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Uncovering and addressing the secret water footprint of AI models 

The rapid advancement of metaverse applications in wireless environments necessitates efficient resource management to enhance Quality of Experience (QoE). This paper presents a novel framework for optimizing wireless resource allocation within the metaverse to optimize QoE using convex optimization and matching theory. We formulate a QoE optimization problem considering packet error rate (PER) and immersive experience. Our problem also enables us to trade off between immersive experience and PER while computing QoE. The formulated problem is a mixed-integer non-linear programming (MINLP) problem, which is addressed through decomposition, convex optimization, matching theory, and block successive upper-bound minimization (BSUM). Specifically, for a solution, our proposed model integrates matching theory, BSUM, and convex optimization to optimize the association, transmit power allocation, and resource allocation. Finally, numerical results are provided. 

Federated learning (FL) offers many benefits, such as better privacy preservation and less communication overhead for scenarios with frequent data generation. In FL, local models are trained on end-devices and then migrated to the network edge or cloud for global aggregation. This aggregated model is shared back with end-devices to further improve their local models. This iterative process continues until convergence is achieved. Although FL has many merits, it has many challenges. The prominent one is computing resource constraints. End-devices typically have fewer computing resources and are unable to learn well the local models. Therefore, split FL (SFL) was introduced to address this problem. However, enabling SFL is also challenging due to wireless resource constraints and uncertainties. We formulate a joint end-devices computing resources optimization, task-offloading, and resource allocation problem for SFL at the network edge. Our problem formulation has a mixed-integer non-linear programming problem nature and hard to solve due to the presence of both binary and continuous variables. We propose a double deep Q-network (DDDQN) and optimization-based solution. Finally, we validate the proposed method using extensive simulation results. 

This paper introduces LiSWARM, a low-cost LiDAR system to detect and track individual drones in a large swarm. LiSWARM provides robust and precise localization and recognition of drones in 3D space, which is not possible with state-of-the-art drone tracking systems that rely on radio-frequency (RF), acoustic, or RGB image signatures. It includes (1) an efficient data processing pipeline to process the point clouds, (2) robust priority-aware clustering algorithms to isolate swarm data from the background, (3) a reliable neural network-based algorithm to recognize the drones, and (4) a technique to track the trajectory of every drone in the swarm. We develop the LiSWARM prototype and validate it through both in-lab and field experiments. Notably, we measure its performance during two drone light shows involving 150 and 500 drones and confirm that the system achieves up to 98% accuracy in recognizing drones and reliably tracking drone trajectories. To evaluate the scalability of LiSWARM, we conduct a thorough analysis to benchmark the system’s performance with a swarm consisting of 15,000 drones. The results demonstrate the potential to leverage LiSWARM for other applications, such as battlefield operations, errant drone detection, and securing sensitive areas such as airports and prisons. 

Background/Objectives: The co-formulation of active pharmaceutical ingredients (APIs) is a growing strategy in biopharmaceutical development, particularly when it comes to improving solubility and bioavailability. This study explores a co-precipitation method to prepare co-formulated crystals of griseofulvin (GF) and dexamethasone (DXM), utilizing nanostructured, functionalized polylactic glycolic acid (nfPLGA) as a solubility enhancer. Methods: An antisolvent precipitation technique was employed to incorporate nfPLGA at a 3% concentration into the co-formulated GF and DXM, referred to as DXM-GF-nfPLGA. The dissolution performance of this formulation was compared to that of the pure drugs and the co-precipitated DXM-GF without nfPLGA. Results: Several characterization techniques, including electron microscopy (SEM), RAMAN, FTIR, TGA, and XRD, were used to analyze the nfPLGA incorporation and the co-precipitated co-formulations. The inclusion of nfPLGA significantly enhanced the dissolution and initial dissolution rate of both GF and DXM in the DXM-GF-nfPLGA formulation, achieving a maximum dissolution of 100%, which was not attained by the pure drugs or the DXM-GF formulation. The incorporation of nfPLGA also reduced the amount of time taken to reach 50% (T50) and 80% (T80) dissolution. T50 values decreased from 52 and 82 min (for pure DXM and GF) to 23 min for DXM-GF-nfPLGA, and the T80 improved to 50 min for DXM-GF-nfPLGA, significantly outpacing the pure compounds. Furthermore, incorporating nfPLGA into the crystal structures greatly accelerated the dissolution rates, with initial rates reaching 650.92 µg/min for DXM-GF-nfPLGA compared to 540.60 µg/min for DXM-GF, while pure GF and DXM showed lower rates. Conclusions: This work demonstrates that nfPLGA incorporation enhances dissolution performance by forming water channels within the API crystal via hydrogen-bonding interactions. This innovative nfPLGA incorporation method holds promise for developing hydrophobic co-formulations with faster solubility and dissolution rates. 

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. 

A cobalt (II) mononuclear complex was synthesized by two-nitrobenzoic acid and pyrazine-two-carboxamide ligands in the presence of sodium bicarbonate and aqueous solution of cobalt acetate tetrahydrate. The synthesized cobalt(II) complex was characterized by single crystal X-rays diffraction. The coordination geometry of the cobalt complex was octahedral with water molecules occupying the axial sites. A lot of intermolecular interactions were in response to stabilize the supramolecular assembly which were inspected by Hirshfeld surface analysis. Enrichment ratios were calculated to find the pair of atoms having the highest propensity to form crystal packing interactions. Void analysis was conducted to forecast how the crystal would respond to applied stress. Interaction energy calculations were carried out using the B3LYP/6-31G(d,p) electron density model to identify which energy types most significantly contributed to the supramolecular assembly. Moreover, the energy data obtained from DFT calculations showed an average level of stability of the molecule. The moderate HOMO-LUMO energy gap suggested reactivity, while a high electrophilicity index indicates a strong tendency for electron-accepting reactions. 

The growing adoption of residential distributed energy resources (DERs) introduces more uncertain variability in power grid operation. More importantly, the residential DERs operate behind customers’ energy meters, and therefore, the utility cannot “directly” monitor them. Prior approaches to enable visibility into behind-the-meter (BTM) DERs either depend on estimations or require intrusive instrumentation on the customer side. To address the critical need for direct real-time monitoring of BTM DERs, in this paper, we propose a novel approach for utility-side direct real-time monitoring of residential BTM DERs. We utilize high-frequency (> 10kHz) conducted electromagnetic interference (EMI) from residential DERs’ grid-tied inverters to monitor their power generation. We discuss the working principle of our approach and present supporting results using three of-the-shelf grid-tied inverters.