Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of Cassava to Irrigation Scheduling and Forage Intercropping

Field experiments were conducted to find out the level of irrigation and the effect of intercropping on the growth and yield Cassava at Veterinary College and Research Institute, Namakkal, Tamil Nadu during 2001 and 2002. The popular hybrid of cassava H 226 was tried as test crop. The soil of the experimental site was moderately drained, loamy sand. The soils were low in available N, medium in available P and low in available K. The experiments were laid out in a split plot design with three replications. In the main plot, four levels of surface irrigation at 1.0, 0.80, 0.60 and 0.40 IW / CPE ratio to 5 cm depth were compared. Three intercropping systems viz., sole cassava, cassava + maize (var. African tall) and cassava + cowpea (var. CO 5) were assigned to sub plot. Disease free setts of cassava were planted at a spacing of 90 x 90 cm. Two rows of intercrops were sown in between the rows of main crop as additive intercropping series. Seeds of fodder maize and cowpea were dibbled in lines at a spacing of 30 x 20 cm accommodating two rows of intercrops between the rows of cassava. A fertilizer dose of 60:60:150 NPK Kg ha -1 was uniformly applied to all the plots. Fertilizers were applied only to the main crop. The results revealed that irrigation at 0.80 IW / CPE ratio registered the highest tuber yield. However this yield was comparable with the tuber yield obtained with irrigation scheduled at 0.60 IW / CPE ratio. The economic evaluation revealed that the BC ratio was higher surface irrigation scheduled at 0.80 IW / CPE ratio followed by irrigation scheduled at 0.60 IW / CPE ratio and were comparable. Among the intercropping systems, sole cassava recorded the highest tuber yield and BC ratio followed by cassava intercropped with cowpea and both were comparable. Cassava intercropped with maize recorded the least tuber yield and BC ratio.
Ice crystals images from Optical Array Probes: classiﬁcation with Convolutional Neural Networks

Although airborne optical array probes (OAP) have existed for decades, our ability to maximize extraction of meaningful morphological information out of the images produced by these probes has been limited by the lack of automatic, unbiased and reliable classiﬁcation tools. The present study describes a methodology for automatic ice crystal recognition using innovative machine learning. Convolutional Neural Network (CNN) have recently been perfected for computer vision and have been chosen as the method to achieve the best results together with the use of ﬁnely tuned dropout layers. For the 5 purposes of this study, CNN has been adapted for the Precipitation Imaging Probe (PIP) and the 2DS-Stereo Probe (2DS), two commonly used probes that differ in pixel resolution and measurable maximum size range for hydrometeors. Six morphological crystal classes have been deﬁned for the PIP and eight crystal classes and an artifact class, for the 2DS. The PIP and 2DS classiﬁcations have ﬁve common classes. In total more than 8000 images from both instruments have been manually labelled, thus allowing for the initial training. For each probe the classiﬁcation design tries to account for the three primary ice crystal 10 growth processes: vapor deposition, riming and aggregation. We included classes such as fragile aggregates and rimed aggregates with high intra-class shape variability and commonly found in convective clouds. The trained network is ﬁnally tested through human random inspections on actual data to show its real performance in comparison to what humans can achieve. deﬁnitions.
N EURAL I NSTRUCTION C OMBINER

Instruction combiner (IC) is a critical compiler optimization pass, which replaces a sequence of instructions with an equivalent and optimized instruction sequence at basic block level. There can be thousands of instruction-combining patterns which need to be frequently updated as new coding styles/idioms/applications and novel hardware evolve over time. This results in frequent updates to the IC optimization pass thereby incurring considerable human effort and high software maintenance costs. To mitigate these challenges associated with the traditional IC, we design and implement a Neural Instruction Combiner (NIC) and demonstrate its feasibility by integrating it into the standard LLVM compiler optimization pipeline. NIC leverages neural Seq2Seq model techniques for generating optimized encoded Intermediate Representation (IR) sequence from the unoptimized encoded IR sequence. To the best of our knowledge, ours is the first work demonstrating the feasibility of a neural instruction combiner built into a full-fledged compiler pipeline. Given the novelty of this task, we built a new dataset for training our NIC neural model. We show that NIC achieves exact match results percentage of 72% for optimized sequences as compared to traditional IC and Bleu precision score of 0.94, demonstrating its feasibility in a production compiler pipeline.
Bayesian Generational Population-Based Training

Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittleness of RL algorithms to core hyperparameters and network architecture choice. Furthermore, non-stationarities such as evolving training data and increased agent complexity mean that different hyperparameters and architectures may be optimal at different points of training. This motivates AutoRL, a class of methods seeking to automate these design choices. One prominent class of AutoRL methods is Population-Based Training (PBT), which have led to impressive performance in several large scale settings. In this paper, we introduce two new innovations in PBT-style methods. First, we employ trust-region based Bayesian Optimization, enabling full coverage of the high-dimensional mixed hyperparameter search space. Second, we show that using a generational approach, we can also learn both architectures and hyperparameters jointly on-the-fly in a single training run. Leveraging the new highly parallelizable Brax physics engine, we show that these innovations lead to large performance gains, significantly outperforming the tuned baseline while learning entire configurations on the fly. Code is available at https://github.com/xingchenwan/bgpbt.
Sources and processes of water-soluble and water-insoluble organic aerosol in cold season in Beijing, China

Abstract. Water-soluble and water-insoluble organic aerosol (WSOA and WIOA) constitute a large fraction of fine particles in winter in northern China, yet our understanding of their sources and processes are still limited. Here we have a comprehensive characterization of WSOA in cold season in Beijing. Particularly, we present the first mass spectral characterization of WIOA by integrating online and offline organic aerosol measurements from high-resolution aerosol mass spectrometer. Our results showed that WSOA on average accounted for 59 % of the total OA and comprised dominantly secondary OA (SOA, 69 %). The WSOA composition showed significant changes during the transition season from autumn to winter. While the photochemical-related SOA dominated WSOA (51 %) in early November, the oxidized SOA from biomass burning increased substantially from 8 % to 29 % during the heating season. Comparatively, local primary OA dominantly from cooking aerosol contributed the major fraction of WSOA during clean periods. WIOA showed largely different spectral patterns from WSOA which were characterized by prominent hydrocarbon ions series and low oxygen-to-carbon (O/C = 0.19) and organic mass-to-organic carbon (OM/OC = 1.39) ratios. The nighttime WIOA showed less oxidized properties (O/C = 0.16 vs. 0.24) with more pronounced polycyclic aromatic hydrocarbons (PAHs) signals than daytime, indicating the impacts of enhanced coal combustion emissions on WIOA. The evolution process of WSOA and WIOA was further demonstrated by the triangle plot of f44 (fraction of m/z 44 in OA) vs. f43, f44 vs. f60, and the Van Krevelen diagram (H/C vs. O/C). We also found more oxidized WSOA and an increased contribution of SOA in WSOA compared with previous winter studies in Beijing, indicating that the changes in OA composition due to clean air act have affected the sources and properties of WSOA.
Ground motion emissions due to wind turbines: observations, acoustic coupling, and attenuation relationships

