Standard Inputs Research Articles

Soil Properties and the Technical Efficiency of Paddy Production in Malaysia: An Application of Stochastic Frontier Analysis

This study investigates the impact of soil properties on technical efficiency in paddy production, focusing on Malaysia’s two main paddy granaries: KADA and IADA BLS. These granaries play a significant role in Malaysia’s paddy production. The research employed three different models, with productivity as the dependent variable. In models 1 and 2, standard physical inputs were used as independent variables. Model 3, however, augmented the inputs with the addition of soil property variables. The empirical results revealed significant impacts from fertiliser, pesticide and land inputs, with the exception of land in model 1, where its coefficient was not statistically significant. Additionally, soil pH and the calcium+magnesium/potassium ratio emerged as crucial factors affecting paddy productivity. Importantly, incorporating soil property variables into the analysis resulted in a relatively lower technical inefficiency estimate. These insights are invaluable for policy formulation, highlighting key inputs that can optimise paddy production. The study’s findings regarding soil properties are particularly relevant for refining strategies in national paddy development programs, emphasising the importance of sustainable soil management. Looking ahead, the study advocates for comprehensive technical efficiency estimations that account for all forms of heterogeneity, including weather conditions, to ensure more accurate and insightful assessments.

Utjecaj mineralogije gline na nedreniranu posmičnu čvrstoću pomoću pokusa s padajućim šiljkom

Consistency limits are the basic parameters used as standard inputs for any soil investigation program to evaluate the geotechnical properties of fine-grained soils. The primary objective of this study is to investigate the variations in the undrained shear strength of clays with different plasticity properties based on the fine content, clay mineralogy, and water absorption capacity. To achieve this objective, 33 Fall cone tests were conducted on low-, high-, and very high-plastic clay mixtures. Various blends of different plasticity were ensured by preparing mixtures of Na-montmorillonite (NaM)/Ca-montmorillonite (CaM), Na-montmorillonite (NaM)/Kaolinite (K), and Na-montmorillonite (NaM)/Sepiolite (S). According to the experimental results, the highest liquid limit (LL) was obtained in 100 % NaM clay at 255.07 %, whereas the lowest LL was obtained in 100 % S clay at 33.55 %. Similarly, the highest undrained shear strength was obtained at 160 kPa at 30 % water content in the 100 % NaM clay, whereas the lowest undrained shear strength was obtained at 10 kPa in the 100 % S clay at the same water content. Consequently, based on an experimental framework, empirical equations were obtained which could be used to establish the relationships among cone penetration-water content, liquid limit-clay content, and undrained shear strength-cone penetration.

Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks

We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for representations learned by deep ResNets. We then use these models to characterize distributions of representations across learned classes. Surprisingly, we find that classes in the investigated models are not fitted in a uniform way. On the contrary: we uncover two groups of classes that are fitted with markedly different distributions of representations. These distinct modes of class-fitting are evident only in the deeper layers of the investigated models, indicating that they are not related to low-level image features. We show that the uncovered structure in neural representations correlate with memorization of training examples and adversarial robustness. Finally, we compare class-conditional distributions of neural representations between memorized and typical examples. This allows us to uncover where in the network structure class labels arise for memorized and standard inputs.

Energy efficiency model-based Digital shadow for Induction motors: Towards the implementation of a Digital Twin

