Coherent (United States)

The planetary boundary layer confines moisture, turbulence, and locally emitted air pollutants, thus accurately discerning the height of this layer is important for constraining pollutant transport and distribution and improved regional weather and climate forecasting. Traditional methods of boundary layer height (BLH) determination rely on radiosonde measurements of potential temperature profiles. However, these sounding measurements lack the instrumentation needed to characterize the chemical composition of the boundary layer that can be provided by larger airborne platforms. In 2024, the NASA DC-8 flew over the Philippines, South Korea, Taiwan, and Thailand during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Measurements during this campaign included an extensive array of gas, particulate, and meteorological measurements. We will present results of a turbulence-based method using 3D winds to determine the boundary layer height across DC-8 vertical flight profiles, including missed approaches at urban airports. The results from this method were used to develop a boundary layer flag for the campaign, and the computed DC-8 BLHs were compared to mixed layer heights determined by an airborne-based LIDAR system.

Unlocking inertial fusion energy (IFE) demands laser technologies that are not only powerful, but also reliable and scalable. This paper introduces a new class of high-power diode laser pump modules tailored for solid-state lasers like Nd:glass and Yb:YAG, which are key components in laser-driven fusion systems. The proposed pump module concept is based on a modular and scalable architecture that simplifies system integration and enables flexible power scaling to address the evolving requirements of fusion technology demonstrators and future power-plant-class lasers. Pump modules delivering optical peak powers below 1 MW can be realized using mature diode laser bar and stack technologies, providing a robust and low-risk pathway for near-term demonstrators. Current implementations utilize 500 W-class diode bars with electro-optical efficiencies exceeding 60%, while the development roadmap targets efficiencies of 70%. Scaling pump modules toward the megawatt power class imposes additional constraints at the component and architecture level. In particular, bar-level peak powers of ≥ 1 kW combined with low-pitch stack architectures are required to achieve the pump power densities needed for compact optical pump engines. Recent advances demonstrating bar-level outputs up to 2 kW highlight substantial headroom for further scaling within this framework. A key requirement for these systems is the uniform excitation of large-aperture gain media, essential for meeting the stringent performance standards of fusion-class lasers. To achieve this, the pump system will provide a homogenized beam profile with adaptable dimensions from 20×20 mm² to 85×85 mm², ensuring consistent energy distribution across the gain medium. The proposed technology combines high optical output power with excellent efficiency and a compact footprint, positioning it as a cornerstone for scaling future laser architectures beyond the limitations of lamp-pumped designs.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) in machine learning (ML) regression algorithms to derive concentrations of particulate matter (PM) with aerodynamic diameters less than 2.5&nbsp;mm (PM_2.5), 10 mm (PM₁₀), and the PM_2.5/PM₁₀ratio. The ML regression models are trained using airborne HSRL measurements acquired over major metropolitan regions in the United States and Asia that are coincident with hourly surface PM_2.5and PM₁₀ measurements from the EPA air quality system and similar networks in other countries. We examine several regression methods and find that exponential Gaussian Process regression (GPR) algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. When evaluated using surface measurements withheld from the training sets, ML models that use the HSRL near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS differences in PM_2.5 retrievals around 5 mg m^-3 and correlation coefficients above 0.8, respectively. Corresponding RMS differences and correlation coefficients for PM₁₀ retrievals are 11 mg m^-3 and 0.7 and corresponding RMS differences and correlation coefficients for PM_2.5/PM₁₀ are 0.17 and 0.75. This retrieval performance is achieved using airborne HSRL measurements alone and so does not depend on external knowledge of or assumptions regarding aerosol type, aerosol mass extinction efficiency, aerosol hygroscopic growth, the ratio of PM_2.5to PM₁₀, particle density, or relative humidity. PM_2.5values in the training set range from about 5 to 80 mg m^-3; PM₁₀ values range from about 10 to 100&nbsp;mg m^-3. Accurate retrievals of PM outside these ranges would require commensurate training data. We present examples of PM retrievals in the United States as well as Asia when HSRL measurements were acquired when the aircraft flew systematic "raster-scan" patterns for several hours over major urban areas. We show that these PM_2.5 retrievals are in good agreement with PM_2.5 derived from coincident airborne <em>in situ</em> measurements near the surface as well as aloft. We describe also how the distribution of PM_2.5varies with aerosol type and altitude over these regions. We use the HSRL measurements of aerosol extinction and retrievals of surface PM_2.5along with HSRL retrievals of aerosol type to derive estimates of the fine mode aerosol mass extinction efficiency (MEE_f) for major aerosol types identified by an updated HSRL aerosol classification method. MEE_f ranges from about 2.6 &plusmn; 0.5&nbsp;m² g^-1 for maritime aerosol to 5.0 &plusmn; 0.7&nbsp;m² g^-1 for smoke. These estimates of MEE_f are also in good agreement with values derived from airborne <em>in situ</em> measurements. We also discuss how this methodology may be applied to measurements from the Atmospheric Lidar (ATLID) on the EarthCARE satellite.