Abstract. Emissions from wind turbines (WT) cover a wide range of infrasound and ground motions. When they are perceived as immissions by local residents, they can become a source of disturbance or annoyance. To mitigate such disturbances, it is necessary to better understand and, if possible, suppress WT-induced emissions. Within the project Inter-Wind we record and analyze ground motion signals in the vicinity of two wind farms on the Swabian Alb in southern Germany, simultaneously with acoustic and meteorological measurements, as well as psychological surveys done by cooperating research groups. The investigated wind farms consist of 3 and 16 WTs, respectively, and are located on the Alb peneplain at 700–800 m height, approximately 300 m higher than the two municipalities considered in our study. Our main aim is to better understand reasons why residents may be affected from WT immissions. Known ground motions include vibrations due to eigenmodes of the WT tower and blades, and the interaction between the passing blade and the tower, causing signals at constant frequencies below 12 Hz. In addition, we observe signals in ground motion recordings at frequencies up to 90 Hz which are proportional to the blade-passing frequency. We can correlate these signals with acoustic recordings and estimate sound pressure to ground motion coupling transfer coefficients of 3–16.5 µms-1Pa-1. Sources for these emissions are the WT generator and possibly the gearing box. The identification of such noise sources can help to find measures to reduce disturbances in order to increase the public acceptance of WTs. Residents perceive more disturbance at the location where the wind farm is closer to the municipality (approximately 1 km). However, there is also a major railway line which produces higher vibration and infrasound signal amplitudes compared to the WTs. Along the measurement lines the decay rate of the WT-induced ground motions is determined for a damping relation proportional to 1/rb. We find frequency-dependent b values for different scenarios at our geological setting of Jurassic limestone on marl, sandstone, and Quaternary deposits. These damping relationships can be used to estimate emissions in the far field and to plan mitigation strategies.
Reflections on complementarities in capital formation and production: Tangible and intangible assets across Europe

This paper investigates capital formation with a view at various tangible and intangible assets across Europe. We assess to what extent there are complementarities among different asset types, i.e. investment in one asset type affecting the productivity of an investment in another. Using novel datasets at both macro and firm level, we estimate translog production functions at different aggregation levels to assess complementarities both at the within-country and the within-sector level. We stress that our estimates do not have a causal interpretation. At macro-level, we show some evidence for complementarities between tangibles and intangibles and, when further splitting the assets into subclasses, between National Accounts intangibles (mainly intellectual property products) and non-National Accounts intangibles (mainly training, orgnanisational capital, branding and new products and designs). At firm-level data, we explore more disaggregated asset classes and find that investing simultaneously in software, training of employees, and business process improvements is associated with better firm performance. Within a sector, firms tend to choose to invest either in own R&D or in embedded R&D and training. Our analysis demonstrates that policy support that aims at stimulating investment only in certain assets (while excluding others) may fall short in unlocking its own full potential. The emphasis should rather be on addressing investment bottlenecks arising from market imperfections, while remaining non-dicriminatory with a view at what sort of capital deepening is envisaged, i.e. leaving it to the firm to find the most appropriate mix of assets. Accordingly, investment support programmes should generally be open to include intangible assets, notably also those not captured as such in the National Accounts, such as training and organisational capital, and help addressing challenges arising from collateralizing such investments. a sensitivity check for our results, we estimate the macro- and the firm-level models using both stocks and investments.
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients

Abstract. We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59–73∘ N, 97–169∘ E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr). Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown. We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha−1), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill. , Pinus L. , Picea A. Dietr. , Abies Mill. , Salix L. , Betula L. , Populus L. , Alnus Mill. , and Ulmus L. In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data. They are available at https://doi.org/10.1594/PANGAEA.943547 (Miesner et al., 2022).
Modeling approaches for atmospheric ion–dipole collisions: all-atom trajectory simulations and central field methods

Abstract. Ion–dipole collisions can facilitate the formation of atmospheric aerosol particles and play an important role in their detection in chemical ionization mass spectrometers. Conventionally, analytical models, or simple parametrizations, have been used to calculate the rate coefficients of ion–dipole collisions in the gas phase. Such models, however, neglect the atomistic structure and charge distribution of the collision partners. To determine the accuracy and applicability of these approaches under atmospheric conditions, we calculated collision cross sections and rate coefficients from all-atom molecular dynamics collision trajectories, sampling the relevant range of impact parameters and relative velocities, and from a central field model using an effective attractive interaction fitted to the long-range potential of mean force between the collision partners. We considered collisions between various atmospherically relevant molecular ions and dipoles and charged and neutral dipolar clusters. Based on the good agreement between collision cross sections and rate coefficients obtained from molecular dynamics trajectories and a generalized central field model, we conclude that the effective interactions between the collision partners are isotropic to a high degree, and the model is able to capture the relevant physicochemical properties of the systems. In addition, when the potential of mean force is recalculated at the respective temperatures, the central field model exhibits the correct temperature dependence of the collision process. The classical parametrization by Su and Chesnavich (1982), which combines a central field model with simplified trajectory simulations, is able to predict the collision rate coefficients and their temperature dependence quite well for molecular systems, but the agreement worsens for systems containing clusters. Based on our results, we propose the combination of potential of mean force calculation and a central field model as a viable and elegant alternative to the brute force sampling of individual collision trajectories over a large range of impact parameters and relative velocities.
Accessibility in MOOCs: the stakeholders’ perspectives

