Integrating rooftop wind turbines (RWTs) into urban environments presents a substantial opportunity for decentralized renewable energy. Still, it is hindered by the intricate aerodynamic interactions induced by urban structures. This investigation establishes an AI-assisted framework for planning the placement of RWTs in urban environments. The MiDaS depth estimation model is employed to convert 2-D satellite imagery into a detailed 3-D terrain model, which is necessary to analyze wind flow accurately. This process captures the geometries of buildings. Machine learning techniques are employed to analyze and generate wind data from NASA POWER at different hub heights, enabling accurate wind speed prediction. The prevailing wind patterns are identified through wind speed distributions and directional analyses. At the same time, the velocity deficits and disturbances caused by urban obstacles are quantified using an updated Jensen multiwake model. The proposed methodology has been applied and validated in two Indian cities, Barmer and Jaipur, Rajasthan, India. The results indicate that there were substantial power losses and reductions in wind speed. Building-induced wakes, modeled via the Jensen multiwake approach, reduced turbine output and caused significant seasonal fluctuations in capacity factors: Barmer—12.24% loss (2370→2080 kWh); Jaipur—17.53% loss (1540→1270 kWh). Comparative analyses underscore the critical impact of urban morphology on RWT performance. The proposed methodology is a data-driven, robust instrument for optimizing turbine placement, intending to improve the efficiency and reliability of urban wind energy systems. Consequently, it contributes to sustainable urban energy planning and decentralized power generation.

Querying source code is an essential aspect of a variety of software engineering tasks such as program understanding, reverse engineering, program structure analysis and program flow analysis. In this paper, we present and demonstrate the use of an algebraic source code query technique that blends expressive power with query compactness. The query framework of Source Code Algebra (SCA) permits users to express complex source code queries and views as algebraic expressions. Queries are expressed on an extensible, object-oriented database that stores program source code. The SCA algebraic approach offers multiple benefits such as an applicative query language, high expressive power, seamless handling of structural and flow information, clean formalism and potential for query optimization. We present a case study where SCA expressions are used to query a program in terms of program organization, resource flow, control flow, metrics and syntactic structure. Our experience with an SCA-based prototype query processor indicates that an algebraic approach to source code queries combines the benefits of expressive power and compact query formulation.

Querying and analyzing source code interactively is a critical task in reverse engineering and program understanding. Current source code query systems lack sufficient formalism and offer limited query capabilities. We introduce the formal framework of Source Code Algebra (SCA), and outline a source code query system based on it. SCA provides a formal data model for source code, an algebraic expression-based query language, and opportunities for query optimization. An algebraic model of source code addresses the issues of conceptual integrity, expressive power, and performance of a source code query system within a unified framework.>

Querying source code interactively for information is a critical task in reverse engineering of software. However, current source code query systems succeed in handling only small subsets of the wide range of queries possible on code, trading generality and expressive power for ease of implementation and practicality. We attribute this to the absence of clean formalisms for modeling and querying source code. In this paper, we present an algebraic framework (Source Code Algebra or SCA) that forms the basis of our source code query system. The benefits of using SCA include the integration of structural and flow information into a single source code data model, the ability to process high-level source code queries (command-line, graphical, relational, or pattern-based) by expressing them as equivalent SCA expressions, the use of SCA itself as a powerful low-level source code query language, and opportunities for query optimization. We present the SCA’s data model and operators and show that a variety of source code queries can be easily expressed using them. An algebraic model of source code addresses the issues of conceptual integrity, expressive power, and performance of a source code query system within a unified framework.

For maintainers involved in understanding and reengineering large software, locating source code fragments that match certain patterns is a critical task. Existing solutions to the problem are few, and they either involve manual, painstaking scans of the source code using tools based on regular expressions, or the use of large, integrated software engineering environments that include simple pattern-based query processors in their toolkits. We present a framework in which pattern languages are used to specify interesting code features. The pattern languages are derived by extending the source programming language with pattern-matching symbols. We describe SCRUPLE, a finite state machine-based source code search tool, that efficiently implements this framework. We also present experimental performance results obtained from a SCRUPLE prototype, and the user interface of a source code browser built on top of SCRUPLE.>

The complex nature of two-dimensional image data has presented problems for traditional information systems designed strictly for alphanumeric data. Systems aimed at effectively managing image data have generally approached the problem from two different views: They either possess a strong database component with little image understanding, or they serve as an image repository for computer vision applications, with little emphasis on the image retrieval process. A general architecture for visual information-management systems (VIMS), which combine the strengths of both approaches, is presented. The system utilizes computer vision routines for both insertion and retrieval and allows easy query-by-example specifications. The vision routines are used to segment and evaluate objects based on domain-knowledge describing the objects and their attributes. The vision system can then assign feature values to be used for similarity-measures and image retrieval. A VIMS developed for face-image retrieval is presented to demonstrate these ideas.>