In many real-world scenarios, such as gas leak detection or environmental pollutant tracking, solving the Inverse Source Localization and Characterization problem involves navigating complex, dynamic fields with sparse and noisy observations. Traditional methods face significant challenges, including partial observability, temporal and spatial dynamics, out-of-distribution generalization, and reward sparsity. To address these issues, we propose a hierarchical framework that integrates Bayesian inference and reinforcement learning. The framework leverages an attention-enhanced particle filtering mechanism for efficient and accurate belief updates, and incorporates two complementary execution strategies: Attention Particle Filtering Planning and Attention Particle Filtering Reinforcement Learning. These approaches optimize exploration and adaptation under uncertainty. Theoretical analysis proves the convergence of the attention-enhanced particle filter, while extensive experiments across diverse scenarios validate the framework's superior accuracy, adaptability, and computational efficiency. Our results highlight the framework's potential for broad applications in dynamic field estimation tasks.

The novel neural networks show great potential in solving partial differential equations. For single-phase flow problems in subsurface porous media with high-contrast coefficients, the key is to develop neural operators with accurate reconstruction capability and strict adherence to physical laws. In this study, we proposed a hybrid two-stage framework that uses multiscale basis functions and physics-guided deep learning to solve the Darcy flow problem in high-contrast fractured porous media. In the first stage, a data-driven model is used to reconstruct the multiscale basis function based on the permeability field to achieve effective dimensionality reduction while preserving the necessary multiscale features. In the second stage, the physics-informed neural network, together with Transformer-based global information extractor is used to reconstruct the pressure field by integrating the physical constraints derived from the Darcy equation, ensuring consistency with the physical laws of the real world. The model was evaluated on datasets with different combinations of permeability and basis functions and performed well in terms of reconstruction accuracy. Specifically, the framework achieves R2 values above 0.9 in terms of basis function fitting and pressure reconstruction, and the residual indicator is on the order of $1\times 10^{-4}$. These results validate the ability of the proposed framework to achieve accurate reconstruction while maintaining physical consistency.

Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate predictions but are computationally expensive, particularly for complex geometries. Several machine learning (ML) models have been proposed in the literature to significantly reduce computation time while maintaining acceptable accuracy. However, ML models often face limitations in terms of accuracy and scalability and depend on significant mesh downsampling, which can negatively affect prediction accuracy and generalization. In this work, we propose a novel ML model architecture, DoMINO (Decomposable Multi-scale Iterative Neural Operator) developed in NVIDIA Modulus to address the various challenges of machine learning based surrogate modeling of engineering simulations. DoMINO is a point cloudbased ML model that uses local geometric information to predict flow fields on discrete points. The DoMINO model is validated for the automotive aerodynamics use case using the DrivAerML dataset. Through our experiments we demonstrate the scalability, performance, accuracy and generalization of our model to both in-distribution and out-of-distribution testing samples. Moreover, the results are analyzed using a range of engineering specific metrics important for validating numerical simulations.

In Nepal, air pollution is a serious public health concern, especially in cities like Kathmandu where particulate matter (PM2.5 and PM10) has a major influence on respiratory health and air quality. The Air Quality Index (AQI) is predicted in this work using a Random Forest Regressor, and the model's predictions are interpreted using SHAP (SHapley Additive exPlanations) analysis. With the lowest Testing RMSE (0.23) and flawless R2 scores (1.00), CatBoost performs better than other models, demonstrating its greater accuracy and generalization which is cross validated using a nested cross validation approach. NowCast Concentration and Raw Concentration are the most important elements influencing AQI values, according to SHAP research, which shows that the machine learning results are highly accurate. Their significance as major contributors to air pollution is highlighted by the fact that high values of these characteristics significantly raise the AQI. This study investigates the Hydrogen-Alpha (HA) biodegradable filter as a novel way to reduce the related health hazards. With removal efficiency of more than 98% for PM2.5 and 99.24% for PM10, the HA filter offers exceptional defense against dangerous airborne particles. These devices, which are biodegradable face masks and cigarette filters, address the environmental issues associated with traditional filters' non-biodegradable trash while also lowering exposure to air contaminants.

This study focuses on membrane-based systems for CO$_2$ separation, addressing the urgent need for efficient carbon capture solutions to mitigate climate change. Linear regression models, based on membrane equations, were utilized to estimate key parameters, including porosity ($\epsilon$) of 0.4805, Kozeny constant (K) of 2.9084, specific surface area ($\sigma$) of 105.3272 m$^2$/m$^3$, mean pressure (Pm) of 6.2166 MPa, viscosity ($\mu$) of 0.1997 Ns/m$^2$, and gas flux (Jg) of 3.2559 kg m$^{-2}$ s$^{-1}$. These parameters were derived from the analysis of synthetic datasets using linear regression. The study also provides insights into the performance of the membrane, with a flow rate (Q) of 9.8778 $\times$ 10$^{-4}$ m$^3$/s, an injection pressure (P$_1$) of 2.8219 MPa, and an exit pressure (P$_2$) of 2.5762 MPa. The permeability value of 0.045 for CO$_2$ indicates the potential for efficient separation. Optimizing membrane properties to selectively block CO$_2$ while allowing other gases to pass is crucial for improving carbon capture efficiency. By integrating these technologies into industrial processes, significant reductions in greenhouse gas emissions can be achieved, fostering a circular carbon economy and contributing to global climate goals. This study also explores how artificial intelligence (AI) can aid in designing membranes for carbon capture, addressing the global climate change challenge and supporting the Sustainable Development Goals (SDGs) set by the United Nations.

