Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of different pesticide application methods on spray deposits, residues and biological efficacy on strawberries

In agricultural food production, positive results can be obtained by using appropriate pesticides through the application of appropriate methods. In this study, we aimed to determine the effects of different spraying methods on spray deposits and drift, pesticide residues and biological efficacy on strawberries. For this purpose, strawberries were sprayed with dicofol by broadcast and band spraying. The effectiveness of different application methods was studied on 09 May, 2007 and 16 April, 2008. Broadcast sprayings were applied via hollow cone nozzles (HC) and air-assisted spinning cage nozzles (ASC). Band spraying was applied via flat fan nozzles (FF). Pesticide deposits on leaf surfaces, ground, drift and pesticide residues on strawberries were analyzed with Gas Chromatography/Electron capture detector (GC/ECD). Strawberries were analyzed based on dicofol residues up to 6 days after spraying. The results obtained showed that the highest pesticide deposits on leaf surfaces and also biological efficiency were obtained with FF in 2007 and 2008. The lowest pesticide deposits on ground were obtained by HC and the highest deposit of drift was obtained by ASC in 2007 and 2008. According to Commission Directive 2000/42/EC, the MRL’s of dicofol on strawberry is 0.02 mg/kg. In this study, residues on strawberries for all types of nozzles were below the suggested maximum residue level (MRL) of dicofol by Commission Directive 2000/42/EC in 2007 and 2008 .
Effect of Perceptual Training on the Production of English Tense-Lax Vowels by Native Chinese Speakers

The current study investigated the effect of percep-tual training on the production of English tense-lax vowels by native Chinese speakers. 16-session phonetic category identification training was conducted, and the tokens from 10 English tense-lax vowels produced by Chinese speakers were recorded before and after training. A series of acoustic measures and analyses demonstrated that there was no significant formant quality change for the Chinese speakers after training, and the formant trajectories of individual vowels did not become more native-like either. Although small changes in vowel duration were found after training, the conclusion that training generally had limited effects on Chinese speakers’ vowel production was clear.
Prediction Interval Construction for Multivariate Point Forecasts Using Deep Learning

It has been demonstrated that deep learning can in certain instances outperform traditional statistical methods at forecasting. This outperformance, however, does not address the challenge of quantifying forecast uncertainty (prediction intervals). Artificial neural networks often do not have probability distributions linked to their point forecasts, which complicates the construction of prediction intervals. In this paper, we explore computational methods of artificially deriving said probability distributions and constructing prediction intervals. The point forecasts, and the associated constructed prediction intervals are compared to those produced by means of the oft-preferred traditional statistical counterparts. Our finding is deep learning outperforms (or at the very least is competitive to) the former. We focus on three deep learning architectures, namely, cascaded neural networks, reservoir computing and long short-term memory recurrent neural networks.
Real-time long-range road estimation in unknown environments

Real-time road estimation is important for dynamic path planning of autonomous driving, especially when construction sites or accidents occur along the road. Among the existing practices, main pain points are strongly dependent on lane markings and brightness. In addition, roads with a variable number of lanes are difficult to predict. To address these issues, we fuse the data sampled from camera, lidar, radar and predict the lanes 90m ahead of the ego vehicle in the top-view perspective by using a deep neural network. Our method is evaluated on real urban and highway scenes under good and bad weather conditions at different daytime, the results showed that our approach can handle roads with a variable number of lanes or roads with small T-intersections. Furthermore, the method can also deal with incomplete or missing lane markings at 166.66 fps.
Fault Detection Method Based on Multi-scale Convolutional Neural Network for Wind Turbine Gearbox

This paper is concerned with the fault detection problem of planetary gearbox in wind energy conversion systems (WECSs). An effective method based on empirical mode decomposition (EMD) and multi-scale convolutional neural network (MSCNN) is proposed. Specifically, the non-stationary vibration signals of the gearbox are decomposed by using the EMD, so that its signal-to-noise ratio (SNR) and characteristic information are improved. Then, a hierarchical convolutional neural network is applied to adaptively extract multi-scale features from the decomposed signal components. Finally, a binary classifier based on cross entropy is employed to automatically realize the classification of the obtained multi-scale features. The effectiveness and superiority of the proposed method are verified on the experiments with vibration data sets from a true WECS.
Efficient Buffer Overflow Detection on GPU

