MIT Academy of Engineering

Industrial wastewater pollution arising from organic dyes and toxic heavy metal ions represents a serious environmental and public health challenge, demanding sustainable and multifunctional remediation strategies. In this study, NiO-SnO2 nanocomposites (NCs) were synthesized via an eco-friendly green solution combustion route using ground nut powder as a bio-derived fuel and systematically explored for environmental and luminescent applications. The resulting materials were comprehensively characterized using XRD, FT-IR, UV-DRS, SEM, TEM, and PL techniques, confirming their crystalline heterostructure, nanoscale morphology, and visible light activity. Among the different compositions, the 1:0.5 NiO-SnO2 NCs sample demonstrated superior photocatalytic performance toward visible-light-driven Rhodamine B (RdB) dye degrading and hexavalent chromium (Cr(VI)) reduction, governed by the surface charge near the point of zero charge (pHPZC=8.35). The NiO-SnO2 NCs exhibited excellent reusability across multiple cycles. Beyond, these NCs showed strong photo-luminescence and high colour purity, enabling their application in latent fingerprint detection. This dual functionality illustrates the potential of NiO-SnO2 NCs as eco-friendly, multifunctional materials suitable for both environmental and forensic applications, contributing to sustainable waste management and advanced biometric identification.

The behavior of pile foundations under combined vertical and lateral loads and moments was investigated. Lateral loads originated from traffic, seismic events, wind, vessel wakes, and earth pressure. Moments resulted from the eccentricity of vertical loads, column-pile fixity, and the resultant lateral load. To prevent buckling instability in specific scenarios, a minimum pile diameter was required based on the unsupported length above the point of fixity. A review of existing methods for analyzing laterally loaded single piles to determine the depth of fixity revealed conflicting positions among them. To address this discrepancy, depth of fixity values derived from finite element analysis were used as a benchmark for verification against these established methods. The analysis demonstrated that the computed depth of fixity varied significantly depending on the method applied. Although a finite element approach was proposed to determine an appropriate depth of fixity, the depths calculated using coefficient-based methods showed relatively good agreement with the finite element results.

We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of α-Fe close to experimental values.

Thyroid-related disorders are increasingly common and are often identified only after noticeable symptoms appear, which delays timely intervention. This highlights the importance of developing simple and accessible screening approaches. Conventional diagnostic methods, including ultrasound imaging and cytological evaluation is reliable but requires specialized infrastructure and expert interpretation, limiting their use in large-scale or early screening scenarios. This study explores infrared thermography as a non-invasive and radiation-free technique for preliminary thyroid screening. A computationally efficient machine learning framework is developed with a focus on real-time applicability. Initially, thermal images are processed to localize the thyroid region using a U-Net-based segmentation approach. The segmentation model achieves a dice coefficient of 0.91 and Intersection over Union (IoU) of 0.87, ensuring accurate region extraction. Subsequently, texture features representing thermal distribution patterns are derived using GLCM. These features are then classified using a lightweight ANN designed to balance performance and computational efficiency. Experimental evaluation on both public and self-acquired datasets demonstrates a classification accuracy of 94.11% with low inference latency. The findings indicate that thermography combined with feature-based learning can serve as an effective tool for early-stage screening and decision support, particularly in resource-constrained environments. However, the proposed framework is intended to complement, rather than replace, conventional diagnostic procedures.

An electric vehicle (EV) battery provides the energy required to power vehicles, ranging from passenger cars to buses. Among EV components, the battery is widely regarded as one of the most critical, as it directly supports vehicle operation. In parallel, the adoption of electric vehicles has increased, largely due to their potential to reduce carbon emissions and contribute to environmental protection initiatives. Consequently, selecting an appropriate battery for electric vehicles represents a significant decision-making challenge. To identify a suitable battery option, a multi-criteria decision-making (MCDM) approach is employed, taking into account several fundamental performance specifications. These criteria include battery lifespan, efficiency, durability, recharge time, temperature dependence, cost, and weight. The present work applies a structured framework for EV battery selection through the use of multiple decision-making techniques, namely the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the Complex Proportional Assessment (COPRAS) method, and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). Collectively, these methods are used to evaluate and rank battery alternatives, thereby providing a systematic and comparative basis for informed decision-making.