signatures. The area under the curve (AUC) of the model was 0.877 (95% CI: 0.829-0.925) in the training cohort, 0.864 (95% CI: 0.789-0.940) in the testing cohort, and 0.836 (95% CI: 0.749-0.924) in the validation cohort. The nomogram applied in all 3 cohorts showed reliable accuracy and calibration. The decision curve also demonstrated the clinical effectiveness of the nomogram. Conclusions: In this study, a nomogram-based model combining 3D radiomic signatures and radiological features was developed. Its performance in identifying IAC and MIA/AIS was satisfactory and had clinical value.

Investigation of the acicular aragonite growth behavior in AOD stainless steel slag during slurry-phase carbonation.

This study presents a novel method for producing acicular aragonite using argon oxygen decarburization (AOD) slag while controlling the reaction temperature, reaction time, stirring speed, and the magnesium-to­calcium stoichiometric ratio. This approach provides steel plants with an opportunity to decrease their CO2 emissions and promote efficient resource utilization and CO2 storage through the production of high-quality value-added products. The experimental results showed that reaction temperature was the most significant factor affecting the carbonation efficiency of AOD slag, followed by reaction time, stirring speed, CO2 partial pressure, and the liquid-to-solid ratio (L/S). The study also found that elevated temperature and prolonged reaction duration favored the preferential precipitation of aragonite. Additionally, raising the temperature and the magnesium-to­calcium stoichiometric ratio was shown to enhance the formation of aragonite, affecting its crystal growth orientation and dimensions. The optimal combination of reaction parameters for the preparation of acicular aragonite was found to be the reaction time of 8 h, the magnesium-to­calcium stoichiometric ratio of 0.8, the reaction temperature of 120 °C, and the stirring speed of 200 r·min-1. Under these conditions, the resulting acicular aragonite exhibited excellent overall uniformity, a large aspect ratio, and a smooth crystal surface, with a content of 91.49 %, a single crystal length ranging from 9.86 to 32.6 µm, and a diameter ranging from 0.63 to 2.15 µm. This study provides valuable insights into the efficient production of acicular aragonite from steel slag while reducing CO2 emissions and promoting the sustainable use of resources.

Highly efficient removal of hexavalent chromium by magnetic Fe-C composite from reed straw and electric furnace dust waste.

Reed straw and electric furnace dust (EFD) waste were used to prepare magnetic Fe-C composite (EFD&C) by co-precipitation and high-temperature activation method to remove Cr(VI) from water. The magnetic EFD&C owned a large specific surface (536.61 m2/g) and a porous structure (micropores and mesopores), and had an efficient removal capacity for Cr(VI). Under conditions of pH (2), the addition amount of EFD&C (1 g/L), the adsorption time (760 min), and the temperature (45 °C), the maximum adsorption capacity reached 111.94 mg/g. The adsorption mechanism mainly attributed to chemical adsorption (redox), Cr(VI) reduced to Cr(III) by Fe(II) and Fe(0) (from Fe3O4 and Fe components in EFD) and surface functional groups of -OH, C = C, C-C and O-C = O (from biochar), and secondary attributed to physical adsorption, Cr(VI) and Cr(III) (from reduced Cr(VI)) adsorbed into the porous structure of EFD&C. This study provided a feasible solution for the preparation of adsorbents for adsorbing heavy metals from iron-containing metallurgical solid waste and biomass waste, which contributed to reducing the environmental pollution and lowering the cost of adsorbent preparation.

Slice spectra approach to synchronous Two-dimensional correlation spectroscopy analysis for milk adulteration discriminate.

The discrimination approach of adulterated milk was proposed combined synchronous two-trace two-dimensional (2T2D) correlation slice spectra at the characteristic wavebands of adulterant in milk with multivariate method. Two common adulterants, melamine and urea, were analyzed to demonstrate useful by the method. 2T2D (near infrared) NIR slice spectra at characteristic wavebands of adulterant were extracted from the synchronous 2T2D correlation spectra, and were input to construct the N-way partial least squares discriminant analysis (NPLS-DA) models. One-dimensional (1D) spectroscopy featuring all the present components in the samples combined with partial least squares discriminant analysis (PLS-DA) was also evaluated for comparison. The results indicated that for one kind of adulterant in model, prediction accuracies of slice spectral models were both 100% for melamine-adulterated and urea-adulterated samples discrimination. Moreover, for two kinds of adulterants in model, prediction accuracies of slice spectral models were 90.57% and 100% for melamine-adulterated and urea-adulterated discrimination, respectively, which was better than those of 1D whole models based on PLS-DA (only 81.13% and 98.15%, respectively). The comparison informs that the 2T2D slice spectra extracted at the characteristic wavebands of adulterant highlighted the adulterant spectral features and was obviously advantage to improve the discrimination accuracy. Meanwhile, the complexity of slice spectra is significantly reduced compared with the whole matrix of synchronous 2T2D correlation spectra.

