Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPACTS OF BIOGENIC AND ANTHOROPOGENIC EMISSIONS ON OZONE AND SECONDARY ORGANIC AEROSOL

Atmospheric fine particles and ozone (O3) are correlated with adverse health effects, visibility reduction, and climate change. In recent decades, China has suffered from the record-breaking severe haze event and elevated ozone problems. Recent field measurements show that organic carbon (OC) was one of the major components during haze events, and it has been suggested that secondary organic aerosol (SOA) could account for a significant fraction of the total observed OC. Quantitative knowledge of the contributions of different emissions sources to SOA and ozone concentrations is significant to better understand their formation mechanisms and is useful to develop the effective emission control strategies. A Community Multiscale Air Quality (CMAQ) model with source-oriented lumped SAPRC-11 (S11L) photochemical mechanism is applied to determine the contributions of anthropogenic and biogenic sources to SOA and ozone concentrations in China. In China, predicted SOA concentrations are generally higher in summer (10-15 μg · m−3) due to large contributions of biogenic emissions within China (country average ∼60%) and in winter due to industrial and residential sectors (country average ∼78% total) based on the Multi-resolution Emission Inventory for China (MEIC) emission inventory. However, the transportation sector (∼30-40% vs. ∼5% by MEIC) is predicted to be much more important while the residential (∼21-24% vs. ∼42%) sector is predicted to be less important based on The Regional Emission inventory in ASia v2.1(REAS2) emissions. These discrepancies in source contributions to SOA needs to be further investigated as the country seeks optimal emission control strategies to fight severe air pollution. The ozone attribution scheme based on the three-regime definition was incorporated into the CMAQ model to quantify NOx and VOC contributions to ozone concentrations in China in August 2013. Most of the areas considered by the two-regime approach as
Application Of Waste Low Heat As A Motive Source For Ejection Air-Conditiong Systems For Motor Yachts

Heat recovery is a common approach for effective energy management. With utilization of the waste heat the investment and operation costs can be reduced. The possibility of utilisation of the waste heat from flue gases in the maritime industry is presented and discussed. Combustion engine is a main source for electric energy consumed by all electrical devices in ships and yachts. Currently, the classic compression refrigeration systems driven by electricity generated in the generators are used for production of cold water used in AC units. Conversion of fuel energy into mechanical and electrical energy is related with creation of a significant amount of heat which is irretrievably removed. The proposed application of heat driven ejection refrigeration system may be thought as an excellent example of an industrial application with a strong potential for implementation. At the same time it combines all positive aspects of the environmentally-friendly cold production approach using clean technology and meets all standards in the use of ecological working fluids.
Isolation of Nano Fibres from Hemp and Flax and Their Thermoplastic Composites——Hemp and Flax Nanofibres and Composites

Agricultural feed stocks are one of the readily available and inexpensive lignocellulosic feed stocks for preparing cellulose nano fibrils. The very common method for isolation and separation of cellulosic fibrils from these feed stocks include successive physicochemical techniques. In this paper, a mild chemical treatment was used to remove pectinic substances and lignin, following a mechanical defibrillation using a commercial grinder to isolate nano fibres from agricultural fibres such as flax and hemp. The defibrillation process was optimized based on the strength properties of the nanofibril films. The generated nano fibres have a diameter distribution in the range of 20-100 nm with 80% fibrils in the range of 20-60 nm. The tensile strength and the modulus of the nano papers were 180-200 MPa and 9-11 GPa compared to 100-95 MPa and 5-6 GPa respectively for hemp and flax. The study showed that, the nano papers can impart high strength even in the presence of hemicelluloses present ed in the fibre bundle indicating removal of hemicelluloses to very low extent using intensive chemical treatments which may not be required for feed stocks with fewer amounts of hemicelluloses, such as agricultural fibres. To exploit the strength properties and reinforcing potential of these nano papers, composites were prepared with polycarbonate. The prepared modulus of the composites showed a significant improvement at a fibre loading of 13 wt%.
The MIRD Schema for Radiopharmaceutical Dosimetry: A Review

and the increasing therapeutic application of such agents, internal dosimetry in nuclear medicine and the MIRD schema continue to evolve — from population-average and organ-level to patient-speci ﬁ c and suborgan to voxel-level to cell-level dose estimation. This article will review the basic MIRD schema, relevant quantities and units, reference anatomic models, and its adaptation to small-scale and patient-speci ﬁ c dosimetry.
Performance and Growth Curve Prediction of Crossbred Chickens from Crossing between Local Roosters and Layer Hens

