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Assessing the short-term exposure risk and mortality burden of dust and fine aerosol PM2.5 in Central Asia
No full textCentral Asia faces severe exposure risks to fine particulate matter (PM2.5) from both wind-blown dust and anthropogenic emissions; however, the associated health effects remain poorly understood. This study assesses the short-term PM2.5 exposure risk and associated premature mortality burden across Central Asia. The highest seasonal mean PM2.5 concentration (13.4 μg m−3) occurs in winter, dominated by anthropogenic fine aerosols, followed by spring (12.2 μg m−3), dominated by dust emission. Population-weighted PM2.5 concentrations exhibit widespread declines from 2017 to 2022, with the largest decrease observed in Almaty. In 2022, the number of days with population-weighted PM2.5 exceeding the WHO air quality guideline ranged from 124 days in Kazakhstan to 251 days in Tajikistan. Among major cities, Almaty experiences the highest exposure to unhealthy and hazardous PM2.5 levels. Short-term PM2.5 exposure is estimated to cause 5074 (95% CI: 3428–6728) premature deaths annually in Central Asia, including 2225 (1504–2949) in Uzbekistan, 1448 (978–1922) in Kazakhstan, 546 (369–724) in Tajikistan, 437 (295–579) in Turkmenistan, and 418 (282–554) in Kyrgyzstan. Source attribution using MERRA2 aerosol reanalysis indicates that dust contributes 63–90% of the mortality burden, although these estimates are subject to uncertainties in the representation of dust and anthropogenic sources in the underlying models
On the q-factorization of power series
No full textAny power series with unit constant term can be factored into an infinite product of the form ∏n≥1(1-qn)-an. We give direct formulas for the exponents an in terms of the coefficients of the power series, and vice versa, as sums over partitions. As examples, we prove identities for certain partition enumeration functions. Finally, we note q-analogues of our enumeration formulas
Efficient Quantum Dot Solar Cells with Sustainable Oxide Thin Films
No full textThin-film solar cells are more promising for low-cost and large-area photovoltaic devices. Tremendous efforts have been invested in using cadmium telluride (CdTe), copper indium gallium selenide (CIGS), and perovskite thin films for energy harvesting. In contrast, zinc oxide (ZnO) and molybdenum trioxides (MoO3) are relatively earth-abundant, environmentally stable, and sustainable for thin-film solar cells. ZnO nanostructures have recently gained success in producing effective (∼8.55%) quantum dot solar cells (QDSCs). While nanostructures offer high surface areas to receive electrons from quantum dots (QDs), they are dominated by surface dangling bonds. These defects can trap electrons and limit effective transport at the interface between the ZnO nanostructures and QDs. We anticipate that QDSCs based on thin-film materials can minimize such interface trapping states and be more efficient than those demonstrated with ZnO nanostructures. We strategically develop quality ZnO and MoO3 thin films to produce QDSCs with power conversion efficiency as high as 11.4%. Our approach will inspire others to use scalable thin-film technology and QDs for solar energy harvesting based on sustainable ZnO and MoO3
PLM-DBPs: enhancing plant DNA-binding protein prediction by integrating sequence-based and structure-aware protein language models
Get PDFDNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several state-of-the-art PLMs to extract high-dimensional protein representations and experimented with various fusion strategies to validate the complementary information between the various representations. Our final model, a fusion of sequence-based and structure-aware ANN models, achieves a notable improvement in predicting DBPs in plants outperforming previous state-of-the-art models. Although sequence-based PLMs already demonstrate strong performance in DBP prediction, our findings show that the integration of structural information further enhances predictive accuracy. This underscores the complementary nature of structural representations and establishes PLM-DBPs as a robust tool for advancing plant research and agricultural innovation. The proposed model and other resources are publicly available at https://github.com/suresh-pokharel/PLM-DBP
Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety
No full textDeliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using metrics, such as Blind/Reference-less Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Entropy-based Image Quality Assessment. The results indicate that green bike lanes are more likely to be recognized by machine vision systems across a wide range of environmental conditions, demonstrating enhanced recognition capabilities. Green lane markings exhibit enhanced visibility and stability, with BRISQUE scores below 82, a median contrast ratio of 17.6, and improved resilience to motion blur and NIQE variations under diverse conditions
Automated Library Shelf Management System with Programmable Logic Controller and Humanmachine Interface
No full textThis paper explores the shelf movement in a library system, driven by an Allen-Bradley PLC and an HMI interface. This project was part of the advanced PLC course at Michigan Technological University and involved building a prototype of library shelves using 3D printing technology. The system is controlled by a PLC, which manages NEMA stepper motors and motor drivers to facilitate shelf movement. Limit switches are employed to ensure the safe positioning of the shelves, preventing them from crashing into the walls, while a photoelectric sensor provides additional safety by ensuring the shelves do not move towards a person. This project demonstrates the integration of advanced control systems and mechanical design to automate library shelf operations efficiently
Extraction of Pure Plastic Resins From PCR Plastic Waste by Solvent-Targeted Recovery and Precipitation (STRAP)
