Holmium (Ho³⁺)-doped tungsten tellurite glasses with the composition (70−x)TeO₂–10Na₂O–20WO₃–xHo₂O₃ (x = 0.5–1.5 mol%) were successfully synthesized using the conventional melt-quenching technique to investigate their suitability for visible upconversion and photonic applications. X-ray diffraction analysis confirmed the amorphous nature of all prepared samples, while differential scanning calorimetry demonstrated good thermal stability with a glass transition temperature of approximately 310 °C and a crystallization temperature near 500 °C. Various physical parameters, including density, molar volume, lanthanide ion concentration, polaron radius, interionic distance, field strength, and oxygen packing density, were evaluated to understand the structural modifications induced by Ho³⁺ incorporation. The density and lanthanide ion concentration increased systematically with increasing Ho³⁺ content, whereas the molar volume, interionic distance, and polaron radius decreased, indicating progressive densification and strengthening of the glass network. Optical absorption spectra exhibited characteristic Ho³⁺ transitions in the visible and near-infrared regions, confirming efficient incorporation of rare-earth ions into the tellurite glass matrix. Under 980 nm laser excitation, intense upconversion emissions centered at approximately 547 nm (green), 660 nm (red), and 760 nm (near-infrared) were observed, corresponding to the ⁵F₄/⁵S₂ → ⁵I₈, ⁵F₅ → ⁵I₈, and ⁵S₂ → ⁵I₇ transitions of Ho³⁺ ions, respectively. The emission intensity increased with excitation power, demonstrating efficient excited-state absorption and cross-relaxation processes responsible for the observed upconversion mechanism. The combination of favorable thermal stability, enhanced physical characteristics, and strong visible upconversion emission demonstrates that Ho³⁺-activated tungsten tellurite glasses are promising candidates for solid-state lasers, optical amplifiers, color display devices, and other advanced photonic applications.
Smriti Tiwari, Ghizal F. Ansari· International Journal of Lat...· 0 citations
Diabetes is one of the most rapidly increasing chronic diseases worldwide and early detection is essential for effective treatment and prevention of severe health complications. Accurate prediction of diabetic patients at an early stage can assist healthcare professionals in making better clinical decisions. In recent years, ML and DL techniques have gained significant attention for medical data analysis and disease prediction. This study presents an intelligent prediction model for early diabetic diagnosis using a MLP based DL approach. The proposed model utilizes medical attributes such as blood pressure, age, insulin level, body mass index (BMI), glucose level, and other health factors to forecast the risk of diabetes. To enhance the quality of the input characteristics, the dataset is first pre-processed using normalization and data cleaning methods. A Multilayer Perceptron neural network with several dense layers, batch normalization, and dropout layers is used to train the data after preprocessing to improve model generalization and lessen overfitting. The experimental findings show that the suggested MLP-based deep learning model reliably predicts outcomes and successfully learns intricate patterns from medical data. According to the simulation results, the suggested model can predict diabetic patients in their early stages with an accuracy of 87.68%. Therefore, the developed system can serve as a supportive decision-making tool for healthcare professionals to identify high-risk diabetic patients at an early stage and improve preventive healthcare management.
Sonam Pandey, Md. Vaseem Naiyer, Ghizal F. Ansari· International Conference Com...· 0 citations
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