Recently, meta-heuristic optimization algorithms have enhanced resource efficiency, facilitated
informed decision-making, and addressed complex problems involving multiple variables and
constraints in engineering and science fields. However, numerous handicaps are reported on the
performance of a quite number of these optimizers, such as local solution trapping, slow convergence
and the requirements for elevated storage and computation capability. This article proposes a
novel, simple, and elaborate remedy for the reported deficiencies of meta-heuristic optimizers. This
deficiency is accomplished by proposing a hybrid optimizer composed of an ambiguous optimizer
and Artificial Intelligence (AI). The performance of the proposed technique is evaluated using four
different meta-heuristic optimizers: Genetic Algorithm (GA), Particle Swarm Optimization (PSO),
Teaching–Learning-Based Optimization (TLBO), and Artificial Gorilla Troops Optimization (AGTO).
These optimizers range from the mature to the recently evolved. These meta-heuristic optimizers
validate the proposed solver and confirm its applicability to any meta-heuristic optimization algorithm.
Economic Dispatch (ED) of the IEEE 30-bus system is utilized to evaluate the performance of the
proposed solver. The comprehensive results demonstrate the superiority, reliability, and adequacy
of the proposed technique. It consistently converges to the global optimum solution, achieving the
minimum energy cost of the system under concern while requiring the fewest iterations and minimal
computational requirements.

Polymers are supposed to resist cavitation erosion more than metals; however, products fabricated using additive manufacturing have different behavior. In this paper, Polylactic Acid (PLA) fabricated using Fused Deposition Modeling (FDM) was tested in a constraint environment of cavitation erosion. Test specimens were fabricated in layer thickness of 0.3 mm on MakerBot 3D printer. Cavitation erosion experiments were con- ducted using a vibratory cavitation device. The results revealed that the specimens resist cavitation erosion more than that of metals. This can be interpreted and attributed to simultaneous contributors such as porosity, damping effect, water absorption, and roughness. The variation of roughness and morphology with time were verified this result. We increased the test time for several hours; however, we observed a hump formed on the induced area. This hump can be interpreted because of separation of the top layer of the specimen due to shock waves and pitting.

The increasing demand for durable and sustainable asphalt pavements has led to the exploration of high-density polyethylene (HDPE) as a modifier in hot asphalt mixtures (HAMs). This study presents a comprehensive framework that integrates Deep Residual Neural Networks (DRNNs) and variance-based global sensitivity analysis (VBSA) to optimize HDPE-modified asphalt mixtures (HDPE-HAMs). By integrating experimental findings with well-documented research, the model’s accuracy and robustness were significantly enhanced, making it more reliable for predicting the performance of HDPE-HAMs across various settings. Preprocessing analysis, including statistical evaluation and correlation analysis, ensures the selection of the most relevant features, while postprocessing analysis using VBSA identifies dominant factors influencing performance. The proposed DRNNs model accurately predicted Marshall stability and flow with high reliability (R² = 0.94 and 0.91). The VBSA revealed that bitumen content, polymer additive percentage, and voids in mineral aggregate are the most influential parameters governing mixture performance. Laboratory results confirmed that incorporating 12% HDPE enhanced stability by 21%, reduced flow by 23%, and improved retained strength by 7% after moisture conditioning compared to the control mix. This data-driven approach not only advances asphalt mixture design but also provides a replicable framework for analyzing various pavement materials, promoting sustainable and cost-effective infrastructure development.

Split-source inverters (SSIs) found vast research
concerns as they utilize lower component numbers and sizes
than other solutions, such as Z-source inverters (ZSIs) and
quasi-ZSIs (qZSIs). Recently, multiple photovoltaic (PV) input
port-based SSI has led to a further reduction of the needed
components compared to single-input topologies. However, controlling
multiple inputs with possible different generated powers,
generating high-quality ac output voltage and current, and
managing SSI’s inductor currents and capacitor voltage control
represent challenging tasks for classical pulse width modulation
(PWM) and other classical control methods. Therefore,
a multiple-objective-based model predictive controller (MPC)
with minimized computational burdens is proposed in this
article based on two novel approaches, namely, the simplified
current-based finite control set model-predictive control (SCFCSMPC)
approach and the simplified voltage-based finite
control set model-predictive control (SV-FCSMPC) approach.
The two proposed approaches ensure effective control of input
sources during partial or complete shading in the case of two
input PV sources. Moreover, the proposed approaches eliminate
the need for weighting factors in the control of the cost function,
simplifying the MPC design. Consequently, the two proposed
MPC approaches avoid cascaded loops for controlling multiple
input topologies, weighting factor adjustment procedures, and
high computation burden problems. Experimental results with
performance evaluations at different expected scenarios are provided
in this article to confirm the superiority and applicability
of the newly proposed weighting factorless MPC approaches.