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Traditional piezoelectric materials cause serious environmental and health issues because of their lead–containing substances, and thus lead–free piezoelectric ceramics would have many applications. In this study, we successfully prepared the lead–free ceramics (1- x) (K 0 · 48 Na 0 · 48 Li 0.04) (Nb 0 · 975 Sb 0.025)O 3 – x (Bi 0 · 5 Na 0.5) (Zr 0 · 8 Ti 0.2)O 3 ((1- x)KNLNS– x BNZT, x = 0, 0.005, 0.0075, 0.01, 0.015, 0.02 and 0.03) with rhombohedral–tetragonal (R–T) phase coexistence near room temperature, employing con–entional solid sintering. The minimum cell volume of the ceramics and R–T phase coexistence in them were achieved at a doping amount x = 0.01, which facilitated the optimal performance of the ceramics (d 33 ∼467 pC/N ± 14 pC/N, T c∼272 °C, ε r ∼1676, k p ∼0.42, where d 33 : piezoelectric constant; T c: Curie temperature; ε r : dielectric constant; k p : electromechanical coefficient). The ceramics exhibited microstructures with high density and low porosity and had a maximum relative density of 95.68 %. At x = 0.01, the ceramics exhibited a uniformly distributed small domain structures with nanodomains, accounting for their high-voltage electrical properties. Because these stable and reliable piezoelectric (1- x)KNLNS– x BNZT ceramics exhibit excellent electrical performance, they can replace lead–based materials. The direct solid–phase sintering method provides prospects for the large–scale application of the lead–free piezoelectric ceramics. The 0.99(K 0 · 48 Na 0 · 48 Li 0.04)(Nb 0 · 975 Sb 0.025)O 3 –0.01(Bi 0 · 5 Na 0.5)(Zr 0 · 8 Ti 0.2)O 3 ceramics exhibit excellent piezoelectric properties owing to their nanodomain structures, rhombohedral–tetragonal coexistence phase boundaries at room temperature (25°C ± 5°C) and dense microstructures. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, there has been a strong interest in the minimum error entropy (MEE) criterion derived from information theoretic learning, which is effective in dealing with the multimodal non-Gaussian noise case. However, the kernel function is shift invariant resulting in the MEE criterion being insensitive to the error location. An existing solution is to combine the maximum correntropy (MC) with MEE criteria, leading to the MEE criterion with fiducial points (MEEF). Nevertheless, the algorithms based on the MEEF criterion usually require higher computational complexity. To remedy this problem, an improved MEEF (IMEEF) criterion is devised, aiming to avoid repetitive calculations of the a p o s t e r i o r i error, and an adaptive filtering algorithm based on gradient descent (GD) method is proposed, namely, GD-based IMEEF (IMEEF-GD) algorithm. In addition, we provide the convergence condition in terms of mean sense, along with an analysis of the steady-state and transient behaviors of IMEEF-GD in the mean-square sense. Its computational complexity is also analyzed. Simulation results demonstrate that the computational requirement of our algorithm does not vary significantly with the error sample number and the derived theoretical model is highly consistent with the learning curve. Ultimately, we employ the IMEEF-GD algorithm in tasks such as system identification, wind signal magnitude prediction, temperature prediction, and acoustic echo cancellation (AEC) to validate the effectiveness of the IMEEF-GD algorithm. • We propose an IMEEF criterion for the high computational complexity of the MEEF. • Based on the IMEEF criterion, we derive a gradient-based algorithm. • We analyze the performance of the proposed algorithm based on statistical properties. • The experimental results show that the proposed algorithm has excellent performance. [ABSTRACT FROM AUTHOR]

Highlights: [•] Exposure to lanthanides impaired renal function in mice. [•] Exposure to lanthanides induced cell necrosis in kidney. [•] Exposure to lanthanides promoted ROS accumulation in kidney. [•] Exposure to lanthanides caused the reduction of antioxidant capacity in kidney. [•] The order of kidney damages was Ce exposure>Nd exposure>La exposure. [Copyright &y& Elsevier]

Two robust kernel adaptive filter (KAF) algorithms, called the kernel least mean p-power (KLMP) and kernel recursive least mean p-power (KRLP), are developed by combining mean p-power error (MPE) criterion and kernel trick for noisy chaotic time series prediction (CTSP). The proposed algorithms employ the MPE to overcome the performance degradation of the CTSP when training data are corrupted by impulsive noises (especially the α-stable noises). First, the KLMP algorithm is proposed by the gradient decent method to improve the robustness of the traditional kernel least mean square (KLMS). Second, the recursion idea and the kernel method are utilized to develop a recursive KAF, namely KRLP, to improve the robustness of the traditional kernel recursive least squares (KRLS). Simulation results show that the proposed algorithms display notable robustness in CTSP when the training data contain different levels of noises, and can perform better in terms of testing MSE than other algorithms. [ABSTRACT FROM AUTHOR]