Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of nitrification inhibitors on the nitrous oxide (N2O) release from agricultural soils—a review

The use of nitrification inhibitors (NI) is a technique which is able to improve N fertilizer use efficiency, to reduce nitrate leaching and to decrease the emission of the climate‐relevant gas N₂O simultaneously, particularly in moderately fertilized agricultural systems adapted to plant N demand. The ammonia monooxygenase (AMO) is the first enzyme which is involved in the oxidation of NH$ _4^+ $ to NO$ _3^ - $ in soils. The inhibition of the AMO by NIs directly decreases the nitrification rate and it reduces the NO$ _3^- $ concentration which serves as substrate for denitrification. Hence, the two main pathways of N₂O production in soils are blocked or their source strength is at least decreased. Although it has been shown that archaea are also able to oxidize NH₃, results from literature suggest that the enzymatic activity of NH₃oxidizing bacteria is the most important target for NIs because it was much stronger affected. The application of NIs to reduce N₂O emissions is most effective under conditions in which the NI remains close to the N ‐ fertilizer. This is the case when the NI was sprayed on mineral ‐ N fertilizer granules or thoroughly mixed with liquid fertilizers. Most serious problems of spatial separation of NI and substrate emerge on pasture soils, where N₂O hotspots occur under urine and to a lesser extent under manure patches. From the few studies on the effect of different NI quantities it seems that the amount of NI necessary to reduce N₂O emissions is below the recommendations for NI amounts in practice. NIs can improve the fertilizer value of liquid manure. For instance, the addition of NIs to slurry can increase N uptake and yield of crops when NO$ _3^ - $ ‐ N leaching losses are reduced. It has clearly been demonstrated that NIs added to cattle slurry are very effective in reducing N₂O as well as NO emissions after surface application and injection of slurry into grassland soils. In flooded rice systems NIs can reduce CH₄emission significantly, whereas the effect on CO₂emission is varying. On the other hand, as an effect of the delay of nitrification by NIs, NH₃emission might increase when N fertilizers are not incorporated into the soil. As compared to other measures NIs have a high potential to reduce N₂O emissions from agricultural soils. Further, no other measure has so consistently been proofed according its efficiency to reduce N₂O emissions. From the published data [Akiyama et al. (2010) and more recent data from the years 2010–2013; 140 data sets in total] a reduction potential of approx. 35% seems realistic; however, further measurements in different management systems, particularly in regions with intense frost/thaw cycles seem necessary to confirm this reduction potential. These measurements generally should cover a whole annual cycle.
Effect of storage of olive mill wastewaters on hydroxytyrosol concentration

This investigation was designed to determine the effect of olive mill wastewaters (OMW) storage on the continuous extraction of potentially high-added-value phenolic compounds from these effluents. Particular emphasis was made on the extraction of hydroxytyrosol, one of the major compounds occurring in OMW. The hydroxytyrosol concentration was constant in OMW supplemented with 10% ethanol during storage. However, with 5 and 0% ethanol, a noticeable increase was observed in the hydroxytyrosol concentration. At time zero, the main polyphenols detected in two samples of fresh OMW without ethanol addition were hydroxytyrosol, hydroxytyrosol-4-β-glucoside, 3,4-dihydroxyphenylacetic acid, p-coumaric acid, ferulic acid, oleuropein, and some other unidentified compounds. After 5 months of storage, significant accumulation of hydroxytyrosol was observed. The corresponding concentration increased from 0.98 to 3.5 g/L for the OMW sample collected in November 2004 and from 0.77 to 3.1 g/L for the sample collected in January 2006. However, the concentrations of the other phenolic compounds were markedly decreased. Furthermore, it was deduced that the OMW storage facilitates the continuous extraction procedure and improves the extraction yield of hydroxytyrosol. This last parameter increased from 85.5 to 96.8%.
Charge relaxation process dominates contact charging of a particle in atmospheric conditions

