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Potassium sodium niobate (KNN) is one of the most promising Pb-free piezo-ceramics. In the present work, KNN was produced by fast-firing with different cooling strategies, i.e., fast and slow cooling. Dielectric, ferroelectric, and piezoelectric properties of fast-fired pellets were determined and compared with those of conventional-sintered products. Although the samples produced by fast-firing had higher density than those obtained by conventional sintering, fast-cooled samples following fast firing show relatively low electrical properties. When fast-firing was combined with slow cooling, the electrical properties, especially piezoelectric d33 values, were improved. The material subjected to fast-firing at 1120 °C using slow cooling gave the highest relative density (about 95 %) with fine grains microstructure and a d33 of 112 pC/N whereas that produced by conventional sintering resulted in d33 of 80 pC/N with a relative density of 88 % for the same dwell time (30 min).

Exercises are crucial for enhancing and maintaining bone and muscle health. ‘Preventative Strategies’ including certain exercises are fundamental in osteosarcopenia’s management. Determining the Frequency, Intensity, Time, and Type are major elements of prescribing an effective exercise program for osteoporosis. Patients with osteoporosis may need modifications in exercise programs due to pain, kyphosis, poor balance, arthritis, frailty, neuromuscular impairments, and cardiovascular and/or pulmonary diseases. In addition, specific exercises for accompanying disorders should be given priority. Consequently, exercise recommendations for osteoporosis must be target-specific, safe, effective, and tailored to patients’ needs.

Cameras are limited in their ability to capture all-in-focus images due to their limited depth of field. This results in blurriness for objects too far in front of or behind the focused point. To overcome this limitation, multi-focus image fusion (MFIF) approaches have been proposed. Although recent MFIF methods have shown promising results for this task, they still need to be improved in terms of artifacts and color degradation. Motivated by these observations, in this paper, we propose a new Generative Adversarial Network (GAN)–based MFIF model to improve fusion quality by predicting more accurate focus maps thanks to a trainable guided filter we incorporated. The proposed model comprises an encoder-decoder network, and a trainable self-guided filtering (TSGF) module that is specifically designed to enhance spatial consistency in the predicted focus map and to eliminate the requirements of post-processing in existing GAN-based methods. The encoder-decoder network first predicts raw focus maps, which are then passed to the TSGF to produce the final focus maps. To train the proposed model effectively, we define three objectives: L1 loss, GAN loss, and Focal Frequency Loss (FFL) in the frequency domain. L1 loss is defined on ground-truth and predicted focus maps, whereas GAN loss and FFL are defined on ground-truth all-in-focus images and fused images. Experimental results show that the proposed approach outperforms the existing GAN-based methods and achieves highly competitive performance with state-of-the-art methods in terms of standard quantitative image fusion metrics and visual quality on three MFIF benchmark datasets.

Purpose: To observe the effects of caffeine intake on vessel densities in macular and peripapillary regions and to analyze the correlations of vascular properties of these two interlinked regions in healthy volunteers using optical coherence tomography angiography (OCTA). Methods: In this prospective randomized, double-masked, placebo-controlled study, 120 participants were grouped into two groups: sixty subjects as a study group, which received caffeine and sixty subjects as a placebo group, which received a placebo. OCTA imaging was performed at baseline and 1 h after drug intake. Changes in macular and radial peripapillary capillary (RPC) vessel densities (VD) and foveal avascular zone (FAZ) parameters and correlation of these parameters were analyzed. Results: Baseline macular and RPC VD and FAZ parameters showed no significant difference between study and placebo groups (P > 0.05 for all). VD parameters in all segments of superficial and deep macular regions and peripapillary region showed a statistically significant decrease after caffeine intake (P < 0.001 for all). All macular superficial VD parameters showed a positive correlation with deep perifovea VD (P < 0.05 for all). Also, superficial perifovea VD was positively correlated with all deep macular VD parameters except deep fovea VD (P < 0.05 for all). Conclusion: We show that caffeine causes a decrease in macular and peripapillary vessel densities in healthy subjects, and correlations were observed between macular and peripapillary VD parameters. We believe that future studies are needed to support the effect of caffeine on peripapillary VD and the idea of using deep perifovea VD as the main parameter to estimate superficial macular parameters.