Demand for crossbred chicken (Indonesian native chicken x laying hens) continues to increase. This study aimed to find the best-crossbred chicken resulting from crossing between local/native roosters and laying hens which produce the highest body weight up to eight weeks of age, and its growth prediction using the Gompertz function. Eighteen (18) native roosters (9 Bangkok roosters, 9 Pelung roosters) and 36 laying hens of strains Isa brown, Lohman brown, and Hyline brown (12 each) were used in this study to produce 360 crossbred chickens. Factorial 2x3 (two male local roosters versus three strains of laying hens) using a completely randomized design with 3 replications was applied in this study. The results showed an interaction between native roosters and layer hens on the body weight of crossbred chickens at eight weeks of age. Crossbred chicken from crossing between Pelung rooster and Isa brown layer hen was the best in body weight at eight weeks of age. This crossbred chicken would reach a mature weight of 3026.89 g with an inflection weight of 1113.53 g and an inflection age of 7.40 weeks. In conclusion, crossbred chicken from crossing between Pelung roosters and layer hens of Isa brown strain was the best crossbred with the highest body weight at eight weeks of age. This crossbred chicken could be an alternative for fulfilling the demand for local chicken in Indonesia because the slaughter age could be shortened from 8 months to 8 weeks.
Heavy metals in urban soils

This thesis is focused on the investigation of heavy metal contaminated urban soils (especially lead and zinc). Soil samples from greater Montreal area were collected. The concentrations of lead and zinc of some soil samples were at elevated levels, with the highest concentration being 2000 mg/kg (the EPA standard limits for both metals is 300 mg/kg at residential soils). The physical and chemical properties of soils, including soil particle size distribution, soil cation exchange capacity (CEC), soil specific surface area (SSA), soil permeability coefficients, were determined. The majority of soil particles in the samples were found to have the texture of fine sand (loamy and silty sands). Selective sequential extraction (SSE) was performed to understand the affinities of heavy metals toward various fractions of soil. The results indicate that lead has the highest affinity toward soil fraction of organic matter, while zinc has the highest affinity toward soil oxides. Soil column leaching tests were carried out. SSE, together with soil leaching tests, explains the mobility of heavy metals. It shows that zinc has a higher mobility in soils than lead. With the knowledge of heavy metal mobility in soils, the soil decontamination scheme can be planned.
A New Estimate of North American Mountain Snow Accumulation From Regional Climate Model Simulations

Despite the importance of mountain snowpack to understanding the water and energy cycles in North America's montane regions, no reliable mountain snow climatology exists for the entire continent. We present a new estimate of mountain snow water equivalent (SWE) for North America from regional climate model simulations. Climatological peak SWE in North America mountains is 1,006 km3, 2.94 times larger than previous estimates from reanalyses. By combining this mountain SWE value with the best available global product in nonmountain areas, we estimate peak North America SWE of 1,684 km3, 55% greater than previous estimates. In our simulations, the date of maximum SWE varies widely by mountain range, from early March to mid‐April. Though mountains comprise 24% of the continent's land area, we estimate that they contain ~60% of North American SWE. This new estimate is a suitable benchmark for continental‐ and global‐scale water and energy budget studies.
Evaluating Microbiological Safety, Sensory Quality, and Packaging for Online Market Success of Roasted Pickled Fish Powder

This study explores the pivotal roles of microbiological safety, sensory qualities, packaging efficiency, and consumer preferences in determining the success of roasted pickled fish powder (RPFP) variants in the online marketplace. The comparison of the nutritional composition of the developed RPFP variants with a commercial benchmark reveals significant differences: the protein content in the herbal flavor variant is found to be 28.97%, which is lower than the 40.17% found in the commercial benchmark, while the fat content in the spicy flavor variant is measured at 19.51%, exceeding the 10.60% present in the benchmark. Additionally, the herbal flavor boasts a superior dietary fiber content of 14.23%, highlighting the intricate relationship between nutritional content and sensory attributes, which is critical in online retail, where physical product evaluation is not possible. Our comprehensive approach, evaluating both nutritional and sensory dimensions, introduces a novel perspective to the adaptation of traditional food products for e-commerce, addressing a gap in the literature. Despite this study’s limitations, including a focused market analysis and constrained sample size, our findings offer valuable insights into enhancing product quality and integrity in the digital marketplace, positioning RPFP for competitive success while suggesting directions for future research.
Multidomain Cyber-Physical Testbed for Power System Vulnerability Assessment