A new model is proposed for a mechanism dominating the charge generated on a particle due to impact or contact with metal plate. In this model, the impact/contact charging of a particle is determined as the remaining charge by the charge relaxation process due to gaseous discharge in atmospheric conditions; here, some difficulties of the simple condenser model are avoided. The model prediction is in good agreement with the `equilibrium charges` given as the result of impact charging experiments. Furthermore, the impact charging experiments conducted in Ar gas indicated that the impact charging characteristics changed corresponding to the breakdown limit potential of the environmental gas; this was also predicted quantitatively by the model. As a result of the proposal of this new model, hereafter in the case of treating charge generating phenomena due to impact or contact, in atmospheric conditions at least, consideration of the charge relaxation process due to gaseous discharge should be essential.
Modeling gross primary production and ecosystem respiration in a semiarid grassland of Mongolia

Abstract We constructed an empirical model in which the rates of gross primary production (GPP) and ecosystem respiration (Reco) were calculated using meteorological, soil and plant variables, based on our previous measurements of carbon dioxide (CO2) fluxes using a closed-chamber technique in Mongolian grasslands. In the previous studies, we determined GPP and Reco using transparent and opaque chambers and simultaneously measured the environmental variables inside and beside the chambers. These measurements allowed us to quantify direct and accurate relationships between the rates of GPP and Reco and their controlling factors. The GPP, Reco and their difference net ecosystem CO2 exchange (NEE) were computed in a semiarid grassland site of Mongolia for the growing seasons of 2010 and 2011 using observed values of photosynthetically active radiation, air and soil temperatures, vapor pressure deficit, soil water content and estimated values of aboveground biomass from a remotely sensed vegetation index. Model performance was validated by comparing the modeled and observed NEE values using an eddy covariance technique. Results showed that the model successfully reproduced the magnitude and seasonal variations of the observed NEE, and regression analysis showed reasonable agreement in both years. Cumulative rates of GPP and Reco during the plant-growing season (May − September) were 200.3 and 210.5 g carbon (C) m−2 in the dry year (2010) and 342.3 and 300.1 g C m−2 in the wet year (2011), respectively. This result indicates that drought was more effective in reducing CO2 uptake by the plant than in reducing ecosystem respiration. Consequently, the grassland ecosystem was a net carbon source during the growing season in 2010 and a net carbon sink in 2011.
The QSPR Study of Water - Octanol Partition Coefficients for a Series of 24 Benzene Derivatives

A series of 24 benzene derivatives are commonly used as solvents or reactants in the chemical process of synthesis. For this reason, a circumstantial study of skin permeability (or biologic membranes) to such classes of substances is enforced. This paper presents a correlation between the log P partition coefficients for these compounds and the descriptors characterizing the molecular structures. These descriptors were calculated using quantum ab initio molecular methods as Polarizable Continuum Model (PCM) for water and 1-octanol solvents which feign biological phase (the interface between water medium and cellular membrane). The partition coefficients analysis depends on the energies of solvation, molecular shape descriptors (area / volume of molecular cavity and Connolly area).
Target Transformed Regression for Accurate Tracking

Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos. Recent anchor-free trackers provide an efficient regression mechanism but fail to produce precise bounding box estimation. To address these issues, this paper repurposes a Transformer-alike regression branch, termed as Target Transformed Regression (TREG), for accurate anchor-free tracking. The core to our TREG is to model pair-wise relation between elements in target template and search region, and use the resulted target enhanced visual representation for accurate bounding box regression. This target contextualized representation is able to enhance the target relevant information to help precisely locate the box boundaries, and deal with the object deformation to some extent due to its local and dense matching mechanism. In addition, we devise a simple online template update mechanism to select reliable templates, increasing the robustness for appearance variations and geometric deformations of target in time. Experimental results on visual tracking benchmarks including VOT2018, VOT2019, OTB100, GOT10k, NFS, UAV123, LaSOT and TrackingNet demonstrate that TREG obtains the state-of-the-art performance, achieving a success rate of 0.640 on LaSOT, while running at around 30 FPS. The code and models will be made available at https://github.com/MCG-NJU/TREG.
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study