The 4th industrial revolution requires the tracking and optimization of energy consumption using an intelligent energy management system (IEMS). Such a system needs accurate real-time energy information to operate industrial machines, where Induction Motors (IMs) represent 42.2% of the global energy consumption. This paper addresses the problems of data acquisition for Induction motors in the context of Industry 4.0 (I4.0) where precision, real-time constraints, and optimal operations play a major role in decision-making. A new vision of Digital Shadow (DS) is proposed for the energy efficiency (EE) of IMs. A hybrid model consisting of a data-driven model and a physics-based model is developed to represent the machine efficiency information in real-time (RT). The proposed method is developed based on a two-stage procedure. Firstly, the IM EE model is established by considering Stator joule losses, core losses, rotor joule losses, friction, and windage losses, in addition to the stray losses to create an improved model. This was established based on the double cage model where core loss resistance is added. Secondly, the machine’s losses and efficiency are visualized using the 8 electrical circuit parameters (ECP) from the double cage model in addition to the rated speed and test temperature. The parameters estimation in RT incurred complexities and required the use of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based modeling. The proposed model was developed using Fuzzy Logic Toolbox and Neuro-Fuzzy Designer app from MATLAB by training Sugeno systems. 8 ANFIS MATLAB models are trained to estimate each of the 8 ECP from the double cage model by using standard inputs. The training and testing dataset is constituted by the experimental data of the ECPs were calculated using the FSOLVE function to solve the nonlinear system formed by 60 motors from 8 manufacturers’ data such as voltage, number of pole-pairs, rated output power, rated torque, current, starting current, maximum torque and the power factor. Finally, the proposed method is validated experimentally using a 1.5 KW, 400/230 V, 50 Hz squirrel cage induction motor (SCIM) for linear speed control. The EE, Torque, and losses are visualized through the proposed model using MATLAB/SIMULINK. The mean value of the RMSE for the eight estimated ECPs are 7.57e-05, and 5.73e-01 respectively for training and testing while the mean values of the MAE are 2.06e-05, and 3.79e-01 respectively for training and testing. The errors of the EE estimation w.r.t the measured EE at rated condition, are RMSE = 0.205, and MAE = 0.1671. These results show that the proposed model can be implemented in the industry to monitor the machine EE and its losses.

Survey Paper on Development of ROS for Fault Detection of Underwater Cables

An introduction to ROS, an open source robot operating system, is given in this paper. In terms of process management and scheduling, ROS is not an operating system. This study explains how Robotic Operating System (ROS) can be used to control items (such as vehicles) remotely and cautiously without human intervention at the location. Instead, it gives heterogeneous computing clusters a structured communication layer on top of the host operating system. This document gives a quick explanation of how ROS fits into the current robot software architecture and how we may utilize it for AUVs (Automated under water vehicle). One of the media that connects the entire world to the internet is optical cable, which is typically installed underground or under water. As a result, it is challenging to inspect them thoroughly because it costs more to do so. To address this issue, we are presenting a solution that involves developing a robotic operating system that would assist in checking the underwater/underground cables. To put this into practice, we have been utilizing VMWare Workstation to virtually install Ubuntu OS, where we will be installing ROS packages, with the ROS-Gazebo toolbox serving as one of the primary tools. We are testing the implemented software with the standard inputs. We are using light radiation as the primary factor to assess the condition of the optical cable.

Rapid and integrated petroleum systems analysis with dynamic gas fraction and flexural reconstructions: Application to hydrocarbon charge and commodity risk

The most time consuming and computationally intensive steps in petroleum systems analysis involve building heat flow boundary conditions that are consistent with regional geologic observations, solving the transient heat equation, and translating a range of hydrocarbon charge predictions from source intervals into prospect specific predictions that can be used for decision making in oil and gas exploration. Furthermore, three-dimensional regional flexure, which can have a significant impact on paleo-migration pathways, often is not included in the critical step of translating charge calculations into trap specific predictions due to the time-consuming nature of map preparation. In this paper, an efficient and integrated petroleum systems analysis (IPA) workflow is presented with a new fast, semi-analytical, decoupled approach for solving the transient heat equation that is fully integrated with a rapid inversion methodology for transient heat flow boundary conditions and a 3D regional flexural reconstruction that uses standard basin modeling inputs and requires little to no additional map preparation. A charge-to-trap model is also described that translates hydrocarbon expulsion calculations with dynamic parameterized gas fractions that vary with transformation ratio into trap specific predictions using flexurally restored migration polygons. This fully integrated and efficient approach enables rapid interactive investigation of uncertainty space and fast probabilistic modeling that is ideal for evaluating hydrocarbon charge and commodity risk in data-sparse frontier basins. The applicability of this approach is demonstrated for assessing charge and commodity risk with a case study from the Colorado Basin, Argentina, whereby two alternative hydrocarbon accumulation hypotheses that postulate that traps are filled to structural spill with undersaturated oil are tested for reservoirs located in the upper Colorado Formation that are charged by postulated Aptian and Neocomian source rocks. This application indicates that it is unlikely that the tested traps are filled to structural spill with undersaturated oil, and how incorrect conclusions on hydrocarbon commodity may be reached if 3D regional flexure is not considered during petroleum systems analysis. Finally, it is also shown how this integrated and efficient approach can be implemented with low-cost computational platforms such as Python Anaconda or in spreadsheet programs that empower modelers to rapidly translate their ideas into benchmarked prototypes that can be immediately leveraged by the geoscience community (www.github.com/eakneller/ipatoolkit3D).