Influence of brands on a discrimination model for adulterated milk based on asynchronous two-dimensional correlation spectroscopy slice spectra.

To improve the robustness of near infrared (NIR) identification models for the milk adulteration, a novel approach was explored based on asynchronous two-dimensional correlation spectroscopy (2D-COS) slice spectra obtained at characteristic wavebands for pure milk and adulterant combined with an N-way partial least squares discriminant analysis (NPLS-DA). NIR diffuse reflectance spectra from four different brands, Guangming (GM), Mengniu (MN), Sanyuan (SY), and Wandashan (WDS), were collected in range of 11,000 to 4000 cm-1. Influence of brands on discrimination models for adulterated milk was analyzed. The asynchronous 2D-COS slice spectra at 10 characteristics wavebands, including 4 wavebands for pure milk and 6 wavebands for urea, were input into NPLS-DA to construct discriminant models. External validations using five independent calibration sets from intrabrand or interbrand were established. The same prediction set of 26 SY samples was used to assess the prediction ability of different calibration sets and compared with traditional one-dimensional (1D) NIR spectra based on a partial least squares discriminant analysis (PLS-DA). It showed that for intrabrand model, the correct rates for the calibration and predication sets were 100% and 96.15%, respectively. For the interbrand model, the correct rates by the NPLS-DA for the calibration set of GM, MN, and WDS milk were both 100%. The corresponding rates for the prediction set were 73%, 88.46% and 69.23%, respectively, which were much higher than those of PLS-DA (only 50%, 53.83% and 50%, respectively). It was proven that model robustness was sensitive to changes in the milk brands. The proposed method can effectively reduce the influence of brands on the discrimination models.

Long-term leaching characterization and geochemical modeling of chromium released from AOD slag.

The long-term leaching of chromium from AOD slag was analyzed by column percolation test (CEN/TS 14405). According to the analytical result, the eluate of the AOD slag exhibited alkaline and reductive property. Chromium released from the AOD slag was primarily presented as trivalent chromium (Cr(III)). The eluate exhibited low hexavalent chromium (Cr(VI)) concentration. As the L/S ratio increased to 115 L kg-1, the accumulated release quantity of Cr(III) and total chromium per AOD slag mass reached 1549.68 and 1613.67 µg kg-1, respectively. The long-term leaching toxicity of chromium from the AOD slag was noticeable. Besides, a long-term geochemical model was built with PHREEQC software to assess the evolution of pH and chromium concentration in the eluate. The simulated pH and chromium concentrations were well consistent with those of the column percolation experiment. The result suggested that the geochemical model for chromium leaching prediction applies to the assessment of the eco-risk of AOD slag during the long-term leaching. The concentration of trivalent chromium presenting as Cr(OH)4- for instability of Cr(III) hydroxide in the alkaline eluate was regulated by the dissolution of the primary phase Cr2O3.

A new precise positioning method for piezoelectric scanner of AFM.

Atomic Force Microscopy (AFM) plays a vital role in nanoscience and nanotechnology due to its nanoscale resolution. However, the realization of highly precise measurement for AFM is still a challenge. A main factor is the positioning accuracy of the piezoelectric scanner (PZT), affected significantly by the hysteresis of PZT. The paper reports a new dynamic polynomial fitting method modeling hysteresis to achieve the inverse model of the PZT. The inverse model is used as the feedforward input, combined with the fuzzy feedback controller proposed in our former paper, to correct the nonlinear errors induced by the hysteresis. The method is demonstrated to be effective in improving the positioning accuracy of the lateral PZT. Its accuracy can achieve 1 nm.