Accessibility in MOOCs: the stakeholders’ perspectives Book Section How to cite: Iniesto, Francisco; McAndrew, Patrick; Minocha, Shailey and Coughlan, Tim (2022). Accessibility in MOOCs: the stakeholders’ perspectives. In: Rienties, Bart; Hampel, Regine; Scanlon, Eileen and Whitelock, Denise eds. Open world learning: research, Innovation and the Challenges of High-Quality Education. London: Routledge, pp. 119–130.
Environmental Controls on Observed Spatial Variability of Soil 1 Pore Water Geochemistry in Small Headwater Catchments 2 Underlain with Permafrost 3

Soil pore water (SPW) chemistry can vary substantially across multiple scales in Arctic permafrost 18 landscapes. The magnitude of these variations and their relationship to scale are critical considerations for 19 understanding current controls on geochemical cycling and for predicting future changes. These aspects are especially 20 important for Arctic change modelling where accurate representation of sub-grid variability may be necessary to 21 predict watershed scale behaviours. Our research goal was to characterize intra- and inter-watershed soil water 22 geochemical variations at two contrasting locations in the Seward Peninsula of Alaska, USA. We then attempt to 23 establish which environmental factors were important for controlling concentrations of important pore water solutes 24 in these systems. The SPW geochemistry of 18 locations spanning two small Arctic catchments were examined for 25 spatial variability and its dominant environmental controls. The primary environmental controls considered were 26 vegetation, NO3 of alders and and of litter NO3 solubility Identification of the dominant controls on current SPW hydrogeochemistry allows for qualitative prediction of future geochemical trends in small Arctic catchments that are likely to experience warming and permafrost thaw. controls In evaluate (SPW) with then to with Ba concentrations. Together, these suggest that SO 4 from another source (likely, atmospheric deposition or 424 sulfidic mineral oxidation), is suppressing barite dissolution, and thus, is reducing dissolved Ba concentrations. Barite 425 solubility can exhibit a redox dependence if conditions are sufficiently reducing to reduce SO 4 to sulphide. This shifts Sr demonstrated greater accumulation potential than Mg, likely via greater affinity of cation 449 exchange surfaces for Ca and Sr compared to Mg. Mineral solubility limitations were the primary controls on Al 450 (Al(OH) 3(am) ), Fe (Fe(OH) 3(am) ), Ba (barite), and Si (chalcedony) concentrations. This suggests that the SPW 451 concentrations of these constituents will remain stable until those mineral phases are exhausted or soil pore 452 hydrochemistry changes sufficiently to alter the solubility of those mineral phases. Changes in redox condition would 453 significantly alter Fe(OH) 3(am) solubility, whereas changes in pH conditions would significantly alter Al(OH) 3(am) and 454 Fe(OH) 3(am) solubility.
Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau

Abstract. The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.
Response to Community Comment 1 (CC1) on: “Threshold in orbital forcing for onset of African Humid Periods decreases with increasing greenhouse gases” by Mateo Duque-Villegas et al.

We thank Dr. Zhengyu Liu very much for carefully reading our manuscript and for providing con-structive remarks. Below we respond to every comment (blue font, our response in black font). [0] This paper discusses the simulation of the North Africa monsoon and vegetation in the last 190,000 years. In particular, it highlights that an increased GHG lowers the threshold for Africa Humid Period (AHP) in the vegetation coverage. The paper is interesting and should be published. But, the paper would be more interesting to readers if some points can be clarified before publication.