Rich thread-level parallelism of GPU has motivated co-running GPU kernels on a single GPU. However, when GPU kernels co-run, it is possible that one kernel can leverage buffer overflow to attack another kernel running on the same GPU. There is very limited work aiming to detect buffer overflow for GPU. Existing work has either large performance overhead or limited capability in detecting buffer overflow. In this article, we introduce GMODx, a runtime software system that can detect GPU buffer overflow. GMODx performs always-on monitoring on allocated memory based on a canary-based design. First, for the fine-grained memory management, GMODx introduces a set of byte arrays to store buffer information for overflow detection. Techniques, such as lock-free accesses to the byte arrays, delayed memory free, efficient memory reallocation, and garbage collection for the byte arrays, are proposed to achieve high performance. Second, for the coarse-grained memory management, GMODx utilizes unified memory to delegate the always-on monitoring to the CPU. To reduce performance overhead, we propose several techniques, including customized list data structure and specific optimizations against the unified memory. For micro-benchmarking, our experiments show that GMODx is capable of detecting buffer overflow for the fine-grained memory management without performance loss, and that it incurs small runtime overhead (4.2 percent on average and up to 9.7 percent) for the coarse-grained memory management. For real workloads, we deploy GMODx on the TensorFlow framework, it only causes 0.8 percent overhead on average (up to 1.8 percent).
Integration of Results From Convolutional Neural Network in a Support Vector Machine for the Detection of Atrial Fibrillation

Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The first method is a convolutional neural network (CNN) trained on modified frequency slice wavelet transform (MFSWT) data. The second is a support vector machine (SVM) classifier trained on multiple AF features data. The third method is an SVM trained on the same feature set but extended by the predictive probability of the CNN. The proposed method (the third one) achieved the highest detection accuracy. MIT-BIH AF database was used as a training set with an accuracy of 97.87% for 30-s ECG episodes and 96.09% for 10-s ECG episodes from fivefold cross-validation. The trained model was tested on the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 database, achieving an accuracy of 93.21% for 30-s episodes and 93.03% for 10-s ECG episodes. When tested on the China Physiological Signal Challenge (CPSC) 2018 database, the corresponding accuracies were 98.48% and 98.61%. The results on the wearable ECGs from a clinical AF patient were 99.21% and 97.04%. We retrained the model on the PhysioNet/CinC Challenge 2017 data set and tested on the other database to explore the generalization ability of the proposed method. Corresponding test results on the MIT-BIH AF database showed accuracies of 96.84% and 95.13%, on the CPSC 2018 database were 96.21% and 98.45%, on the wearable ECGs were 99.08% and 96.43%. The results proved that the proposed method could provide high accuracy and reliable recognition for AF events.
Fast Minimization of Polynomial Decomposition using Fixed-Polarity Pascal Transforms

Polynomials can be represented as a weighted sum of various powers of binomials where the weights are spectral coefficients and the associated powers of binomials are basis functions. The minimization problem is concerned with finding a set of basis functions that result in a maximum number of zero-valued spectral coefficients, or alternatively, wherein the spectral vector has a norm that is as close to zero as possible. One application of this minimization problem includes compression of signals that are represented with fitted polynomials. This problem can be considered to be a higher-radix generalization of the fixed-polarity Reed-Muller minimization problem since polynomial decomposition can be efficiently accomplished using the properties of the fixed-polarity family of Pascal transforms. We devise an efficient decomposition technique based on properties of the Pascal transform and we formulate a heuristic minimization algorithm to search for the minimal decomposition. Experimental results are provided that compare the quality and performance of the heuristic search to an exhaustive search for the minimal decomposition. The experimental results indicate that finding such a minimal decomposition can be achieved in a practical amount of runtime.
Comparison of anoxic granulation in USB reactors with various inocula

Anoxic granulation process with four different inocula was monitored in a laboratory post-denitrification up-flow sludge bed (USB) reactor. Wastewater containing 20 mg L−1 NO3-N and methanol as an organic carbon source was used. Gradual increase of mass volumetric loading (Bv) and hydraulic loading (γ) resulted in spontaneous granulation of anoxic biomass both from flocculant activated sludge and from anaerobic granulated sludge. With flocculant activated sludge as the inoculum, anoxic granules sedimentation properties and maximum loadings of the USB reactor depended on the sludge volume index (SVI) of the inoculum. Bv,max achieved in the USB reactor with flocculant inoculum from a municipal wastewater treatment plant (SVI = 208 mL g−1) was only 4.2 kg of COD per m3 per day and 0.7 kg of NO3-N per m3 per day. Bv,max using flocculant inoculum from an industrial wastewater treatment plant (SVI = 170 mL g−1) was 8.1 kg of COD per m3 per day and 1.35 kg of NO3-N per m3 per day. With anaerobic granulated inoculum (SVI range 8–11 mL g−1), markedly higher loadings in the USB reactor and lower SVI values of anoxic granulated biomass were achieved. Values of Bv,max were in the range of 16.1–22.4 kg of COD per m3 per day and of 2.7–3.7 kg of NO3-N per m3 per day (depending on the inoculum and the granulation procedure). It was proved that anaerobic granulated sludge is not just an inoculum, it is also a carrier for new denitrification biomass.
Robust Function and Sensor Design Considering Sensor Measurement Errors Applied to Automatic Emergency Steering

