This paper aims to investigate local communities’ participation in eco-tourism projects by using the community of Situ Cisanti located in Tarumajaya Village, West Java as a case. Data were gathered through observation, in-depth interviews, and documentation analysis. Observations and in-depth interviews were conducted simultaneously for two months, from September to October 2021. In-depth interviews were conducted with 15 informants from the elements; village government officials of Tarumajaya, Perhutani, and local communities who participated in the Situ Cisanti eco-tourism project, which was completed through a documents analysis. According to the findings, local community participation in Situ Cisanti eco-tourism consists of conservation and economic participation. Conservation participation is demonstrated by their participation in restoration and greening activities such as reforestation, etc. in Situ Cisanti and its surroundings, whereas economic participation is demonstrated by the establishment of stalls, culinary, coffee, souvenir, and homestay businesses as a result of Situ Cisanti eco-tourism. Furthermore, the existence of this eco-tourism has empowered women because new business opportunities that arise are not only run by men but also by women. As a result, this study implies that the participation of local eco-tourism communities not only has an impact on empowering conservation knowledge and economics, but it can also imply women empowerment.

In order to improve the quality and efficiency of heat treatment in welds of power stations, this paper summarizes the current situation of 600 MW supercritical power plant welding site heat treatment and puts forward the improved methods and measures accordingly. The heat treatment of welding holes in the construction site Play a certain guiding role.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

In this study, daily averages of air quality parameters were measured in two stations (S1 and S2) of the organized industrial district in Samsun. The meteorological variables were measured at only one station (S1), such as temperature, relative humidity, wind speed, solar radiation, and ambient pressure in 2007, and the daily promised limit for nitrogen dioxide has been especially exceeded at 206 times for 1^st station. However, exceeds of the limit value in 2006 for 1^st station was reduced by approximately 3.5 times. The daily nitrogen dioxide concentration did not exceed the daily limit of WHO^[1] as for 2^nd station. The results obtained showed that under the influence of dominant wind direction, the second station measurement results are higher than that of the first station. To determine all of the possible environmental effects, the measurements should be analyzed from a multi-point perspective.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.