Mahindra Group (India)

Abstract We present the habitability and stability of the proposed planets around the young stellar system HD 21841 based on the estimated astrophysical parameters of its components. Our study utilizes an orbital analysis of the system, employing Tokovinin's Method and incorporating 12 new speckle interferometric measurements that collectively span approximately 90 degrees of the orbit. Additionally, we use Al-Wardat's method for analyzing binary and multiple stellar systems in conjunction with Gaia DR3 astrometry and spectral data for a comprehensive spectrophotometric assessment of the components. This approach utilizes Kurucz’s model stellar atmospheres and available photometric data to develop synthetic spectral energy distributions (SEDs) for each component and subsequently the entire system. We can identify the physical parameters and the optimal solution by comparing the synthetic SED of the entire system with the observational data from Gaia. The analysis successfully determined the stellar parameters of the system for the first time, revealing the following: $ T_{\rm eff.}=5945\pm80\,K$, $R=1.018\pm0.05\, R_\odot$ , $\mathcal{M}=1.12\pm0.10\, \mathcal{M_\odot}$ for the primary component and $T_{\rm eff.}=5700\pm80\,K$, $R=0.975\pm0.06\, R_\odot$ , $\mathcal{M}=1.05\pm0.09\, \mathcal{M_\odot}$ for the secondary component. Both components are found to have solar metallicity and are approximately 1 billion years old. The combined spectrophotometric and dynamical analysis resulted in precise stellar masses and a new dynamical parallax of $23.756\pm0.001$ mas, consistent with the Hipparcos 1997 measurement ($23.76\pm1.07$) and intermediate between that of Gaia DR2 ($23.51\pm0.28$) and Gaia DR3 ($24.31\pm0.19$). In addition to studying the habitability and stability of the exoplanets within the system, we explored the processes of its formation and evolution.

The growing urgency of climate action at the city level has led to an exponential rise in documents that describe a city’s policy, action plan, or progress towards climate action. The increased number of documents has made it increasingly difficult for governments to track commitments and compare approaches across jurisdictions. These documents are essential for informed decision-making, but extracting useful information from unstructured PDF reports remains a largely manual, resource-intensive, and inconsistent process. Recent advances in AI and large language model (LLM) based document understanding offer strong potential, but their application in urban climate governance workflows is still limited. Integrating AI-driven document analysis into this workflow offers opportunity for building scalable, standardized, and transparent climate policy assessment.This study presents an AI-assisted natural language processing (NLP) pipeline that automatically extracts, segments, and classifies climate actions from diverse policy documents. The workflow integrates layout-aware text extraction with an action-segmentation mechanism to identify action statements across heterogeneous formats. A fine-tuned, two-stage ClimateBERT classifier then categorizes actions: Stage 1 differentiates mitigation and adaptation measures (F1 = 93%), while Stage 2 assigns domain-specific sub-categories, achieving 92% F1 for mitigation and 91% for adaptation. An equity-detection module further identifies references to vulnerable groups, inclusivity, and justice-oriented themes.The pipeline significantly reduces manual review effort and enhances consistency in understanding climate action. By enabling standardized comparisons, the approach directly supports mayors, policymakers, and urban practitioners in evaluating progress and designing more effective and equitable interventions.As AI capabilities advance, such automated tools will strengthen climate governance by improving the accessibility, reliability, and strategic value of climate policy data.

With the growing reliance on DC microgrids (DC MGs) in critical infrastructure, securing them against sophisticated cyberattacks is essential. This study presents a hybrid cyber-defense (HCD) framework that detects and mitigates false data injection (FDI) and replay attacks through a combination of bidirectional LSTM (Bi-LSTM) autoencoders for anomaly detection, cross-correlation analysis for replay attack identification, and a physics-informed neural network (PINN) for adaptive control. A Kalman filter-based estimator maintains control stability when sensor measurements are compromised. The framework operates in a fully decentralized manner at the local controller level, enabling fast, low-latency responses. Validation on a 4-bus MATLAB/Simulink model and a 3-bus DC MG experimental testbed demonstrates over 95% detection accuracy and effective mitigation, preserving voltage stability under attack. The proposed control algorithm is deployed in the AT19SAM3X8E microcontroller, acting as the local controller at each node, occupying 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$KB$</tex-math></inline-formula> of memory with an execution time of 0.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ms$</tex-math></inline-formula>. This real-time deployment confirms practical applicability for lightweight, standalone operation. These results demonstrate the proposed method's robustness and scalability, advancing intelligent, real-time cyber-resilience for future DC MG.

