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A music player is a computer program for playing audio files or songs encoded in MP3 format. This application will reside in the user's computer, such as iTunes, Windows Media Player and RealPlayer that are used to organize a music collection and play audio files. Our player provides an easy-to-use graphical user interface with symbols provided on the buttons to enable varied types of users to use the player efficiently. Along with the basic functionalities of a music player like playing, pausing, stopping and playing next or previous song, we have provided some interesting features which make our player intelligent. Hence, we have rightly named it ‘Brain Waves Music Player’. It has the ability to judge our sentiments and play the suitable songs accordingly. Also, the player can be controlled using gestures.

Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fraction of the computational cost of traditional theoretical chemistry methods while maintaining high accuracy. Graph neural networks (GNNs) are particularly promising in this regard, but different types of GNNs have not yet been systematically compared. In this work, we benchmark and analyze five different GNNs for the prediction of excitation spectra from the QM9 dataset of organic molecules. We compare the GNN performance in the obvious runtime measurements, prediction accuracy, and analysis of outliers in the test set. Moreover, through TMAP clustering and statistical analysis, we are able to highlight clear hotspots of high prediction errors as well as optimal spectra prediction for molecules with certain functional groups. This in-depth benchmarking and subsequent analysis protocol lays down a recipe for comparing different ML methods and evaluating dataset quality.

Litter was collected from 12 roadside ditches in the Finger lakes Region of New York State over two sampling periods: pre-COVID-19 pandemic and during COVID-19 pandemic. Litter pieces were washed in DI water, oven dried, massed and plastic-type visually determined. Macroplastic data was analysed to assess the impact of land use, traffic, and COVID-19 variables on macroplastic accumulation on a piece, mass, and plastic-type basis. These data are all litter pieces collected, including both plastics categorized as 1 through 7 in the RIC resin classification codes as well as non-plastic litter. These data have wide-ranging reuse potential, as terrestrial microplastic accumulation is not well documented. These data could be compared with other litter accumulation across regions, specifically to assess total environmental macroplastic loading and enable contaminant mitigation strategies. These data also have direct application to modelling and transport of macroplastics into surface water bodies as a result of road ditch sampling locations. Macroplastic accumulation data across varying land uses, traffic, and COVID-19 conditions has been published [1].

Macroplastics are a ubiquitous and growing environmental contaminant with impacts in both marine and terrestrial systems. Marine sampling has dominated research in this field, despite the terrestrial origins of most plastic debris. Due to the high surface water connectivity facilitated by roadside ditches, these landscape features provide a unique sampling location linking terrestrial and surface water systems. We collected and analyzed macroplastic accumulation by number of pieces, mass, and polymer type in roadside ditches across four land uses, before and during the COVID-19 pandemic in the Finger Lakes Region of New York State. Commercial land use plastic accumulation rate was highest, while forested land use accumulation rates were lowest on a piece basis. Pre-COVID-19 piece accumulation rates were significantly higher than COVID-19 piece accumulation rates across all land uses. Mass accumulation rates followed similar patterns observed in piece accumulation, but the patterns were not always statistically significant. Plastic type 4 (i.e. thin plastic films), especially plastic bags and wrappers, was the most frequently collected type of macroplastic by piece across all land uses within the 1-7 Resin Identification Codes. By mass, the data were distributed less consistently across land uses. Cigarette filters, containing the polymer cellulose acetate, were the most frequently found roadside plastic, but are not within the 1-7 classification system. Our results suggest that policies in place limiting plastic bag usage could substantially reduce roadside plastics but other plastics, such as food wrappers and other single use plastic films, which comprised a large proportion of the plastic debris collected, should also be regulated to further decrease macroplastic pollution.