This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the motivation behind on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We provide a novel connection on how label smoothing affects distributions of semantically similar and dissimilar classes. Then we propose a metric to quantitatively measure the degree of erased information in sample's representation. After that, we study its one-sidedness and imperfection of the incompatibility view through massive analyses, visualizations and comprehensive experiments on Image Classification, Binary Networks, and Neural Machine Translation. Finally, we broadly discuss several circumstances wherein label smoothing will indeed lose its effectiveness. Project page: http://zhiqiangshen.com/projects/LS_and_KD/index.html.
The complete classification for quantified equality constraints

We prove that QCSP$(\mathbb{N};x=y\rightarrow y=z)$ is PSpace-complete, settling a question open for more than ten years. This completes the complexity classification for the QCSP over equality languages as a trichotomy between Logspace, NP-complete and PSpace-complete. We additionally settle the classification for bounded alternation QCSP$(\Gamma)$, for $\Gamma$ an equality language. Such problems are either in Logspace, NP-complete, co-NP-complete or rise in complexity in the Polynomial Hierarchy.
Machine Learning Assisted Prediction of Cathode Materials for Zn‐Ion Batteries

Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety. However, a lack of reliable cathode materials has largely pledged their applications. Herein, a machine learning (ML)‐based approach to predict cathodes with high capacity (>100 mAh g−1) and high voltage (>0.5 V) is developed. Over ≈130 000 inorganic materials from the materials project database are screened and the crystal graph convolutional neural network based ML approach is applied with data from the AFLOW database, the combination of these two gives rise to ≈80 predicted cathode materials. Among them, ≈10 cathode materials have been experimentally discovered previously, which agrees remarkably well with experimental measurements, while ≈70 new promising candidates have been predicted for further experimental validations. The authors hope this study could spur further interests in ML‐based advanced theoretical tools for battery materials discovery.
Commonsense Spatial Reasoning for Visually Intelligent Agents

Service robots are expected to reliably make sense of complex, fast-changing environments. From a cognitive standpoint, they need the appropriate reasoning capabilities and background knowledge required to exhibit human-like Visual Intelligence. In particular, our prior work has shown that the ability to reason about spatial relations between objects in the world is a key requirement for the development of Visually Intelligent Agents. In this paper, we present a framework for commonsense spatial reasoning which is tailored to real-world robotic applications. Differently from prior approaches to qualitative spatial reasoning, the proposed framework is robust to variations in the robot's viewpoint and object orientation. The spatial relations in the proposed framework are also mapped to the types of commonsense predicates used to describe typical object configurations in English. In addition, we also show how this formally-defined framework can be implemented in a concrete spatial database.
SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods. The code is publicly available in https://github.com/tanmay-ty/SpectralNET.
Embedded Self-Distillation in Compact Multibranch Ensemble Network for Remote Sensing Scene Classification

Remote sensing image classification task is challenging due to the characteristics of complex composition, so different geographic elements in the same image will interfere with each other, resulting in misclassification. To solve this problem, we propose a multibranch ensemble network to enhance the feature representation ability by fusing final output logits and intermediate feature maps. However, simply adding branches will increase the complexity of models and decline the inference efficiency. To reduce the complexity of multibranch network, we make multibranch share more weights and add feature augmentation modules to compensate for the lack of diversity caused by weight sharing. To improve the efficiency of inference, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main branch. Through optimizing with SD, the main branch will have close performance as an ensemble network. In this way, we can cut other branches during inference. In addition, we simplify the process of SD and totally adopt two loss functions to self-distill the logits and feature maps. In this article, we design a compact multibranch ensemble network, which can be trained in an end-to-end manner. Then, we insert an SD method on output logits and feature maps. Our proposed architecture (ESD-MBENet) performs strongly on classification accuracy with compact design. Extensive experiments are applied on three benchmark remote sensing datasets, AID, NWPU-RESISC45, and UC-Merced with three classic baseline models, VGG16, ResNet50, and DenseNet121. Results prove that ESD-MBENet can achieve better accuracy than previous state-of-the-art complex deep learning models. Moreover, abundant visualization analyses make our method more convincing and interpretable.
Piezoelectric metastructures for simultaneous broadband energy harvesting and vibration suppression of traveling waves