This chapter describes the current state of the literature on the role of gamification in engaging a diverse workforce. To do this, the author did a systematic literature review of 13 research papers, the documents of which were extracted from the Scopus database. Themes that form the basic structure of each of these book chapters such as challenges in and significance of gamification in engaging diverse workforce were extracted using thematic analysis. Two real-world primary cases on how taking care of workforce diversity in designing a game is critical for driving engagement in learning at work have also been included toward the end of this chapter.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range&lt;/div&gt;&lt;/div&gt;

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The customer perception of ride comfort with vehicle performance is the most important aspect in a vehicle design. The ride comfort and vehicle performance are influenced by driveline components i.e. propeller shaft phase angle, inclination angle and critical frequency of the driveline system. The optimization of the driveline system is essential to ensure the efficient and smooth power transfer. Propeller shaft is one of the critical components in the driveline to influence the vehicle performance.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Propeller shaft characteristics influenced by several factors like vehicle max torque, propeller shaft joint type, materials properties, UJ phase and inclination angle and shaft unbalance value. The optimization of the above parameter within the tolerance limit enables to meet the required performance standard. Various methodologies are available to optimize these parameters to enhance the vehicle performance and comfort leads to customer satisfactions. This study focuses on the analytical optimization of the propeller shaft’s universal joint (UJ) phase and inclination angle and validated at vehicle level.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;An analytical model was employed to evaluate the velocity fluctuations across the full rotation of the propeller shaft by considering the different UJ phase angles (0&lt;sup&gt;°&lt;/sup&gt; − 360&lt;sup&gt;°&lt;/sup&gt;) and inclination angles (1&lt;sup&gt;°&lt;/sup&gt; − 5&lt;sup&gt;°&lt;/sup&gt;). It was observed from the study that optimization of these parameter improves the vibration performance and decreases the velocity fluctuations. The results shows that the well optimized propeller shaft will enhance smoothness in the driveline systems, reduction in NVH levels, vehicle performance and ride comfort. This study suggests the importance of the precise geometry alignment in driveline design and provide the further refinement methodology.&lt;/div&gt;&lt;/div&gt;

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In last two decades, Farm customer expectation on cabin comfort has been increased multifold. To provide the best-in-class customer experience in terms of comfort without adding cost and weight is bigger challenge for all NVH Engineers. It is evident from literature survey that cabin tractors with better comfort is well accepted by customers in US and European Market.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Apart from engine excitation, customer has become more sensitive to customer-actuated-accessory noises due to overall reduction in cabin noise in last 2 decades. This paper presents the study conducted on HVAC blower noise in 30HP cabin tractor. Tactile vibrations and cabin noise is not acceptable when AC is switched on due to low frequency modulating nature in frequency range of ~65Hz and 130Hz. The investigation is carried out systematically considering each component of Source-Path-Receiver model. HVAC blower unit as source is diagnosed in detail to understand root cause. Strong dominance of first order of blower been observed on tactile vibrations and cabin noise. Blower unbalance is identified major cause of excitation. Effect of blower rpm on tactile vibration and boom noise is studied. Structural transfer paths were investigated in terms of stiffness &amp;amp; damping of radiating panels. Use of isolation strategy for blower is also explored for reduction in OEL (Operator Ear Level) noise. The damping of blower housing casing has shown contribution of 2-3 dB (A) at OEL noise. Both improvement in reduction of blower 1st order excitation and isolation strategy has shown to reduce low idle cabin noise by 10-15 dB (A) at 63Hz while operating at blower speed-3. Design NVH contenting modal frequency criteria for transfer paths, selection of blower speed is discussed to avoid similar issues in future.&lt;/div&gt;&lt;/div&gt;

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Quieter cabins in an automobile are the new era, they provide customers with pleasurable driving experience. Squeak and Rattle are spoil sport for any OEM that aim to improvise customer driving experience. Their nonlinear nature makes it difficult to formulate design frontloading methods. The issue of seals rubbing against the body &amp;amp; door interface is a clear sign of seal squeak &amp;amp; seal chucking. Seals are applied with anti-friction coatings to avoid stick slip phenomena between EPDM and painted panel. Primary root cause for seal squeak is coating erosion. The challenge lies in determining whether the body or the closure side contributes to the seal issue. This paper presents a distinctive approach for identifying the seal squeaking noise and enriches on the new modelling methods for seal interaction with door and body interfaces using FE software.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The proposed method was able to highlight the locations along the door-body interface for squeak noise. The approach for reducing the relative displacements was tackled by checking the contributing modes at every interface. A simulation-driven approach of “Panel Contribution Analysis” (PCA) has been introduced analyzing the contribution of several BIW &amp;amp; Closure panels. The optimized panel thicknesses minimized the displacement and reduced the likelihood of squeaking and chucking. The key highlight is a method of DOE followed to minimize experimental runs with a weighted optimization of panel thickness. This method enables engineers to achieve the ideal trade-off between structural integrity, weight, cost, and performance.&lt;/div&gt;&lt;/div&gt;