Amyloid-beta (Aβ) aggregation is the key component of neuritic plaques that drives Alzheimer's disease (AD) progression and cognitive decline. Although synaptic dysfunction strongly correlates with cognitive impairment, its underlying mechanisms remain unclear. Recently, the kynurenine pathway (KP) of tryptophan metabolism has emerged as a key contributor to AD pathology, and xanthurenic acid (XA), a naturally occurring end-product of the KP, has been implicated in neuroprotection. In this study, we investigated the neuroprotective effects of intranasally administered XA in an Aβ-induced AD mouse model. AD-like pathology was induced in mice by intracerebroventricular injection of Aβ1-42. The mice received daily intranasal instillation of XA (0.5 μg/5 μL per nostril) for 6 weeks. After XA treatment was completed, the cognitive performance was assessed in behavioral tests, then the mice were euthanized, and the brain were collected for molecular and biochemical analyses. We showed that XA treatment significantly improved the cognitive function of AD mice, and reduced AD-related pathological markers such as APP, Aβ and BACE-1 in the cortex, hippocampus and olfactory bulb. XA treatment also attenuated Aβ-induced oxidative stress through upregulation of the Nrf2/HO-1/SOD1 and key enzymatic antioxidants (GSH, GST, CAT, SOD), while concurrently reducing lipid peroxidation. Furthermore, XA treatment preserved synaptic integrity, evidenced by restoring both pre- and postsynaptic markers (SNAP-25, SYP, SNAP-23, PSD-95) and enhancing signaling via the cAMP-PKA-CREB pathway. Notably, XA differentially modulated metabotropic glutamate receptors, decreasing mGluR2 and increasing mGluR3 expression. In vitro experiments were conducted in APPswe/ind-transfected SH-SY5Y neuroblastoma cells. XA (3-100 µM) dose-dependently improved the cell viability while reducing cytotoxicity and apoptosis. Overall, these results demonstrate that XA confers multifaceted neuroprotection by modulating Aβ pathology, oxidative stress, synaptic function, and glutamatergic signaling, suggesting its potential as a novel therapeutic strategy to mitigate cognitive decline and pathological progression in AD.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) in machine learning (ML) regression algorithms to derive concentrations of particulate matter (PM) with aerodynamic diameters less than 2.5&nbsp;mm (PM_2.5), 10 mm (PM₁₀), and the PM_2.5/PM₁₀ratio. The ML regression models are trained using airborne HSRL measurements acquired over major metropolitan regions in the United States and Asia that are coincident with hourly surface PM_2.5and PM₁₀ measurements from the EPA air quality system and similar networks in other countries. We examine several regression methods and find that exponential Gaussian Process regression (GPR) algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. When evaluated using surface measurements withheld from the training sets, ML models that use the HSRL near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS differences in PM_2.5 retrievals around 5 mg m^-3 and correlation coefficients above 0.8, respectively. Corresponding RMS differences and correlation coefficients for PM₁₀ retrievals are 11 mg m^-3 and 0.7 and corresponding RMS differences and correlation coefficients for PM_2.5/PM₁₀ are 0.17 and 0.75. This retrieval performance is achieved using airborne HSRL measurements alone and so does not depend on external knowledge of or assumptions regarding aerosol type, aerosol mass extinction efficiency, aerosol hygroscopic growth, the ratio of PM_2.5to PM₁₀, particle density, or relative humidity. PM_2.5values in the training set range from about 5 to 80 mg m^-3; PM₁₀ values range from about 10 to 100&nbsp;mg m^-3. Accurate retrievals of PM outside these ranges would require commensurate training data. We present examples of PM retrievals in the United States as well as Asia when HSRL measurements were acquired when the aircraft flew systematic "raster-scan" patterns for several hours over major urban areas. We show that these PM_2.5 retrievals are in good agreement with PM_2.5 derived from coincident airborne <em>in situ</em> measurements near the surface as well as aloft. We describe also how the distribution of PM_2.5varies with aerosol type and altitude over these regions. We use the HSRL measurements of aerosol extinction and retrievals of surface PM_2.5along with HSRL retrievals of aerosol type to derive estimates of the fine mode aerosol mass extinction efficiency (MEE_f) for major aerosol types identified by an updated HSRL aerosol classification method. MEE_f ranges from about 2.6 &plusmn; 0.5&nbsp;m² g^-1 for maritime aerosol to 5.0 &plusmn; 0.7&nbsp;m² g^-1 for smoke. These estimates of MEE_f are also in good agreement with values derived from airborne <em>in situ</em> measurements. We also discuss how this methodology may be applied to measurements from the Atmospheric Lidar (ATLID) on the EarthCARE satellite.

