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Milk is often referred to as the ultimate food because it meets the nutritional needs of infants, children and adults alike. It is a rich source of protein, fat, sweetness, vitamins and minerals. Because of its widespread usage as a healthy dairy product, the issue of milk adulteration is of global significance. The increasing frequency of fraudulent methods in the dairy business raises concerns about its purity and quality.

A study was conducted and reviewed that looked at several approaches for detecting milk adulteration during the past 15 years. This study examines the current state of research and analyzes recent advances in development.

There are ways and technology available that can effectively put an end to the abhorrent practice of milk adulteration.

This research takes a unique approach, focusing on the application of milk adulteration. It provides an overview of milk adulteration detection and investigates the effectiveness of biosensors in identifying common milk adulterants.

This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.

An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.

Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.

These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.