Tata Consulting Engineers (Qatar)

Robotic technology has become a crucial tool in disaster response and recovery, offering innovative solutions to complex challenges faced by responders. This chapter provides an overview of robotics’ role in disaster management, emphasizing recent advancements, ongoing challenges, and future directions. Innovations include unmanned aerial vehicles, unmanned ground vehicles, underwater robotics, and wearable systems designed for reconnaissance, search and rescue, infrastructure inspection, debris clearance, and rehabilitation. These technologies improve disaster response by enhancing situational awareness, increasing speed and efficiency, and reducing risks to human lives. However, deploying robotics in disaster scenarios presents challenges such as limited battery life, communication issues, and adaptability to various environments. Ethical concerns about privacy, data security, and human–robot interaction also impact the adoption of these technologies. Additionally, regulatory and socioeconomic factors, along with the need for interdisciplinary collaboration, complicate the field. Future research should focus on overcoming these obstacles and seizing opportunities to advance robotics in disaster response. This includes improving autonomous systems, enhancing human–robot interaction, leveraging sensors, and ensuring ethical deployment. By addressing these challenges, we can maximize the potential of robotics to create safer and more resilient communities in the face of disasters.

Artificial intelligence (AI) is rapidly redefining financial risk management, particularly within the realm of parametric insurance an innovative model that issues payouts based on predefined triggers rather than traditional loss assessments.This paradigm shift demands a robust theoretical foundation to evaluate its economic implications, especially as automation, predictive modeling, and data analytics reshape actuarial methodologies.From a macroeconomic standpoint, AI-enhanced parametric insurance has the potential to stabilize economies vulnerable to climate risks, natural disasters, and systemic financial disruptions by ensuring rapid liquidity and minimizing administrative losses.At the microeconomic level, it reconfigures underwriting, claims processing, and premium pricing mechanisms through real-time data integration and machine learning algorithms.The theoretical framework proposed integrates elements of expected utility theory, information asymmetry, and risk transfer efficiency to capture the economic interplay between technological advancement and financial resilience.It positions AI not merely as a computational tool but as an institutional enabler that enhances transparency, reduces moral hazard, and improves market efficiency.Additionally, the framework incorporates system dynamics modeling to assess how digital infrastructure, regulatory adaptation, and behavioral economics jointly influence cost structures and adoption rates.By linking technological innovation to economic performance indicators such as loss ratios, payout speed, and capital efficiency this framework offers a comprehensive foundation for evaluating the financial sustainability of AI-driven insurance ecosystems.Ultimately, it provides a structured lens for policymakers, insurers, and investors to quantify both the direct and spillover economic impacts of AI-enhanced parametric insurance across diverse markets and risk domains.

Abstract The variability in responses generated by Large Language Models (LLMs) like OpenAI’s GPT-4 poses challenges in ensuring consistent accuracy on medical knowledge assessments, such as the United States Medical Licensing Exam (USMLE). This study introduces a novel multi-agent framework—referred to as a "Council of AIs"—to enhance LLM performance through collaborative decision-making. The Council consists of multiple GPT-4 instances that iteratively discuss and reach consensus on answers facilitated by a designated "Facilitator AI." This methodology was applied to 325 USMLE questions across Step 1, Step 2 Clinical Knowledge (CK), and Step 3 exams. The Council achieved consensus responses that were correct 97%, 93%, and 94% of the time for Step 1, Step 2CK, and Step 3, respectively, outperforming single-instance GPT-4 models. In cases where there wasn’t an initial unanimous response, the Council of AI deliberations achieved a consensus that was the correct answer 83% of the time. For questions that required deliberation, the Council corrected over half (53%) of responses that majority vote had gotten incorrect. At the end of deliberation, the Council often corrected majority responses that were initially incorrect: the odds of a majority voting response changing from incorrect to correct were 5 (95% CI: 1.1, 22.8) times higher than the odds of changing from correct to incorrect after discussion. We additionally characterized the semantic entropy of the response space for each question and found that deliberations impact entropy of the response space and steadily decrease it, consistently reaching an entropy of zero in all instances. This study showed that in a Council model response variability—often viewed as a limitation—could be leveraged as a strength, enabling adaptive reasoning and collaborative refinement of answers. These findings suggest new paradigms for AI implementation and reveal diversity of responses as a strength in collective decision-making even in medical question scenarios where there is a single correct response.Author SummaryIn our study, we explored how collaboration among multiple artificial intelligence (AI) systems could improve accuracy on medical licensing exams. While individual AI models like GPT-4 often produce varying answers to the same question—a challenge known as "response variability"—we designed a "Council of AIs" to turn this variability into a strength. The Council consists of several AI models working together, discussing their answers through an iterative process until they reach consensus.When tested on 325 medical exam questions, the Council achieved 97%, 93%, and 94% accuracy on the Step 1, Step 2CK, and Step 3, respectively. This improvement was most notable when answers required debate: in cases where initial responses disagreed, the collaborative process corrected errors 83% of the time. Our findings suggest that collective decision-making— even among AIs—can enhance accuracy and AI collaboration can potentially lead to more trustworthy tools for healthcare, where accuracy is critical. By demonstrating that diverse AI perspectives can refine answers, we challenge the notion that consistency alone defines a "good" AI. Instead, embracing variability through teamwork might unlock new possibilities for AI in medicine and beyond. This approach could inspire future systems where AIs and humans collaborate (e.g. on Councils with both humans and AIs), combining strengths to solve complex problems. While technical challenges remain, our work highlights a promising path toward more robust, adaptable AI solutions.