Tracers provide a way to determine, follow and quantify biochemical cycles and energy fluxes within the soil-plant-atmosphere continuum (SPAC). Thereby, different tracers, such as dyes, carbonyl sulfite or stable isotopes are employed. One major disadvantage of many tracers is, that very often, plants have to be destructively harvested to analyze the tracer concentration, making it difficult to measure continuous fluxes. Additionally, for stable isotope studies, fractionation or exchange effects can make interpretation and quantification of biogeochemical fluxes difficult. Novel tracers that are already frequently used in animal systems, are fluorescent C-dots. These nanoparticles (5-50 nm diameter) provide a non-destructive imaging option using “in vivo imaging systems” (IVIS). We examined a first approach to apply and measure C-dots as possible tracers in tree saplings of three species (Picea glauca, Pinus strobus, Tsuga canadensis). Roots were excavated from soils and exposed to 20 µmol/l liquid silica-based nanoparticles (diameter of 5.1 nm) labeled with a near-infrared fluorophore, cyanine 5.5 (excitation maximum 646 nm, emission maximum 662 nm). Subsequent continuous IVIS imaging showed real-time uptake and transport of the C-dots by the trees. Respective fluorescence intensity revealed the concentration of C-dots in each of the tissues at measured time steps. Subsequent cross-sectioning of roots, stems and leaves elucidated the internal transport pathway of the C-dots inside the saplings’ tissues. Finally, measurements of stomatal conductance, photosynthesis, transpiration rate, stem hydraulic conductivity and pre-dawn leaf water potentials in comparison to control saplings revealed no phytotoxic effect by the C-dots on plant functioning. In conclusion, C-dots provide a non-toxic new technique for measuring biochemical cycles within the SPAC. Future applications include high resolution tracing of water flow cycles and turnover times within the SPAC, compound specific analyses of root exudation or determining mechanisms of pest influences on plant metabolism.

Forest cover exerts a significant control on the partitioning of precipitation between evapotranspiration and surface runoff. Thus, understanding how plants take up and transpire water in forested catchments is essential to predict flooding potential and hydrologic cycling. A growing literature underscores the importance of integrating whole-plant hydraulics, including such processes as the spatial variability of root distribution and the temporally dynamic nature of root water uptake by depth in understanding the relationship between changes in vegetation and hydrology. The analysis of stable isotopes of water (18O and 2H) sourced from soils and plant tissue has enabled the estimation of tree root water uptake depths and water use strategies. Despite the general acceptance of stable water isotopic data to estimate plant hydraulic dynamics, this methodology imposes assumptions that may produce spurious results. For example, end member mixing analysis neglects time-delays during tree-water storage. Also, it is likely that hydraulic redistribution processes of plants, which transport water across soil depths and both into and out of plant tissue, modify δ18O and δ2H; the isotopic signature of a collected sample may thus reflect a history of transport and exposure to fractionating processes not accounted for in analysis. We tested the feasibility of C-dots, core-shell silica polyethylene-glycol coated fluorescent nano-particles (5.1 nm diameter) in 20 µmol/l solution with H2O labeled with a near-infrared fluorophore, cyanine 5.5 (excitation maximum of 646 nm, emission maximum of 662 nm), as an alternative to stable water isotopes in the investigation of plant hydraulics. We examined the absorption and transport of C-dots through soil, as well as roots and aerial structures of Eastern hemlock (Tsuga canadensis), Eastern white pine (Pinus strobus), and white spruce (Picea glauca) saplings (n = 12 each) via an IVIS-200 luminescence in-situ imaging system. We compared the fluid mechanics, residence times and mixing schemes of C-dots with 2H-labeled water during transport within these plant species to establish the nanoparticles as a viable alternative through a split-root hydraulic redistribution experiment under moderate and severe drought conditions. We present a residence-time distribution to elucidate the mixing scheme of C-dot solution and calibration curves to aid future studies. This research is the premier assessment of this nanoparticle as an alternative tracer to stable water isotopes, and as such may yield insights for broader applications.

Trees influence the partitioning of water between catchment water yield and evapotranspiration through mediation of soil water via Root Water Uptake (RWU). Recent research has estimated the depth of RWU for a variety of tree species at plot scales with measurements of stable isotopes in water and sap‐flux. Though informative, there are some challenges bridging the gap between plot‐ and catchment‐scale water fluxes. We estimated catchment‐scale tree RWU behavior for 139 forested catchments across the continental United States from continuous streamflow records with inverse ecohydrological modeling. Our catchment‐scale RWU estimates agreed well with existing plot‐scale research. Monoculture catchments dense with trees reliant on shallow soil water exhibited reduced transpiration losses compared to deep‐rooted and mixed‐species forests within the Budkyo framework. This research highlights the importance of representing plant characteristics that define RWU control of transpiration in land surface and earth systems models.