Vehicular safety functions that take over the control during dangerous driving situations increase automotive safety. As such functions use the measurements of sensors in order to determine the driving situation, unavoidable sensor measurement errors can have a negative impact on both the safety and the satisfaction of the customer. In this paper, it is shown how a new methodology for the robust design of sensors and functions in vehicular safety considering sensor measurement errors that has already been applied to design an automatic emergency braking (AEB) system can also be applied to design an automatic emergency steering (AES) system. Based on a stochastic model, we formulate the robust design as optimization problems, whose solution yields the optimal parameters for the AES system with respect to a probabilistic quality measure. The probabilistic quality measure is defined similarly to that for the robust design of the AEB system and a closed-form expression is derived for it in case of circular vehicle shapes and an emergency steer intervention with constant lateral acceleration. For more complex vehicle shapes and emergency steer interventions, an approximation of the probabilistic quality measure by a Monte Carlo simulation is proposed in this paper, which leads to a robust design which is based on simulations of the vehicular safety system under design and applicable to other vehicular safety systems as well.
Oxidation Equilibrium of Iron in Borosilicate Glass

Ferrous/ferric equilibria were determined in alkali-alkaline-earth borosilicate glass as a function of temperature, oxygen partial pressure, and glass composition. Expected linear relations are found between log(Fe2+/Fe3+) and log(pO2) or 1/T. The slopes of the correlation with 10g(pO2) are near the expected value of -0.25, but are found to decrease with increasing temperature. Reaction enthalpies determined from the correlation with 1/T of –100 to –116 kJ/mol are similar to those reported for other silicate glasses. The ferroudferric equilibrium is not dependent on total iron content in the range 0.5 to 0.09% Fe2O3. More reducing conditions are required at lower temperatures to stabilize the amber chromophore. The ferroudferric equilibria are correlated to the number of bridging and nonbridging oxygen ions in the glass. The results suggest that the oxidation-reduction reaction can be written as: Fe2++ (1/4)O2+ (3/2)O2-= FeO2−
Robustness of Neural-Network-based Nonlinear Iterative Learning Control

The purpose of this work is to develop a robust iterative learning control for nonlinear systems based on neural networks. In order to introduce the robustness to the control scheme the problem of accurate estimation of uncertainties associated with the black-box type model is concerned. An uncertainty of the system is derived in terms of the variance of the model output prediction using a concept of Fisher information matrix well-known in the optimum experimental design theory. Once the bounds of the system response are estimated, they can be directly applied during training of the learning controller by a rigorous definition of the penalty cost function. Then, a neural controller is suitably adopted to the effective design of iterative learning control for nonlinear systems. The proposed approach is experimentally verified on the example of a magnetic levitation system.
Exploring Hybrid Linguistic Feature Sets to Measure Filipino Text Readability

Proper identification of the difficulty level of materials prescribed as required readings in an educational setting is key towards effective learning in children. Educators and publishers have relied on readability formulas in predicting text readability. While these formulas abound in the English language, limited work has been done on automatic readability assessment for the Filipino language. In this study, we build upon the previous works using traditional (TRAD) and lexical (LEX) linguistic features by incorporating language model (LM) features for possible improvement in identifying readability levels of Filipino storybooks. Results showed that combining LM predictors to TRAD and LEX, forming a hybrid feature set, increased the performances of readability models trained using Logistic Regression and Support Vector Machines by up to $\approx$ 25% – 32%. From the results of performing feature selection using Spearman correlation and Information Gain on the feature set, we found out that traditional, numeric based features such as word counts and polysyllable word counts are still contributive towards accurate identification of readability levels in Filipino although not by itself. Future directions of the study include extracting more diverse feature sets such as syntactic and morphological predictors.
Position Report Enhancement Using Bayesian Estimator

In this article, a Bayesian estimator for a target position report is proposed. It is based on a maximum a priori algorithm, where the user's device knowledge about its location is used to deduce the prior probability density function. The algorithm does not require knowledge about the signal and noise levels, meaning, noninformative priors are used. It is well known that with a higher number of antenna elements in an array, narrower beams can be formed. In beamforming, task narrow beams are useful for serving many users simultaneously, whereas in direction-of-arrival (DOA) estimation, we are not interested in narrow beams as such; instead, estimation accuracy is important. So, reducing the number of antenna elements used for DOA estimation is beneficial from a system complexity point of view. In this article, signal source location report is used to enhance the estimated DOA, for the task, MAP estimator is developed. We will show that in the case of small array size and large array covariance matrix error values, the proposed estimator is the only one capable of improving the prior knowledge about the transmitter when comparing it with other popular algorithms, such as the maximum likelihood estimator and Root MUSIC. The algorithm could be used in a variety of different scenarios, but its advantages emerge in the case of complex signal propagation environments, such as urban canyons and large airports.
Parametric Modeling of the Cost of Power Plant Projects

