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.

Abstract Digital transformation has evolved from a technology-oriented initiative into a comprehensive organizational transformation that affects strategy, structure, processes, capabilities, culture, and value creation. At the same time, artificial intelligence (AI) has emerged as a central technological driver capable of redesigning organizational processes, supporting decision-making, automating repetitive activities, and enabling new forms of human–technology collaboration. However, the organizational value of AI not achieved through technological implementation alone. Successful implementation requires strategic alignment, organizational readiness, employee participation, leadership commitment, appropriate governance, and systematic change management. This study investigates the relationship between digital transformation strategies, AI implementation, organizational process transformation, and change management through a structured review and synthesis of the relevant literature. The analysis integrates perspectives from digital transformation, dynamic capabilities, technology adoption, organizational change, and human–AI collaboration. The findings indicate that successful AI-enabled transformation depends on four interconnected dimensions: strategic alignment and digital capabilities, process redesign and technological integration, human-centered change management, and governance and continuous organizational learning. The evidence further suggests that organizations achieve greater transformation value when AI is implemented as part of redesigned organizational processes rather than as an isolated technological tool. Leadership communication, employee training, participation, trust, and reinforcement mechanisms appear particularly important for reducing resistance and supporting sustainable adoption. The study proposes an integrated framework in which digital strategy establishes direction, AI provides technological capabilities, process redesign converts these capabilities into operational value, and change management facilitates organizational adoption and institutionalization. The results contribute to the literature by connecting technological transformation with the human and organizational mechanisms required for sustainable AI-enabled transformation.

Abstract The present study aims to identify the constraints prevailing over the establishment of good governance and the parliamentarization of the Islamic Republic system in Iran’s positive laws, by examining the impact of establishing a parliamentary system on strengthening the foundations of good governance in Iran. The researcher believes that establishing a parliamentary system can serve as an effective strategy for strengthening these foundations and realizing popular government. Although this change may introduce new challenges and consequences in the governmental structure, a thorough examination of these effects and their feasibility can pave the way for improving governance in Iran’s political system. Currently, Iran’s political system is recognized as a semi-presidential–semi-parliamentary system, and the existing electoral systems have been shaped in accordance with this arrangement over the past few decades. However, to fully realize the components of good governance, it appears necessary to create and implement an indigenous model of an electoral system. The transition from the current presidential–parliamentary system toward a parliamentary system requires the design of a specific electoral system. The electoral system plays a significant role in shaping and influencing components such as rule of law, participation, accountability, responsiveness, transparency, and effectiveness; at the same time, its success also depends on the proper functioning of these components. This research, conducted using an analytical-descriptive method, shows that under current conditions, changing the political system to a parliamentary one is not feasible, and this matter requires field studies, further research, and dialogue with officials in this arena to prepare the ground for the possibility of such a change in the future.