Characteristics analysis of muscle function network and its application to muscle compensatory in repetitive movement

In rehabilitation medicine, highly repetitive rehabilitation training can help patients recover their physical and cognitive abilities. When the patient undergoes rehabilitation training, the muscles that are supposed to function are unable to perform their functions properly, resulting in other muscles compensating or even replacing their functions, a phenomenon is known as muscle compensation. Analysis of muscle compensation allows the study of mechanisms of central nervous system control of movement. Muscle functional networks quantify the functional connectivity between synergistic muscles. They can identify the frequency characteristics between muscles regulated by standard neural inputs, which can be used as a valuable feature to analyze changes in muscle compensation. Therefore, this paper analyzes muscle compensation induced by repetitive fatiguing movements of the arm (with rest). Our work recruited 30 subjects to perform four bouts of isotonic exercise and collected Surface EMG and kinematic data. The muscle function network was used to extract the muscle compensatory characteristics of the subjects, and the neuromodulatory mechanisms of muscle compensation were explored at a macroscopic scale by linear fitting of the characteristics. The results showed that BOUT2 had the highest slope of the feature fit in the four bouts, with an average increase of 90.20% compared to BOUT1, an average decrease of 80.70% compared to BOUT3, and an average decrease of 71.56% compared to BOUT4.

Design and development of accessible video chat application for people with disabilities

Communication has been a struggle for everyone since the covid outbreak and in the aftermath, people have had to get accustomed to video conferencing applications. However people with physical or mental limitations are still unable to use video conferencing apps and their interfaces. This necessitates the development of web-based video chat applications. These applications can aid those who are unable to communicate verbally and/or operate using standard mouse and keyboard inputs, but yet need to feel close to others when they are apart. The proposed application incorporates various accessibility features such as speech-to-text and text-to-speech, gaze tracking and pictorial speech interfaces. It enables individuals with disabilities to participate in virtual meetings on an equal footing with their peers. The goal is to remove barriers and promote inclusiveness in remote work and collaboration for all users, regardless of their abilities using this application.

Geography-based Neural Networks for the Simulation of Air Pollution

Most environmental variables, including air pollution, are characterized by high variability in time and space. The classical approach to modelling these variables is developing physically based models with high data and computational requirements or their surrogate version, often implemented through a neural structure. We suggest that adding the geographical coordinates to the other standard inputs of the neural network architectures provides more accurate results without additional measurement costs and with minor increments of computer time. We demonstrate the advantages of the proposed approach in an application to Northern Italy.

Consideration of risk in the implementation of probabilistic prognostications of hotel revenues and expenses

PurposeThe purpose of this paper is to be the first empirical article to provide necessary standard deviation inputs for adoption in probabilistic prognostications of hotel revenues and expenses, i.e. prognostications that consider risk. Commonly accepted methodologies to develop hotel financial projections resulting in point estimates of upcoming performance have been perceived as egregiously insufficient because they do not consider risk in lodging investments. Previous research has recommended the use of probabilistic methodologies to address this concern, and it has been recommended that analysts use Monte Carlo simulation. This methodology requires the estimation of standard deviations of specific, future hotel revenue and expense items, and this paper provides such inputs based on a large sample of actual, recent data.Design/methodology/approachThis study provides actual standard deviations using a sample of recent hotel profit and loss (P&L) statements for over 3,000 hotels (Over 19,000 P&L statements) to provide analysts with empirically-supported standard deviations that may be applied to Uniform System of Accounts for the Lodging Industry (USALI) hotel revenues and expenses in hotel financial (revenue and expense) prognostications.FindingsFindings are presented for standard deviations based on typical line items as defined in the USALI, and these findings may be used by practitioners as inputs for hotel financial projections. Findings also include that hotel revenue items generally have higher standard deviations than expense items. Findings are presented in detail in the manuscript, including overall findings, as well as findings based on hotel class.Practical implicationsRather than practitioners adopting standard deviations of hotel revenue and expense line items based on guesswork or judgment, which is the current “state of the art” in hotel financial projections, this paper provides practitioners with actual standard deviations which may be adopted in probabilistic prognostications of hotel revenues and expenses.Originality/valueThis paper may be the first to provide practitioners with actual standard deviations, based on typical USALI line items, for adoption in probabilistic prognostications of hotel revenues and expenses.