In recent years, artificial intelligence (AI) has become an integral part of daily life, serving as a transformative tool across various professional domains [1] and driving personal applications through advancements in transformer models that power large language models (LLMs) [2]. However, both training and inference of AI models demand substantial computational and energy resources, which are becoming increasingly challenging to access [3, 4]. While server-class GPUs are effective for training, their energy inefficiency [5] and high costs present significant barriers [6]. Additionally, the environmental impact of energy-intensive AI systems has raised critical concerns about their role in exacerbating climate change [4]. Amdahl's law predicts that performance and efficiency gains are best achieved through innovative application-specific accelerator architectures rather than scaling up multi-core general-purpose processors [7]. Consequently, applicationspecific integrated circuits (ASICs), both digital and analog, have emerged as critical solutions for enabling highefficiency training and inference of artificial neural networks [7, 8, 9]. Digital accelerators are widely adopted for training workloads. Notable examples include the Brainwave Neural Processing Unit (NPU) [10], Google's Tensor Processing Unit (TPU) [11], and low-precision inference accelerators such as YodaNN [5], the Unified Neural Processing Unit (UNPU) [12], and BRein Memory [13].

Multi-agent systems (MAS) are increasingly applied to complex task allocation in two-sided markets, where agents such as companies and customers interact dynamically. Traditional company-led Stackelberg game models, where companies set service prices, and customers respond, struggle to accommodate diverse and personalised customer demands in emerging markets like crowdsourcing. This paper proposes a customer-led Stackelberg game model for cost-efficient task allocation, where customers initiate tasks as leaders, and companies create their strategies as followers to meet these demands. We prove the existence of Nash Equilibrium for the follower game and Stackelberg Equilibrium for the leader game while discussing their uniqueness under specific conditions, ensuring cost-efficient task allocation and improved market performance. Using the satellite constellation services market as a real-world case, experimental results show a 23% reduction in customer payments and a 6.7-fold increase in company revenues, demonstrating the model's effectiveness in emerging markets.

Effective multi-user delay-constrained scheduling is crucial in various real-world applications, such as instant messaging, live streaming, and data center management. In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system dynamics, which are often time-varying and challenging to estimate. Current learning-based methods typically require interactions with actual systems during the training stage, which can be difficult or impractical, as it is capable of significantly degrading system performance and incurring substantial service costs. To address these challenges, we propose a novel offline reinforcement learning-based algorithm, named \underline{S}cheduling By \underline{O}ffline Learning with \underline{C}ritic Guidance and \underline{D}iffusion Generation (SOCD), to learn efficient scheduling policies purely from pre-collected \emph{offline data}. SOCD innovatively employs a diffusion-based policy network, complemented by a sampling-free critic network for policy guidance. By integrating the Lagrangian multiplier optimization into the offline reinforcement learning, SOCD effectively trains high-quality constraint-aware policies exclusively from available datasets, eliminating the need for online interactions with the system. Experimental results demonstrate that SOCD is resilient to various system dynamics, including partially observable and large-scale environments, and delivers superior performance compared to existing methods.

As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on centralized cloud servers. However, implementing and executing complex algorithms in embedded devices poses significant challenges due to limited computational power, memory, and energy resources. This paper presents algorithmic and hardware techniques to efficiently implement two LinearUCB Contextual Bandits algorithms on resource-constrained embedded devices. Algorithmic modifications based on the Sherman-Morrison-Woodbury formula streamline model complexity, while vector acceleration is harnessed to speed up matrix operations. We analyze the impact of each optimization individually and then combine them in a two-pronged strategy. The results show notable improvements in execution time and energy consumption, demonstrating the effectiveness of combining algorithmic and hardware optimizations to enhance learning models for edge computing environments with low-power and real-time requirements.

This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is required to predict human actions over a finite future horizon; such predictions are often inaccurate in real-world scenarios. One possible approach to reduce the uncertainties is to provide the robot with the capability of reasoning about the human's awareness of potential dangers. This paper discusses that by using a binary variable, so-called danger awareness coefficient, it is possible to differentiate between concerned and unconcerned humans, and provides a learning algorithm to determine this coefficient by observing human actions. Moreover, this paper argues how humans rely on predictions of other agents' future actions (including those of robots in human-robot interaction) in their decision-making. It also shows that ignoring this aspect in predicting human's future actions can significantly degrade the efficiency of the interaction, causing agents to deviate from their optimal paths. The proposed robot action planning scheme is verified and validated via extensive simulation and experimental studies on a LoCoBot WidowX-250.