Wind turbines penetration for the electric grid were increase day by day and play a crucial role in the distribution bus systems. Much more attention has been provided for large wind turbine scale penetration, while on the other side the medium scale wind turbines required to be examined and tested with the distribution networks. Therefore, the examination of dynamic stability such as active and reactive power losses as well as voltage profiles within distributed power systems after raising up of the medium scale wind turbine generation should be considered. This paper studies effect on voltage stability is studied under different levels of medium-scale wind power penetrations in the distribution system without causing instability problems in the network. The simulation case studies are conducted using ETAP software, considering various scenarios of transient and steady-state voltage stabilities. This is to demonstrate the impact with and without wind sets to evaluate and study the voltage dynamic stability along with active and reactive power. The obtained results show in the case of low penetration level that it has a negligible effect on the voltage stability and the voltage profile is supported by the integrated wind turbines set into the IEEE 13 Bus distribution system at high penetration level.

This paper describes some of the various repair and conservation works carried out at the Tower of London, a World Heritage Site and Scheduled Ancient Monument, on behalf of Historic Royal Palaces. The works are generally managed on site by a surveyor from the Surveyor of the Fabric department and inspected by an Inspector of Ancient Monuments for Historic England, generally Jane Sidell.

Through the past few years, the last planner system (LPS) as one of the main important methods of lean implementation in the (AECO) industry proved to improve efficiency, performance, reliability, planning and coordination. LPS can be implemented in design projects as well as in construction projects. This research reports the implementation process of the LPS as a basis for continuous improvement in the design stage. The first part is about Lean Concept definition, values and wastes; challenges faced, and Lean implementation. Second part is focusing on the last planner system, its definition and method of implementation. Third part is a case study of how LPS is implemented in managing a design project in a design consultancy firm, how to lead the lean enterprise, and how to deal with change resistance within the team.

ABSTRACT This study presents two approaches for evaluating damage and recovery characteristics of asphalt mixtures. The first approach focuses on determining damage by extracting recoverable viscoelastic strain that has no damage and comparing it with the strain response of a material that has damage. The second approach is based on identifying the capacity of asphalt mixtures to recover some of the damage that may occur during creep loading. The advantage of these approaches is that they utilise experimental data to extract damage and recovery responses, without detailed mathematical modelling of material behaviour. The developed approaches are used to analyse the behaviours of fine aggregate mixtures (FAM) incorporating warm mix additives. The results show that these mixtures vary in their damage and recovery characteristics. For example, Sasobit FAM experienced the highest damage among mixtures, but it also had the highest recovery. Ageing was found to reduce the differences among mixtures in terms of resistance to damage, but it had less effect on the differences in their healing potential.

Upon the design of any pavement structure, three fundamental external design parameters have to be considered throughout the design process: the characteristics of the subgrade, traffic loads, and environmental conditions. This paper focuses on the main challenges being faced to estimate site-specific traffic loading for a pavement design due to the lack of accurate and recent traffic data. A case study for a pavement structural design process for an actual expressway project in the State of Qatar is discussed in this paper. This case study goes through traffic loading calculations inputs; the Average Daily Traffic (ADT), Truck Factor (TF), as well as Heavy Good Vehicle Percentage (HGV%). The challenges of calculating these site-specific traffic loading inputs are discussed through investigation of the accuracy in traffic loading estimation using Qatar Strategic Transport Model (QSTM), and how the model can be incorporated in the design. Besides, limited available Wight-In-Motion (WIM) surveys have been discussed and the paper ends with the conclusion that rich availability of traffic data enhances the accuracy of the pavement structural design. It affects significantly the value engineering practice, and results in sustainable pavement sections.