In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results.

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.

This study focuses on the automatic generation control (AGC) system, which is crucial for maintaining balance between power generation and demand in power systems. The implementation of the AGC system needs to be more precise due to the increasing uncertainty surrounding renewable energy sources (RESs) and changes in demand. The objective of this study is to investigate the AGC functions in a two-area hybrid power system that combines a PV system with a reheat thermal system. To improve system performance, we utilize a proportional-integral (PI) controller. We utilized a recently developed optimization method, RIME, for tuning controller parameters. This technique has not been studied before in AGC processes. Furthermore, the optimization procedure utilizes a modified version of the integral time-multiplied absolute error (ITAE) objective function. The study compares the performance of the RIME-tuned PI controller under various scenarios, including changes in thermal system load, load variations in both areas, and robustness considerations, with well-known techniques in the literature, such as black widow optimization algorithm (BWOA), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), firefly algorithm (FA) and genetic algorithm (GA). Our comparative study demonstrates that the proposed controller outperforms state-of-the-art approaches in terms of overshoot values and damping durations for both system frequency and tie-line power changes. The study provides valuable information on the effectiveness of the RIME-tuned PI controller in controlling AGC processes in complex hybrid power systems.

Localization of GPS-less UAV is fundamental for path planning and navigation. In this letter, a TDoA-based approach for self-localizing UAV is proposed. The approach utilizes multiple Base Stations (BSs) to obtain TDoA measurements without requiring exact UAV-BS synchronization. A reformulation of the measurements which relates the new transformed measurements to UAV’s initial position is derived. A least squares (LS) approach is proposed to solve the nonlinear equations. Sufficient condition of convexity of the LS cost function has been proved mathematically and verified experimentally. Also, a condition on step-size is derived to assure the convergence of the algorithm. This leads to efficient solutions for minimization. Results show that the proposed method has outstanding performance, especially, when compared to RSS-based UAV localization method.

This study assesses quad-band metamaterial perfect absorbers (MPAs) based on a double X-shaped ring resonator for electromagnetic interference (EMI) shielding applications. EMI shielding applications are primarily concerned with the shielding effectiveness values where the resonance is uniformly or non-sequentially modulated depending on the reflection and absorption behaviour. The proposed unit cell consists of double X-shaped ring resonators, a dielectric substrate of Rogers RT5870 with 1.575 mm thickness, a sensing layer, and a copper ground layer. The presented MPA yielded maximum absorptions of 99.9%, 99.9%, 99.9%, and 99.8% at 4.87 GHz, 7.49 GHz, 11.78 GHz, and 13.09 GHz resonance frequencies for the transverse electric (TE) and transverse magnetic (TM) modes at a normal polarisation angle. When the electromagnetic (EM) field with the surface current flow was investigated, the mechanisms of quad-band perfect absorption were revealed. Moreover, the theoretical analysis indicated that the MPA provides a shielding effectiveness of more than 45 dB across all bands in both TE and TM modes. An analogous circuit demonstrated that it could yield superior MPAs using the ADS software. Based on the findings, the suggested MPA is anticipated to be valuable for EMI shielding purposes.