No full textWe have been developing a solvent-based plastic recycling technology called STRAP. The technology is based on dissolving a targeted plastic resin in a specific solvent that does not dissolve other resins. We have demonstrated STRAP in thousands of bench scale experiments for a large variety of wastes. Recently we have demonstrated the technology for PCR, using mixed plastic wastes (MPWs), from a wet Material Recovery Facility (MRF). The process includes (1) infrared (IR) characterization to determine the plastic composition for accurate selection of the solvent to be used for the extraction of the pure resins. (2) Shredding to the right size and aspect ratio required for flowable and fast dissolvable process. (3) Mixing the MPW in the first solvent to dissolve the first resin. (4) Filtration of the solution plastic blend, to separate the nondissolved plastic from the solution. (5) Further filtration of the solution to remove micron-sized particle of pigments and fibers. (6) Cooling for precipitation. (7) Filtration of pure resins. (8) Drying of a pure resin. (9) Extrusion of the resin to pellets. (10) Generating films or other products from the pure resin. Steps 1–10 can be considered as one-cycle that extracted the first resin. (11) A second resin can be extracted with a respective solvent from the plastic that did not dissolve in the first cycle and following steps 1–10 described above. The process also includes characterization of interim and final products. The effort includes building a pilot system at 25 kg/h throughput. We will present specific results for various PCR
ASSESSING ACTIVATORS OF CAENORHABDITIS ELEGANS GERMLINE EXPRESSION USING AN RNA INTERFERENCE SCREEN
No full textA soma-to-germline transformation, a phenomenon seen in multiple cancer types, is when somatic cells adopt properties exclusive to germ cells. The mechanism of the misexpression of germline genes is not fully understood. In the organism Caenorhabditis elegans, the soma-to-germline transformation occurs with a loss-of-function mutation in the transcription regulator complex known as DRM. We hypothesize that DRM indirectly represses germline gene transcription in somatic cells to prevent soma-to-germline transformation by directly repressing transcription of germline genes. We predict that when DRM function is lost in C. elegans, the transcription factor(s) will activate the targeted germline genes in somatic cells. To test this hypothesis, we performed an RNA interference (RNAi) screen to assess the suppression of ectopic expression of the GFP-tagged glh-1 gene in the lin-35(n745) DRM loss-of-function strain. We used mes-4 as our positive control due to its ability to suppress ectopic expression in DRM mutants. We discovered four genes of interest: oma-2, ceh-39, pqn-21, and sptf-3 as transcription factors that may activate ectopic expression of germline genes in somatic cells. These results help glean insight into how DRM protects somatic cells from the soma-to-germline transformation, which can be applied to an understanding of the phenomenon in cancer cells
RELICT PONDEROSA PINE MORTALITY AND FUELS STUDY IN THE BOB MARSHALL WILDERNESS, MONTANA, USA
Get PDFMany forests have historically experienced mixed-severity fire regimes that were disrupted during the 20th century due to fire exclusion policy. A stand of relict Pinus ponderosa in the Bob Marshall Wilderness Complex that burned in 2003, 2011, and 2018 provides a unique case study for investigating the effects of returned fire to the landscape. Many of the trees have fire scars or Indigenous bark-peeling scars, preserving valuable ecological and anthropological history. We sought to determine whether scarred trees were more susceptible to fire damage, if larger trees had greater fire resilience than smaller trees, and if fuel loads have changed with fire. A stand inventory was conducted in 2024, continuing previous inventories since 2004. Results indicate a linear relationship between diameter and mortality suggesting that larger tree scars remain resilient to fire. Litter and duff loads are low compared to prefire loads and multiple fires have reduced sapling regeneration
BOUND PRESERVING DISCONTINUOUS GALERKIN METHODS FOR EULER EQUATIONS AND NONEQUILIBRIUM FLOWS
Get PDFThis dissertation is composed of four chapters in which we will closely examine the high order bound preserving discontinuous Galerkin methods for solving partial differential equations, specifically non-equilibrium chemical reacting flows and Euler equations under gravitational fields. A shared requirement between the two is the necessity for positive values of both density and pressure. Due to this physical nature of the two systems, constructing a positivity preserving scheme become very essential in our research.
For non-equilibrium flows where multi-reactions and multi-species are involved, we are also required to keep the bounds of the mass fraction of each species in between 0 and 1. In this case, the positivity preserving technique should be applied to each species to preserve the lower bound 0. Then enforce the summation of the mass fraction be 1 by using consistent flux and conservative time integration for maintaining the upper bound 1. We apply the Patankar time integration. This is a conservative implicit method targeted stiff source term to reduce the computational cost. We develop the bound-preserving discontinuous Galerkin method coupled with the Patankar time integration for the purpose of conservation, bound preserving, and efficiency. The bound preserving DG and Patankar time integration require polynomials on rectangular meshes in two dimensional space to match the degree of freedom. The reason is that Patankar can keep the positivity of the pre-selected point-values of the target variables but the positivity-preserving technique for DG method requires positive numerical approximations at the cell interfaces. Additionally, slope limiters are applied to ensure the positivty of the numerical solutions.
The Euler equations under gravitational fields admit steady state solutions. Therefore, besides being positivity preserving, we should also emphasize on the ability of the numerical scheme to yield steady-state solutions when at equilibrium and to catch the small perturbations when they appear. The well-balanced scheme should be used for this purpose or else the truncation error will be built up and destroy the robustness. We develop high order positivity-preserving well-balanced discontinuous Galerkin methods with Lax-Friedrich fluxes. The difficulty of such scheme is the compatibility of the PP DG and WB. One needs the penalty term in the flux to be large and the other needs it to be zero. The solution to this concern is to use the relationship between numerical fluxes to obtain a new penalty term which not only guarantees the positivity but also turns zero at the steady state. For the two studies, numerical experiments are given to demonstrate the performance of both schemes