ESG complementarities in the US economy

This paper investigates ESG from the perspective of changes in input elasticities of substitution and complementarity. Rather than compute these elasticities from the cost function, we compute them from the Input Distance Function (IDF). Our data are from Refinitiv Eikon Datastream database. We focus on the US economy due to her global role in the world economy and hence spillover effects of uncertainties on the rest of the world. The data consist of 5,798 companies comprising 38 US industries that span for 12 years from 2009 to 2020 and include: (i) financial data on sales, capital and employees; (ii) two financial ratios and (iii) three main ESG indicators. We compute Antonelli Elasticity of Complementarity (AEC) and Allen-Uzawa Elasticity of Substitution (AES) from the translog of IDF function. We find that the standard inputs have positive AEC elasticities; however, ESG cross-elasticities exhibit negative signs, classifying them as q-substitutes. Therefore, an increase in one of the ESG values leads to a decrease in the marginal value of the other. On the other hand, AES elasticities have a negative sign only for the Governance-Environment “doublet'; the rest of the pairs are positive implying that they are p-complements.

Influence of bushing flexibility and its constitutive behavior on the performance of suspension system

We study the effect of strut top mount bushing flexibility and its constitutive law on the ride comfort performance of a quarter car suspension model. A fractional Kelvin–Voigt model has been employed to capture the viscoelastic behavior of the top mount bushings. To study the effectiveness of the suspension system, we have considered sinusoidal and standard (motion over bumps) road inputs. For the sinusoidal input, the frequency response function for both the traditional and fractional Kelvin–Voigt models has been obtained using the harmonic balance method. However, we have resorted to numerical simulations for the standard base inputs for which a reduced-order system of ODEs has approximated the fractional derivative term. Convergence of the reduced system of ODEs is established by comparing the numerical results for sinusoidal forcing with those obtained analytically. We find that the bushing modeled as a traditional Kelvin–Voigt element increases the response amplitude in the resonance zone, while for higher frequencies, the response reduces. Hence, the inclusion of bushing flexibility is equivalent to decreasing the damping in the suspension. These observations also remain valid for the fractional Kelvin–Voigt model, but the effect is less pronounced than in the traditional model. Ride comfort for standard road profile is studied in terms of performance indices which suggests that a fractional Kelvin–Voigt bushing improves the ride comfort. Accordingly, bushing flexibility with an appropriate constitutive law should be accounted in the design of a suspension system for ride comfort. • Effect of strut top mount bushing (STMB) flexibility on ride comfort is studied. • Fractional Kelvin–Voigt model is employed for the viscoelastic behavior of the STMB. • Studied the suspension system response on sinusoidal and standard road inputs. • Ride comfort has been studied in terms of transmissibility and performance indices. • We find that STMB flexibility improves ride comfort especially during transient loads.

Performance of Multilayered Nanocoated Cutting Tools in High-Speed Machining: A Review

In machining processes, cutting tools play a dominant role in producing quality products. The quality of finished goods is directly related to the cutting tool condition. Several types of research have been carried out in cutting tool condition monitoring. On the other hand, the manufacturing industries should be aware of the cutting tool selection, operating conditions, and performance of cutting tools. This article emphasizes the performance of coated cutting tools and tool materials for various machining operations. Nowadays, the nanocoating of CNC tool inserts increases the wear resistance, vibration emissions, metal removal rate, etc. These coating techniques influence the manufacturing industry to increase the productivity and quality of the finished goods and reduce the machining cost. The performance of thin film multilayered coatings such as TiN, TiAlN, AlTiN, Ti, and TiCN on plain silicon carbide tool inserts is revealed by the researchers to guide the manufacturing industry for proper tool selection and standard machining inputs for metal removal operation. The influence of coating material such as TiBN, TiN, TiAlN, and CrAlSiN in cutting tools leads to increase the life time of the cutting tools, which decreases the material sticking and cutting forces. Titanium carbo nitride is wear-resistant and corrosion-resistant. Compared to TiCN, TiAlN is harder due to the higher hardness of 32 GPa. This article concludes the material selection based on the work piece material which yields good metal removal with less cutting forces. The article concludes the cutting material selection based on the work piece for machining operations.

