درخت حوزه‌های تخصصی

Background: Digital twin (DT) technologies have become significant enablers of urban management, utilising real-time information, data analytics, and IoT connectivity to manage challenging urban issues. Nonetheless, existing studies reveal the capacity of the DTs, while their generalization, flexibility, and cross-disciplinary application for various urban environments are not thoroughly studied yet. Objective: This article aims to evaluate the effectiveness of DT technologies in improving traffic management, energy efficiency, infrastructure maintenance, and public safety across six case study cities: There are Singapore, Helsinki, Barcelona, Dubai, New York, and Tokyo. The study examines how DTs can be extended and implemented to target urban issues and how their use operational performance might be optimized. Methods: The study used quantitative data processing, on-line data analysis with factorization and machine learning, and assessment of the case studies. Quantitative measures which included traffic flow, energy loss, down time, and response to emergency situations were investigated pre and post DT application. The improvements mentioned were statistically confirmed, and the metrics of scalability and adaptability were evaluated in the course of the cities. Results: DT technologies increased traffic flow by up to 42.9%, reduced energy losses by 35%, minimum down time was 42%, emergency response was 44.9%. This was the case because the network had high IoT coverage and because DTs were applied to the context when it specifically needed them. Conclusion: The study proves that DTs can be implemented in different environments due to their flexibility to accommodate different urban conditions. AI and cross domain integration can add to the effectiveness of DT in general and both are inarguably now crucial for the management of contemporary urban environment.

Purpose: Neurotransmitters, emotional intelligence, and personality traits are the key factors that affect the behavior and decisions of individual investors. This study aimed to investigate the effects of neurotransmitters, emotional intelligence, and personality traits on the behavior and decisions of individual investors simultaneously, in the Tehran stock exchange. Design/methodology/approach: In terms of purpose, the current research was practical. In terms of method, it was a descriptive survey research. In terms of time, it was a cross-sectional research. The research data were collected through a questionnaire and convenience sampling method. The sample size was 212 individual investors. Composite reliability, average variance extracted, convergent validity and divergent validity were used to measure the validity and reliability of the questionnaire. Data were analyzed using structural equation modeling. Findings: This study includes 19 constructs (variables). These constructs include dopamine, serotonin, norepinephrine, and epinephrine (neurotransmitters); Self-appraisal of emotions, other’s emotion appraisal, use of emotion and regulation of emotion (emotional intelligence); extraversion, neuroticism, openness, conscientiousness, and agreeableness (personality traits); investment horizon, risk attitude, personalization of loss, confidence, and control (investors' behavior); and investors' decisions. The current study includes six main hypotheses and 78 sub-hypotheses. The research findings showed that neurotransmitters, emotional intelligence, and personality traits affect the investors' behavior and decisions. Originality/value: The findings of this study showed that the neurofinance and behavioral finance have a key role in decision making of policymakers and investors. Finally, the findings of this study would provide new horizons in neurofinance and behavioral finance.