In this paper, we explore an electromechanical metastructure consisting of a periodic array of piezoelectric bimorphs with resistive-inductive loads for simultaneous harvesting and attenuation of traveling wave energy. We develop fully coupled analytical models, i.e. an electroelastic transfer matrix method, and exploit both locally-resonant and Bragg band gaps to achieve a multifunctional metastructure which is capable for maximum energy conversion and vibration mitigation in a broadband fashion. Our analytical and numerical results show that the proposed metastructure can achieve energy harvesting efficiency up to 95% at the local resonance frequency of 3.18 kHz, while reaching about 51% at 5.8 kHz near the upper limit of the Bragg band gap. The broadband vibration mitigation performance based on 50% power attenuation is predicted as 1.8 and 1.1 kHz in the vicinity of the band gaps. The theoretical frameworks and the applicability of the proposed metastructure are validated using a full-scale experimental setup.
Channel Measurement and Path-Loss Characterization for Low-Terahertz Indoor Scenarios

TeraHertz (THz) communications are envisioned as a promising technology, owing to its unprecedented multiGHz bandwidth. In this paper, wideband channel measurement campaigns at 140 GHz and 220 GHz are conducted in indoor scenarios including a meeting room and an office room. Directional antennas are utilized and rotated for resolving the multi-path components (MPCs) in the angular domain. Comparable path loss values are achieved in the 140 and 220 GHz bands. To investigate the large-scale fading characteristics for indoor THz communications, single-band close-in path loss models are developed. To further analyze the dependency on the frequency, two multi-band path loss models are analyzed, i.e., alpha-beta-gamma (ABG) and multi-frequency CI model with a frequency-weighted path loss exponent (CIF), between which the ABG model demonstrates a better fit with the measured data. Moreover, a coherent beam combination that constructively sums the signal amplitudes from various arrival directions can significantly reduce the path loss, in contrast with a non-coherent beam combination.
Sunflower Cake as a Substitute for Groundnut Cake in Commercial Broiler Chicks Diets

The study was carried out to investigate the effects of replacing groundnut cake (GC) with decorticated sunflower cake (DSC) on broiler chicks performance. A total of 72 one-day-old unsexed broiler chicks (Ross 308) were used. The experiment lasted 6 weeks (0-42 day). Three experimental diets were supplied ad-libitum. The GC in broiler chicks starter (260g/Kg) and finisher (185g/Kg) diets was replaced with DSC at 50 and 100% on an isocaloric and isonitrogenous basis. Replacement of GC with DSC at 50 and 100% had no significant effect (P>0.05) on body weight gain, feed consumption and feed efficiency of broiler chicks during starter, finisher and the whole period. It was concluded that DSC can replace up t o 100% of GC in broiler chicks starter and finisher diets.
Security and Machine Learning Adoption in IoT: A Preliminary Study of IoT Developer Discussions

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, it is crucial to ensure IoT devices follow strict security requirements. At the same time, the prevalence of IoT devices offers developers a chance to design and develop Machine Learning (ML)-based intelligent software systems using their IoT devices. However, given the diversity of IoT devices, IoT developers may find it challenging to introduce appropriate security and ML techniques into their devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers’ security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. First, we collect around 53K IoT posts (questions + accepted answers) from SO. Second, we tokenize each post into sentences. Third, we automatically identify sentences containing security and ML-related discussions. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML-related discussions. There is no overlap between security and ML-related discussions, i.e., IoT developers discussing security requirements did not discuss ML requirements and vice versa. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.
The Formation of Carboxylic Acids during Kraft and Kraft-Anthraquinone Pulping of Birch Wood

The formation of carboxylic acids was followed during kraft and kraft-anthraquinone (AQ) pulping of birch wood. In all 26 hydroxy monocarboxylic acids and 16 dicarboxylic acids were identified in addition to acetic and formic acids. Their total amount corresponded to 20% (kraft pulping) and 18% (kraft-AQ pulping) of the wood weight. Five of these acids have not earlier been detected in the black liquors. Acetic acid was formed rapidly at the initial stage of pulping, whereas the amounts of the other acids increased continuously without any distinct culmination period. In the presence of AQ the formation of lactic, 2-hydroxybutanoic and isosaccharinic acids was depressed, whereas increased amounts were recorded for glycolic, 3-deoxytetronic and 3-deoxypentonic acids.