Generative workplace and space planning in architectural practice

Generative design is emerging as an important approach for design exploration and design analysis in architectural practice. At the interior design scale, although many approaches exist, they do not meet many requirements for implementing generative design in practice. These requirements include the need for end-user accessible tools and skills, rapid execution, the use of standard inputs and outputs, and being scalable and reusable. In this paper, we describe a hybrid process that uses both space allocation and shape grammar algorithms to solve workplace and space planning interior design problems. Space allocation algorithms partition spaces according to program requirements while shape grammar automates the placement of inventory and the production of high-resolution drawings. We evaluate using three real world example projects how this hybrid approach meets the identified requirements of generative space planning in architectural practice.

Workers’ tenure and firm productivity: New evidence from matched employer‐employee panel data

AbstractUsing rich longitudinal matched employer‐employee data on Belgian firms, we explore the impact of workers’ tenure on firm productivity. To do so, we estimate production functions augmented with firm‐level measures of tenure. We deal with the endogeneity of standard inputs and tenure, which arises from unobserved firm heterogeneity and reverse causality, by applying a modified version of Ackerberg et al.’s (2015) control function method, which explicitly removes firm fixed effects. Consistently with recent theoretical predictions, our analyses point to positive, but decreasing, returns to tenure. We also find that the impact differs widely across several firm dimensions. Tenure is particularly beneficial for productivity in contexts characterized by a certain degree of routineness and low job complexity. Along the same lines, our findings indicate that tenure exerts stronger positive impacts in industrial and capital‐intensive firms, as well as in firms less reliant on ICT‐intensive and knowledge‐intensive processes.

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Research is needed to explore the limitations and potential for improvement of machine learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was launched in 2019. This effort was the largest building energy meter machine learning competition of its kind, with 4370 participants who submitted 39,403 predictions. The test dataset included two years of hourly whole building readings from 2380 meters in 1448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competition’s top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The errors are classified according to timeframe, behavior, magnitude, and incidence in single buildings or across a campus. The results show machine learning models have errors within a range of acceptability (RMSLEscaled = < 0.1) on 79.1% of the test data. Lower magnitude (in-range) model errors (0.1 < RMSLEscaled = < 0.3) occur in 16.1% of the test data. These errors could be remedied using innovative training data from onsite and Web-based sources. Higher magnitude (out-of-range) errors (RMSLEscaled > 0.3) occur in 4.8% of the test data and are unlikely to be accurately predicted.

Challenges of Effective Principals Leadership in School Improvement in Government Secondary Schools of Oromia Regional State, Ethiopia

This research study aimed at investigating varied challenges that principals have been facing in an endeavor of school improvement in terms of input, process and outputs in secondary Schools of Oromia Regional State, Ethiopia. In this study, a qualitative research approach was followed and as such the data were collected with the help of semi-structured interview guides and focus group discussions. The study employed clustering, stratified, random and purposive sampling techniques to select 6 zones of Oromia region out of 20; 80 sample secondary schools out of 30, to draw 45 interviewees and 36 discussants, respectively. In this way, the interviews were conducted with the interviewees and focus group discussions were carried out among the discussants. Data obtained from these informants were analyzed using thematic analysis and narrative methods and paraphrasing the results of the discussants. The study identified inactive involvement of key stakeholders in the schools’ affairs; a weak capacity building for the principals; poor effort and commitment of students for class attendance and improvement of academic results; shortages of standard facilities and inputs; weakness of the principals together with SIP committee in properly developing strategic plan of the schools were among the main challenges that hindered effectiveness of principals’ school leadership in overall improvement of the schools. Because of these and other factors a great majority of the schools stood at level two that is below the expected level (level three &amp; four ) in the study area. Based on the findings, it was recommended that principals should be equipped with basic knowledge and skills of school leadership, necessary facilities and inputs need to be fulfilled by the concerned bodies and stakeholders should be re-oriented in order to contribute in all possible aspect for betterment of school performance.

