In light of swift urbanization and the lack of precise land use maps in urban regions, comprehending land use patterns becomes vital for efficient planning and promoting sustainable development. The objective of this study is to assess the land use pattern in order to catalyze sustainable township development in the study area. The procedure adopted involved acquiring the cadastral layout plan of the study area, scanning, and digitizing it. Additionally, satellite imagery of the area was obtained, and both the cadastral plan and satellite imagery were geo-referenced and digitized using ArcGIS 9.2 software. These processes resulted in reasonable accuracy, with a root mean square (RMS) error of 0.002 inches, surpassing the standard of 0.004 inches. The digitized cadastral plan and satellite imagery were overlaid to produce a layered digital map of the area. A social survey of the area was conducted to identify the specific use of individual plots. Furthermore, a relational database system was created in ArcCatalog to facilitate data management and querying. The research findings demonstrated the approach's effectiveness in enabling queries for the use of any particular plot, making it adaptable to a wide range of inquiries. Notably, the study revealed the diverse purposes for which different plots were utilized, including residential, commercial, educational, and lodging. An essential aspect of land use mapping is identifying areas prone to risks and hazards, such as rising sea levels, flooding, drought, and fire. The research contributes to sustainable township development by pinpointing these vulnerable zones and providing valuable insights for urban planning and risk mitigation strategies. This is a valuable resource for urban planners, policymakers, and stakeholders, enabling them to make informed decisions to optimize land use and promote sustainable development in the study area.

The main purpose of this study is to investigate the effect of environmental transformational leadership on organizational citizenship behavior through the mediating role of perceived meaningful work in Tehran District 22 Municipality. The study population in this study is the employees of the municipal district of District 22 of Tehran. The number is about 400 people, and the sample size was obtained according to Cochran’s 196 formula. The research method in the present study is applied in terms of purpose and descriptive in terms of implementation method. The Kolmogorov-Smirnov test was used to test the normality of the data, which proved with 95% confidence that the variables had an abnormal distribution. Therefore, due to the abnormality of the data distribution, Pls software was used to analyze the data. The results showed that environmental transformational leadership has an effect on organizational citizenship behaviors.

This paper discusses the dawn of cognitive neuroscience in management and organizational research. The study does that in two tiers: first, it reviews the interdisciplinary field of organizational cognitive neuroscience, and second, it analyzes the role organizational cognitive neuroscience (OCN) could play in reducing counterproductive workplace behaviors (CWB). Theoretically, the literature has established the benefits of a neuro-scientific approach to understanding various organizational behaviors, but no research has been done on using organizational neuroscience techniques to study counterproductive work behaviors. This paper, however, has taken the first step towards this research avenue. The study will shed light on this interdisciplinary field of organizational cognitive neuroscience (OCN) and the benefits that organizations can reap from it with respect to understanding employee behavior. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of cognition in counterproductive work behaviors, also by using neuroscience techniques. The study concludes by providing evidence drawn from the literature in favor of adopting an OCN approach in organizations.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.