درخت حوزه‌های تخصصی

One of the biggest risks in the economies of countries is the bankruptcy of industries and companies. Predicting the bankruptcy of companies and industries provides the opportunity for the government, investors, and shareholders to take strategic actions to deal withcrises and prevent potential losses. From an economic perspective, financial distress can be interpreted as the company being loss-making, in which case the company has failed to succeed. In fact, in this case, the company's rate of return is less than the cost of capital. Another form of financial distress occurs when the company fails to comply with one or more of the debt covenant terms, such as maintaining the current ratio or the special value ratio to total assets according to the contract. Therefore, there is no general agreement on the definition of financial distress, and the reasons that lead companies towards failure are very different. One possible reason for failure is the wrong direction ofthe company, which can be analyzed through the financial ratios of the companies. Since the 1960s, many studies have only examined financial ratios as indicators of failure. Therefore, the analysis is the basis for improving and developing failureprediction models. In this study, the influential variables in predicting bankruptcy were first selected using backgrounds. The most important factors were identified with the opinions of experts, and the weight of each criterion was obtained using the AHP technique. Then, these criteria were ranked using the TOPSIStechnique to identify the most important factors. After completing these steps, by entering these variables into an artificial neural network of the multilayer perceptron type as input and using the backpropagation algorithm to train the network, the prediction accuracy increased, and accurate and reliable results were obtained

Objective: The present study aimed to explore the process of meaning formation in adolescents. Methods: The research method was qualitative and utilized a grounded theory approach. The research population consisted of adolescents aged 12–20 years in Isfahan, Iran. Sampling was conducted theoretically. Nine participants were included in the study, and data collection was carried out through semi-structured interviews. The data analysis process followed Glaser's (1987) methodology. At the end of the coding stages, the final model of the study was presented. Findings: The results revealed that meaning formation in adolescents occurs through five stages: the occurrence of conflict (a triggering event), expression of conflict (problematic coping with a painful and damaging context, emotional confusion, and protesting against meaning), confrontation with conflict (addressing unclear feelings, focusing on core pain (self-awareness), and developing acceptance capacity), processing conflict (recalling the experience, exploring, expanding and deepening, gaining insight into unmet needs, and processing and accepting one's and others' experiences), and the emergence of resolution and achieving integration (softening and adopting a new perspective, manifesting agency (decision-making), generating experience, and integrating with prior experiences). Conclusion: Based on the findings, it can be inferred that meaning formation in adolescents occurs as a process. Therefore, it can be concluded that the journey toward finding meaning is not a fixed or instantaneous event but a continuous and evolving process shaped by various experiences and reflections. This process unfolds as a series of interrelated stages, reflecting a dynamic and transformative approach to navigating and understanding significant life changes. This structured delineation of stages provides valuable insights into how individuals actively engage with and create meaning when encountering transformative moments.

Energy, besides other factors, production is considered the main factor in growth and economic development, and economics can play beneficial roles in the performance of different sectors. Hence, the country authorities should try to predict anything more precisely regarding energy consumption in the proper planning and guidance of consumption to control the way they desire energy demand and supply parameters. This paper aims to evaluate the hybrid model of artificial neural networks and genetic algorithms in forecasting demand energy to predict energy consumption in the country. A case study is energy consumption in Iran's transportation sector. So, for this review, we use the annual data on energy consumption of transport as a variable output of forecast models and data from the entire country's annual population, GDP, and the number of vehicles as the input variables. Evaluation results showed that themodel ofArtificial hybrid model of Neural Networks (ANN) and Genetic Algorithm (GA), campared toother models, has the highest accuracy in predicting energy demand in the transportation sector

Artificial intelligence technology has become an influential and trending topic in architectural layout design. The core technology of AI, machine learning, has attracted the attention of architects as a decision-making tool. The focus of many studies that apply machine learning to layout design is using the generative adversarial network (GAN within a given boundary. Previous research demonstrates that training a GAN with labels can help a computer understand how spatial elements relate and the logical relationship between spatial elements and boundaries. However, this paper applied conditional GAN to generate space layouts with given boundaries and supplementary conditions. The supplementary conditions provide designers control over the generated layout plans by satisfying input boundary and user requirements. It also allows designers to generate different layout plans within the same boundary. To achieve this, a method for dividing image channels is proposed so that both given boundaries and supplementary conditions become the model's input. The dataset consists of 660 apartment plans in Hamadan. The dataset is split into a training set and a test set. The training set includes 594 images, and the test set includes 66 (10%) of the images. After training the model with the training set, the model is tested using the test set. Finally, the model outputs are evaluated based on quantitative and qualitative methods. The results show that the supplementary conditions provide further guidance to the model for space layout generation based on user preferences and reduce the image quality problems of the synthetic images.