Article Boronizing Effect on the Corrosion Behaviour of Chilled Cast Iron and AISI 1050 Steel was published on August 1, 2010 in the journal High Temperature Materials and Processes (volume 29, issue 4).

With the development of automotive technology, long life, high power, and low fuel consumption performance are expected from the internal combustion engine. This study investigated the effects of commercial oil additives on friction under different experimental conditions. In order to examine the friction effect, the samples prepared from the piston ring and cylinder were tested with the Reciprocating Test Rig at different speeds and loads. Using additives in the commercial oil lubricant showed a little reduction under the lower load. When the applied load increased commercial oil showed better friction coefficient performance in the experimental study. As for the expected results, the friction coefficient reduced with increasing sliding speeds under all conditions. In general, it was found that the commercial oil supplement did not have much effect on reducing the coefficient of friction when all the results were evaluated under these experimental conditions

This study investigates the thermodynamic and drying performance of a greenhouse dryer integrated with a parabolic trough solar collector (PTSC) and develops interpretable operating rules using a bio-inspired explainable artificial intelligence framework. Outdoor apple-drying experiments were conducted, and system performance was evaluated in terms of energy, drying, and exergy efficiencies. The experimental results indicated that energy efficiency ranged from 17.7% to 29.2%, drying efficiency from 1.0% to 9.7%, and exergy efficiency from 5.6% to 8.4%. Measured variables, including temperature, relative humidity, product weight, and solar radiation, were used to classify the efficiencies into low, medium, and high categories using the Chaotic Rule-based Strength Pareto Evolutionary Algorithm 2 (CRb-SPEA2). As a bio-inspired evolutionary computing approach, CRb-SPEA2 employs population-based search, selection, Pareto dominance, and multi-objective optimization mechanisms inspired by natural evolutionary processes. In contrast to conventional black-box machine learning models, the proposed method extracts explicit decision rules that define physically meaningful operating ranges. The maximum recall values were 0.952, 1.000, and 0.971 for the high-energy-, drying-, and exergy-efficiency classes, respectively. The extracted rules identified solar radiation, temperature, relative humidity, and product weight as dominant factors affecting dryer performance.