Tata Consultancy Services (India)

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising materials for electronics, energy storage, and solar energy systems. Chemical vapor deposition (CVD) offers a scalable bottom-up route for synthesizing high-quality TMD films; however, optimization of operating conditions is challenging due to strongly coupled mass, momentum, and heat transfer phenomena. To address these complexities, a detailed CFD model incorporating transport equations and reaction kinetics is developed and validated by using published experimental data. A systematic design of experiments is then applied to generate a data set linking the operating temperature, pressure, and reactant flow rates to key film characteristics, including the deposition rate and uniformity. Surrogate machine learning models have been developed to reduce the computational cost associated with CFD simulations. Among several algorithms evaluated, Extreme Gradient Boosting (XGBoost) provides the highest predictive accuracy for MoS2 film growth behavior. Model interpretability is further enhanced using SHapley Additive exPlanations (SHAP), which reveal how reactor variables interact and contribute to deposition outcomes. Finally, the XGBoost model is integrated with the nondominated sorting genetic algorithm II (NSGA-II) to simultaneously maximize film thickness and uniformity in a cold-wall horizontal CVD reactor. The resulting optimal operating windows are visualized through parallel coordinate plots, providing practical guidelines for improving MoS2 film quality and enhancing CVD process efficiency.

The advent of quantum computing poses significant challenges to classical cryptography and is a threat to secure communication protocols. This has created an urgent need to migrate networked applications to post-quantum cryptography (PQC). Enterprises operating large-scale networks must migrate thousands of applications that rely on cryptographic primitives, each requiring distinct remediation strategies and varying effort. Applications with higher risk profiles must be prioritized. However, budget limitations, skilled-resource availability, and application dependencies make PQC migration planning a challenging task. In this paper, we propose an optimization model that generates a migration plan for enterprise applications, determining which applications should be migrated in each time frame and in what order, such that the overall quantum risk exposure is minimal. We demonstrate the implications of delayed application migration on overall effort estimation and quantum risk exposure as well as the impact on application prioritization due to budget limitations and resource availability.

Generative Pre-trained Transformers (GPT) has ushered in a transformative shift in the landscape of automated text generation. These models use artificial neural networks to produce responses as per human prompts. These GPTs are now widely used in many NLP tools such as ChatGPT, Gemini, Pi, and Perplexity. As these systems become more common in academic and knowledge-based work, it is important to carefully examine the authenticity and reliability of their outputs. This study investigates the text-generation capabilities of these GPT-based tools by employing the cosine similarity index to compare their outputs against ground truth definitions sourced from the Britannica Encyclopedia. Definitions of selected concepts in science and social science were analyzed to ascertain the alignment of GPT-generated texts with established knowledge. The results show clear differences in how closely the four GPT-based systems matched Britannica definitions. Perplexity achieved the highest similarity with the least variability, followed closely by Pi and ChatGPT, while Gemini consistently produced the lowest similarity scores. The analysis of variance confirmed the statistical significance of these differences. The post hoc testing showed that Gemini formed a separate group due to its lower alignment with ground-truth definitions. These findings highlight that although GPT tools provide reasonably accurate information, their outputs still vary from ground truths. Overall, Perplexity Pi, and ChatGPT were closer to ground truth than Gemini, indicating the need for further improvement in Gemini’s performance.

Trauma-related perceptual experiences may resemble psychotic symptoms, creating diagnostic challenges in adolescent populations. We report the case of a Filipino preadolescent female diagnosed with posttraumatic stress disorder (PTSD) and major depressive disorder following prolonged intrafamilial sexual abuse. During trauma-focused psychotherapy incorporating expressive techniques, the patient consistently depicted and occasionally perceived a non-vivid, dark, cloud-like visual phenomenon that appeared exclusively during states of emotional calm, safety, or happiness. The experience was transient, purely visual, and associated with a subjective sense of peace. Importantly, the patient demonstrated intact insight, preserved reality testing, absence of distress, and no functional impairment related to the phenomenon. Comprehensive phenomenological assessment and differential diagnosis ruled out psychotic disorder, neurological causes, and mood-congruent psychosis. The visual experience was conceptualized as a non-psychotic, trauma-related perceptual phenomenon. This case highlights the importance of contextual, emotional, and insight-based assessment of perceptual experiences in traumatized adolescents to prevent misdiagnosis and inappropriate treatment.

Agriculture Technology (AgTech) integrates various technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, the Internet of Things (IoT), cloud and edge computing are applied to provide capabilities for collecting, transmitting, storing, processing, and analyzing agricultural data. Large amounts of data are gathered from multiple sources and processed in real time, enabling more effective decision-making. Data can be used to automate processes, resulting in savings on farm labor. To obtain the benefits of AgTech, data trustworthiness is essential and cyber resilience is required. Security flaws may result in farming equipment and processes being interrupted or inoperable, with significant revenue and capital losses. It is important for farmers to identify and respond to cyber incidents. Several existing works have attempted to classify AgTech technologies. However, most existing classifications are based on a limited set of characteristics and only focus on communication technologies in use or provide generic adversarial scenarios. There are real risks to data trustworthiness that impact on digital agriculture, and many of these risks are yet to be addressed. It is critical to understand the building blocks and possible components to be considered in a future security framework and their capabilities for satisfying the data confidentiality, quality, authenticity, and integrity requirements. This paper proposes a taxonomy to enable a consistent means of classifying the technologies that form the backbone of AgTech and outline the building blocks of data trustworthiness. This taxonomy consists of seven main criteria that recognize the technologies and systems (used to enable the movement of data at different stages) in terms of how it operates, its features, its benefits, and its limitations. Understanding the taxonomy enables effective implementation of anomaly detection and cryptographic methods to ensure data trustworthiness. Efficient AgTech with no security represents a serious risk to long-term sustainable smart farming and food security. Further, this paper outlines the integration of post-quantum cryptography with AgTech. The usefulness of the proposed taxonomy is demonstrated using two different types of case study related to the design and implementation of a modular security framework in real-world scenarios. The paper also identifies and discusses other important factors that impact data trustworthiness.

The urgent need to properly manage thermal conditions of supercomputing systems has become increasingly important because of their rising complexity and performance levels. The three standard cooling techniques that use air cooling fans alongside liquid circuits along with elaborate refrigerant systems prevail yet fail to strike equilibrium between environmental sustainability and operational performance. The deployment of such conventional cooling methods consumes vast amounts of power and results in substantial operational expenses alongside environmental deterioration. A meaningful change has occurred through implementing nature-based cooling solutions during the past few years. Thermal load reduction and energy reduction become achievable through innovative utilization of environmental resources including cold air from outdoor temperatures, ocean water, underground temperatures and glacial environments by organizations today. Several real-life examples prove natural cooling methods effective for large-scale high-performance computing operations: Microsoft uses underwater facilities while Facebook operates in Arctic conditions and Google accesses ocean water for cooling. The study evaluates heat management evolution by studying standard cooling practices against nature-driven heat removal solutions. The research explores ways to use natural elements for improving thermal efficiency while simultaneously reducing operational costs toward creating sustainable computing systems. The paper demonstrates through quantitative assessment and concrete environmental applications that natural elements are effective solutions for supercomputing heat management.