Malnutrition remains a critical public health issue, especially among children in developing countries, due to limited access to nutritional knowledge and appropriate meal planning. A web-based rule-based expert system is proposed in this research to help carers create individualized meal plans for malnourished children. After analyzing input factors including age, gender, height, weight, and dietary preferences using rule-based reasoning, the system suggests meals that are suitable for the child's daily calories and nutritional requirements. The system, which was created using the Waterfall Model, uses evidence-based recipes from Volumes I and II of the Nutritionists' Choice Cookbook and incorporates Flask for backend processing and MySQL for data administration. The output of the system guarantees nutritional accuracy and relevance by conforming to Malaysia's Dietary Guidelines for Children and Adolescents. Its usefulness and efficacy in assisting carers were validated by expert validation and functionality testing. In the future, the system can be improved by incorporating expert feedback to improve the knowledge base, putting strong data protection measures in place based on international standards like the General Data Protection Regulation (GDPR), and adding a progress-tracking component to track children's nutritional development over time.

Quantum machine learning (QML) promises computational advantages for complex learning tasks, but identifying which datasets stand to benefit remains an open question. The recently proposed bit-bit encoding scheme encodes both inputs and outputs as bitstrings, which leads to quantum models that are universal approximators. Under bit-bit encoding, the number of input and output pairs increases exponentially with the number of qubits, which allows the calculation of the number of qubits required to fully represent a dataset. Datasets that can be covered with fewer than 50 qubits are unlikely to benefit from quantum advantage, as they are classically simulable. We use bit-bit encoding to perform a resource estimation study on both synthetic and real-world classification datasets. On synthetic data, we analyze how qubit requirements scale with the number of features and samples. On real datasets, we compare qubit requirements across different classical dimensionality reduction schemes. We find that all tested datasets require 49 qubits or fewer for full coverage, regardless of dimensionality reduction method. This suggests that standard, medium-sized, single-label classification datasets are unlikely to see performance gains from QML. In future work, we will explore more complex data types, such as multi-label, sequential, and regression, that may require more than 50 qubits for coverage and could therefore be promising candidates for quantum advantage.

Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However, current quantum algorithms for Hamiltonian simulation yield results that are inadequate for real use cases and suffer from lengthy execution times when implemented on near-term quantum hardware. In this work, we introduce a framework for evaluating the performance of quantum simulation algorithms, focusing on the computation of observables, such as energy expectation values. Our framework provides end-to-end demonstrations of algorithmic optimizations that utilize Pauli term groups based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-commutativity, generate customized Clifford measurement circuits, and implement weighted shot distribution strategies across these groups. These demonstrations span multiple quantum execution environments, allowing us to identify critical factors influencing runtime and solution accuracy. We integrate enhancements into the QED-C Application-Oriented Benchmark suite, utilizing problem instances from the opensource HamLib collection. Our results demonstrate a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 7. 1} \boldsymbol{\%}$</tex> error reduction through Pauli grouping methods, with an additional 37.6% improvement from the optimized shot distribution strategy. Our framework provides an essential tool for advancing quantum simulation performance using algorithmic optimization techniques, enabling systematic evaluation of improvements that could maximize near-term quantum computers’ capabilities and advance practical quantum utility as hardware evolves.

Optical neural networks (ONNs) promise computing efficiency beyond microelectronics for modern artificial intelligence (AI). Current ONNs using analog matrix-vector multiplication (MVM) implementations are fundamentally limited in numerical precision due to accumulated noise in electro-optical processing. We propose a digital-analog hybrid MVM architecture that achieves a high numerical precision without sacrificing computing efficiency. Our fabricated proof-of-concept hybrid optical processor (HOP) achieves 16-bit precision in high-definition image processing, with a pixel error rate of 1.8 × 10−3 at a signal-to-noise ratio of 18.2 dB, and shows no accuracy loss in MNIST digit recognition. We further explore applying the HOP processor in You Look Only Once (YOLO) object detection and demonstrate sufficient numerical precision is crucial for high confidence detection in real-world neural networks. The hybrid optical computing concept may be applied to various photonic MVM implementations to enable accurate optical computing architectures.