Abstract

Effective drafting of construction contract conditions is essential for minimizing disputes and ensuring efficient project delivery. While prior studies have explored methods for selecting suitable contract forms, limited attention has been given to comparing multiple forms to identify missing clauses. This research proposes an integrated NLP-based framework that supports both contract form selection and completeness assessment. Guided topic modeling is used to evaluate major contractual criteria from literature across different standard forms, user-defined priorities are incorporated through AHP and TOPSIS to determine the most suitable contract conditions form. A pretrained Legal-BERT model is applied to compare the chosen form against others, detecting missing clauses through semantic similarity analysis. Multiple similarity measures, including Cosine, Manhattan, and Jaccard, are tested, with sensitivity analysis to identify optimal thresholds. Results show that Cosine similarity achieves the highest performance, with an F1 score of 91.8%, demonstrating the framework’s effectiveness in enhancing automated contract conditions assessment.

 Biography

Mohab Nasser is an Assistant Lecturer in Construction Engineering and Management at Cairo University and a practicing contracts engineer specializing in contract analysis, cost estimation, and procurement evaluation. His research focuses on applying artificial intelligence and natural language processing to construction contract analysis and decision-making. He has publications mainly on automated contract clause classification and AI-supported selection of construction contract types.

Abstract

This work presents an alternative method for the inverse kinematics problem when serial chain industrial robots are considered. Such method is based on an iterative search algorithm that solves the inverse kinematics by allowing that only one joint to change the robot movement at a time, in a simulation stage. This transforms the n-dimensional problem in simpler one-dimensional problems, which analytical solution for each joint is presented employing the Denavit-Hartenberg convention. The considered method does not possess internal singularities. Furthermore, due to its simplicity, it is shown experimental results obtained employing a 3-Degrees-of-Freedom redundant manipulator.

Biography

Breno S. Nucci holds a degree in Industrial Mechatronics Technology from the Faculty of Technology of Mogi Mirim “Arthur de Azevedo” (2022), a Master’s degree in Electrical Engineering from the State University of Campinas (UNICAMP) (2024), and is currently pursuing a PhD in Electrical Engineering at the same institution. He is a member of the research group at the Laboratory of Modular Robotic Systems (LSMR), affiliated with the School of Electrical and Computer Engineering at UNICAMP, with experience in electrical engineering and a focus on industrial robotics, working mainly in the areas of robotics, nonlinear systems control, neural networks, mobile robotics, trajectory control, and robotic joint control.

Abstract

As systems become increasingly interconnected, traditional cybersecurity threat identification strategies often fail to defend against nefarious actors and their sophisticated attacks. Given the current internet protocols (IP) meant to protect against email infiltrations such as Domain-based Message Authentication, Reporting and Conformance (DMARC), DomainKeys Identified Mail (DKIM), and Sender Policy Framework (SPF), a more comprehensive security analysis is desired for early threat identification. These protocols are intended to address specific email-based threats, however; the threat landscape being remediated is not typically incorporated into system conceptual or logical models. This presentation demonstrates a model-based systems engineering (MBSE) approach for threat modeling that leverages the ISO 21434 – “Road vehicles – Cybersecurity Engineering” additions to the Systems Modeling Language (SysML) profile. Capabilities of the extension include the ability to represent threats, attack trees, and hazards for further analysis. These elements are linked to structural and behavioral elements of the system architecture to maintain traceability throughout the lifecycle. Integrating a cybersecurity perspective into system structural and behavioral models promises to improve security posture of the overall architecture. There is an increase in visibility of threats to IPs by integrating security threats into a system model, increasing transparency among affected team members. Benefits to the audience include an enhanced understanding of email protocols, how they are shown in a system model, current analysis techniques, and threat mitigation methods.

Biographies

Sarah Rudder is a systems engineer and ontologist for Enola Technologies and a current PhD candidate at Colorado State University specializing in model-based systems engineering tools, languages, and approaches.