Estimating a realistic budget for a complex power plant projects is always challenging, which results in cost overruns of such projects worldwide. Many factors affect the cost of power plant projects. Among others, quantifying the effects of the project’s specific attributes and the country’s economic parameters are always ignored in the existing literature. Thus, the broader aim of this study is to analyze the effects of the project’s specific attributes and the country’s economic parameters on allocating the costs of different power plant projects. Accordingly, this study first analyzes the development trends of power plant projects in Bangladesh and determines the factors affecting power plant projects’ costs using parametric analysis. Following that, the effects of project attributes and the country’s economic parameters on project budgets are quantified using multiple linear regression models. Finally, a parametric model is developed for predicting the cost of a power plant project. The study outcomes will help policy planners or top-level decision-makers prepare budgets for future power plant projects in Bangladesh and economically similar countries.
Speaker Identification and Its Application to Social Network Construction for Chinese Novels

Character is one of the three elements of a novel, and conversation is an important way to describe characters. The personality, emotion, and interpersonal relationships of characters are reflected in conversations. Thus, extracting conversations, speakers and other conversation elements from novels are crucial for character analysis and content understanding. We start with Jin Yong’s novels, annotate the largest corpus for speaker identification with 9721 quotes, propose a machine learning-based speaker identification method, and design feature templates showing a good performance. For the application of speaker identification, we construct the social network of characters in Jin Yong’s novels based on dialogue chain, which lays the foundation for the analysis of the relationship between characters in long text novels.
Analytical Method for Determination of Some Aroma Compounds on White Wines by Solid Phase Microextraction and Gas Chromatography

A method for analyzing volatiles such as alcohols (1-octen-3-ol), aldehydes (trans-trans-2,4-decadienal, trans-2-nonenal), terpenols (linalool, terpineol, and oxides), and isoprenoids (β-damascenone, β-ionone, 1,1,6-trimethyl-1,2-dihydronaphtalen and vitispirane) on white wines using solid phase microextraction has been developed. The analytes were extracted directly from the headspace of a constant volume of wine using 7 different fibers coated with different stationary phases compared with a classic dichloromethane extract. The best extraction efficiency was obtained with a divinylbenzene/Carboxen/polydimethylsiloxane, 50/30 μm. Sample volume, time, and temperature of the extraction were optimized and the matrix effect studied. The limits of quantification varied between 0.013 and 1.81 μg/L. The r-values for linearity studies varied between 0.9761 and 0.9999. Among the compounds measured, some are related to the aroma of young white wine, such as linalol, terpineol, β-damascenone, whereas others, for example, 1-octen-3-ol, trans-trans-2,4-decadienal, trans-2-nonenal, 1,1,6-trimethyl-1,2-dihydronaphtalene, and vitispirane, are related to evolution characteristic of white wines.
Drivers of Innovation Ambidexterity on Small Medium Enterprises (SMEs) Performance

The development of the broiler poultry industry has experienced a significant increase along with the increasing public demand for meat consumption. However, in 2019, many broiler poultry farmers are forced to close down their businesses because the price of broilers in living conditions continues to be below the Production Cost. Further, broiler poultry farmers also experience obstacles including unpredictable price fluctuations and intense competition. Innovation ambidexterity is the innovation capability in managing exploration and exploitation simultaneously to improve firm performance. Several influential factors in increasing innovation ambidexterity include technological capability, owner-manager characteristic, and environmental dynamism. This study aims to develop a model that describes innovation ambidexterity in broiler poultry farmers to improve firm performance. This study also analyzes the factors that can influence innovation ambidexterity and its effect on firm performance. This research mainly uses quantitative methods for its analysis. The sampling technique uses non-probability sampling with convenience sampling technique. Data collection was conducted by distributing 223 questionnaires to broiler poultry farmers in West Java. The analysis technique uses Partial Least Square - Structural Equation Modeling (PLS-SEM) which includes measurement model analysis and structural model analysis. The results of this study indicate that innovation ambidexterity has a direct influence on firm performance, while the influence of environmental dynamism on firm performance is partly mediated by innovation ambidexterity. In addition, technological capability and owner-manager characteristics have a positive influence on innovation ambidexterity and have an indirect influence on firm performance. In the near future, implications of this study will be discussed in a separate manuscript.