The rapid digitalization of power systems involves enhanced interconnectivity, intelligence, and cost-efficiency across all components. In the era of Industry 5.0, the criticality of energy supply makes power systems prime targets for attacks, highlighting the need for the creation and evaluation of solutions against cyber-physical threats. Testbeds have emerged as essential tools for these purposes by representing real-world power systems in controlled environments and simulating cyber-physical attack-defense experiments. This paper introduces a Cyber-Physical Security (CPS) testbed rooted in the Smart Grid Architecture Model (SGAM) and developed adversary setup within the National Smart Grid Laboratory at the Norwegian University of Science and Technology. By adhering to the SGAM framework, this study delves into the classification and assessment of threats within the structure of the CPS testbed, examining vulnerabilities at distinct structural levels. Significantly, the strategic placement of the adversary setup within these levels enables a comprehensive evaluation of cyber-physical vulnerabilities in simulated systems, thereby facilitating the assessment of protective measures. Furthermore, this research presents case studies using three data sources as an aggregated dynamic power system model simulated in the real-time digital simulator OPAL-RT, real power grid, and playback of previously recorded data frames using virtual phasor measurement units functionality. The focus of this work is on the analysis of the five most common cyberattacks on power systems, such as passive and active reconnaissance, interruption in communication, TCP packet injection, and men-in-the-middle attacks utilizing the C37.118. 2-2011 protocol. The results of the case studies illustrate the framework for the adversary setup and provide proof-of-concept attack scenarios for evaluation purposes. As part of future work, we intend to expand upon this research with a defender setup and implement more sophisticated, stealthy attacks.
Retrieval of Leaf Area Index From MODIS Surface Reflectance by Incorporating the Subpixel Information From Decametric-Resolution Data

High-frequency leaf area index (LAI) dataset is essential for vegetation dynamic monitoring and crop yield estimation; however, due to the negative impacts of land surface heterogeneity, current hectometric-resolution LAI products cannot satisfy the uncertainty requirement of LAI dataset in practice. Here, we proposed a method named “use of subpixel information” (USPI) that leverages fine-scale remote sensing data to improve the accuracy of hectometric-resolution LAI retrieval. Specifically, based on machine learning (ML) models trained by representative samples, we retrieved the USPI LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance by incorporating the subpixel information from Sentinel-2 LAI estimates. The USPI LAI was comprehensively evaluated using 30-m LAI reference maps in three aspects: the performance of different ML models, the comparison with MODIS LAI products, and the potential correction of USPI LAI for clumping effect. Results showed that the Gaussian process regression (GPR) model outperformed other ML models for deriving LAI estimates; furthermore, USPI LAI exhibited better performance than MODIS LAI product, with bias, root mean square error (RMSE), and $R^{2}$ of −0.308, 0.593, and 0.826, respectively, especially for pixels contaminated by atmospheric conditions. The underestimation of USPI LAI should be, nevertheless, noted because the effective LAI provided by Sentinel-2 was involved in the GPR training process; thus, it is necessary to introduce the accurate clumping index (CI) dataset for further improvement of USPI LAI retrievals. Our study indicates that incorporating the subpixel information from decametric-resolution data can effectively reduce the uncertainty of hectometric-resolution LAI retrieval, which is promising for generating the high-accuracy LAI time series dataset.
Viral Epitope Scanning Reveals Correlation between Seasonal HCoVs and SARS-CoV-2 Antibody Responses among Cancer and Non-Cancer Patients

Seasonal coronaviruses (HCoVs) are known to contribute to cross-reactive antibody (Ab) responses against SARS-CoV-2. While these responses are predictable due to the high homology between SARS-CoV-2 and other CoVs, the impact of these responses on susceptibility to SARS-CoV-2 infection in cancer patients is unclear. To investigate the influence of prior HCoV infection on anti-SARS-CoV-2 Ab responses among COVID-19 asymptomatic individuals with cancer and controls without cancers, we utilized the VirScan technology in which phage immunoprecipitation and sequencing (PhIP-seq) of longitudinal plasma samples was performed to investigate high-resolution (i.e., epitope level) humoral CoV responses. Despite testing positive for anti-SARS-CoV-2 Ab in the plasma, a majority of the participants were asymptomatic for COVID-19 with no prior history of COVID-19 diagnosis. Although the magnitudes of the anti-SARS-CoV-2 Ab responses were lower in individuals with Kaposi sarcoma (KS) compared to non-KS cancer individuals and those without cancer, the HCoV Ab repertoire was similar between individuals with and without cancer independent of age, sex, HIV status, and chemotherapy. The magnitudes of the anti-spike HCoV responses showed a strong positive association with those of the anti-SARS-CoV-2 spike in cancer patients, and only a weak association in non-cancer patients, suggesting that prior infection with HCoVs might play a role in limiting SARS-CoV-2 infection and COVID-19 disease severity.
SDST: Self-Supervised Double-Structure Transformer for Hyperspectral Images Clustering

