Advanced Journal of Management, Humanity and Social Science

Abstract Cooling systems in thermal power plants play a decisive role in maintaining steam cycle efficiency, enhancing equipment reliability, and reducing maintenance costs. The chemical quality of water in these systems is one of the most critical factors affecting corrosion rates, scale formation, and heat transfer degradation. In the present study, the water chemistry and corrosion behaviour in the hybrid (dry-wet) cooling system of a sample thermal power plant were investigated. Sampling was conducted on demineralised water, recirculating water of the dry tower, inlet water to heat exchangers, wet tower water, and makeup water. General parameters including pH, electrical conductivity, TDS, temperature, and salinity were measured. Additionally, the concentrations of metallic elements were determined using ICP-MS. The results indicated that the demineralised water exhibits exceptionally high purity and is virtually devoid of metal ions, whereas in the dry cycle, a significant increase in aluminium concentration up to approximately 260 ppb was observed, indicative of active corrosion in the aluminium radiators of the dry tower. In contrast, in the wet tower, the concentrations of calcium, sodium, potassium, silica, copper, and zinc increased markedly, attributable to evaporation, concentration of dissolved solids, and the use of wastewater as make-up water. Comparison of the results with EPRI guidelines revealed that although iron and copper levels fall within acceptable ranges, the elevated aluminium concentration may be regarded as the primary indicator of heat transfer equipment degradation. Accordingly, the revision of the water chemistry control programme, continuous monitoring of dissolved aluminium, and reassessment of the dry tower operating conditions are proposed as the most critical management strategies. The findings of this research can serve as a basis for the development of intelligent corrosion monitoring programmes in power plants equipped with hybrid cooling systems.

Abstract The proliferation of Artificial Intelligence (AI) companions—conversational agents designed to provide social interaction and emotional support—has prompted significant scholarly debate regarding their psychological and social consequences, particularly for young adults. This study investigates the impact of AI companion use on loneliness, emotional attachment to AI, and interpersonal communication competence among university students (N = 1,200). Utilizing a cross-sectional survey design, we measured social-interaction burnout, subjective loneliness, AI parasocial interaction, emotional attachment to AI, and interpersonal communication skills. Our findings reveal a complex, paradoxical relationship: while AI companions offer immediate relief from loneliness (β = 0.254, p < .001) and are perceived as low-risk sources of social connection, they also significantly predict emotional dependence (β = 0.318, p < .001) which, in turn, negatively correlates with interpersonal communication competence (β = -0.41, p < .001). Furthermore, social-interaction burnout emerged as a strong predictor of AI attachment (β = 0.238, p < .001). The results support the "Algorithmic Sanctuary" hypothesis, suggesting that young adults may retreat to AI interactions to escape the perceived judgment and demands of human relationships, a process that paradoxically may impair the very social skills needed to forge meaningful human connections. This study underscores the need for digital literacy interventions that address the developmental and relational implications of sustained AI companionship.