Improved Ke3 radiative corrections sharpen the K\u03bc2\u2013Kl3 discrepancy

The measurements of Vus in leptonic (Kμ2) and semileptonic (Kl3) kaon decays exhibit a 3σ disagreement, which could originate either from physics beyond the Standard Model or some large unidentified Standard Model systematic effects. Clarifying this issue requires a careful examination of all existing Standard Model inputs. Making use of a newly-proposed computational framework and the most recent lattice QCD results, we perform a comprehensive re-analysis of the electroweak radiative corrections to the Ke3 decay rates that achieves an unprecedented level of precision of 10−4, which improves the current best results by almost an order of magnitude. No large systematic effects are found, which suggests that the electroweak radiative corrections should be removed from the “list of culprits” responsible for the Kμ2–Kl3 discrepancy.

Conventional measures of intrinsic excitability are poor estimators of neuronal activity under realistic synaptic inputs.

Activity-dependent regulation of intrinsic excitability has been shown to greatly contribute to the overall plasticity of neuronal circuits. Such neuroadaptations are commonly investigated in patch clamp experiments using current step stimulation and the resulting input-output functions are analyzed to quantify alterations in intrinsic excitability. However, it is rarely addressed, how such changes translate to the function of neurons when they operate under natural synaptic inputs. Still, it is reasonable to expect that a strong correlation and near proportional relationship exist between static firing responses and those evoked by synaptic drive. We challenge this view by performing a high-yield electrophysiological analysis of cultured mouse hippocampal neurons using both standard protocols and simulated synaptic inputs via dynamic clamp. We find that under these conditions the neurons exhibit vastly different firing responses with surprisingly weak correlation between static and dynamic firing intensities. These contrasting responses are regulated by two intrinsic K-currents mediated by Kv1 and Kir channels, respectively. Pharmacological manipulation of the K-currents produces differential regulation of the firing output of neurons. Static firing responses are greatly increased in stuttering type neurons under blocking their Kv1 channels, while the synaptic responses of the same neurons are less affected. Pharmacological blocking of Kir-channels in delayed firing type neurons, on the other hand, exhibit the opposite effects. Our subsequent computational model simulations confirm the findings in the electrophysiological experiments and also show that adaptive changes in the kinetic properties of such currents can even produce paradoxical regulation of the firing output.

DMPP mitigates N2O emissions from nitrogen fertilizer applied with concentrated and standard vinasse

The application of vinasse, an organic byproduct of ethanol production, can enhance greenhouse gas emissions from N fertilizers, especially nitrous oxide (N2O). The objective of this work was to test the efficiency of the nitrification inhibitor (NI) 3,4 dimethylpyrazole phosphate (DMPP) in reducing N2O emissions from urea (UR) and ammonium nitrate (AN) applied with or without standard (V) or concentrated vinasse (CV). Two laboratory studies were conducted with repacked soil columns from a sugarcane field. Based on usual field rates, UR and AN were added at a rate equivalent to 100 kg N ha−1, V was added at 100 m3 ha−1 (51 kg N ha−1), and CV was added at 20 m3 ha−1 (Exp. 1: 35 kg N ha−1 and Exp. 2: 56 kg N ha−1). N2O emissions were measured for 75 and 61 days after the input of fertilizers and vinasses. When mineral fertilizers were compared, the N2O-N emission factors (EFs) were: 0.08% (AN) and 1.44% (UR). Combined with N fertilizers, vinasses (CV and V) greatly enhanced the N2O emissions from soil (ranging from 0.76% to 1.5% in one study and 1.37% to 6.27% in the other). In addition, CV + N fertilizer emitted more N2O than V + N fertilizer or N fertilizer alone. However, NI reduced N2O emissions by 80% on average for vinasses (CV and V) plus N fertilizer (AN and UR) and UR alone, but not for AN alone. Since both vinasses and mineral N fertilizers are standard inputs for sugarcane production and high N2O emissions are observed when they are applied in the same area, NI should be considered for mitigation of soil N2O emissions from sugarcane used for bioenergy.