Due to the lack of labeled information and the high spectral variability in high-dimensional hyperspectral images (HSIs), HSI clustering has emerged as an effective unsupervised approach for HSI information extraction and classification. Deep clustering methods have achieved significant success in unsupervised HSI classification (HSIC) and have gained increasing attention. However, these methods have limitations in terms of robustness, adaptability, and feature representation when dealing with complex large-scale HSI datasets. Therefore, this article introduced a novel self-supervised double-structure transformer (SDST) approach for HSI clustering. Specifically, in our approach, we designed a shared autoformer structure based on an autoencoder to learn the global properties of HSI data by fusing the multilevel features from the autoencoder with the transformer. Furthermore, we proposed a Siamese dual-former graph module (DFGM) with superpixel-level features for fewer nodes, which reveals long-dependency graph convolutional features, resulting in more precise graph structure features. By constructing a graph with long-dependencies, this module significantly preserves the properties of global dependencies, while focusing on the local features of each superpixel to better represent the fine-grained local details. Finally, we designed a joint optimization module to jointly optimize the double-structure model composed of the shared autoformer module and the Siamese DFGM. To validate the effectiveness of the proposed SDST method, we conducted a series of experiments on the Salinas, Botswana, Indian Pines, and Houston2013 datasets. The proposed SDST achieves competitive clustering accuracies compared with the state-of-the-art clustering methods. The codes are available at https://github.com/YiLiu1999/SDST.
Geographic, Patient, and VA Medical Center Variation in the Receipt and Mode of Primary Care in a National Sample of Veterans with Diabetes during 2020

While telemedicine infrastructure was in place within the Veterans Health Administration (VHA) healthcare system before the onset of the COVID-19 pandemic, geographically varying ordinances/closures disrupted vital care for chronic disease patients such as those with type 2 diabetes. We created a national cohort of 1,647,158 non-Hispanic White, non-Hispanic Black, and Hispanic veterans with diabetes including patients with at least one primary care visit and HbA1c lab result between 3.5% and 20% in the fiscal year (FY) 2018 or 2019. For each VAMC, the proportion of telehealth visits in FY 2019 was calculated. Two logistic Bayesian spatial models were employed for in-person primary care or telehealth primary care in the fourth quarter of the FY 2020, with spatial random effects incorporated at the VA medical center (MC) catchment area level. Finally, we computed and mapped the posterior probability of receipt of primary care for an “average” patient within each catchment area. Non-Hispanic Black veterans and Hispanic veterans were less likely to receive in-person primary care but more likely to receive tele-primary care than non-Hispanic white veterans during the study period. Veterans living in the most socially vulnerable areas were more likely to receive telehealth primary care in the fourth quarter of FY 2020 compared to the least socially vulnerable group but were less likely to receive in-person care. In summary, racial minorities and those in the most socially vulnerable areas were less likely to receive in-person primary care but more likely to receive telehealth primary care, potentially indicating a disparity in the impact of the pandemic across these groups.
Devulcanization of Whole Passenger Care Tire Material

Ground tire rubber (GTR) from whole passenger car tires is devulcanized using the conditions which are the best compromise for tire rubbers. The elaboration of the optimal devulcanization process for GTR is performed in order to obtain a homogeneous and high degree of devulcanization. Swelling of GTR in a mixture of an oil and a devulcanization aid before devulcanization was studied as a way to achieve a more homogenous breakdown of the crosslink network. Further optimization of the parameters in terms of devulcanization oil loading and process time were elaborated. By using these optimized devulcanization conditions, a significant reduction of crosslink density can be achieved for GTR
Semantics-Assisted Multiview Fusion for SAR Automatic Target Recognition

Multiview fusion algorithms have been widely applied in synthetic aperture radar automatic target recognition (SAR ATR). However, they usually ignore semantic information, which results in limited recognition performance. Therefore, how to effectively integrate semantic information into multiview fusion algorithms and explore the correlation between multiple views and semantic information is a crucial and urgent problem. To address this problem, we develop a semantics-assisted multiview fusion (SMVF) algorithm, which includes three loss terms, that is, the view-specific loss term, the semantics-regularized loss term, and the view-semantics-coupled loss term. To be specific, the view-specific and semantics-regularized loss terms convert multiple views and semantic information into their corresponding sparse codes, respectively. The view-semantics-coupled loss term constrains the sparse codes of multiple views and semantic information to explore their correlation. Finally, these three loss terms are jointly optimized to acquire the optimal sparse codes to calculate the reconstruction errors for recognition, by which SMVF not only effectively exploits semantic information, but also explores the correlation between multiple views and semantic information. Extensive experiments demonstrate that SMVF achieves high recognition accuracies (i.e., 99.7%, 91.6%, and 97.2%) under three scenarios (i.e., EOC-1, EOC-2, and SAR-ACD), which are better than other advanced algorithms.