Miracle-dz/UniFAR-FTUs huggingface dataset:
Agricultural efficiency, technical change and productivity in China

Economic reform in China helped transform the structure and volume of agricultural production and resulted in significant changes in efficiency and productivity. This article measures agricultural technical efficiency (TE) and total factor productivity (TFP) in China by including all producers in different groups operating under their own technologies. A metafrontier function approach is applied using a panel data set on 28 provinces during 1991–2005. The provinces are categorised into advanced and low-technology provinces. Based on the metafrontier estimation, TFP growth is decomposed into TE change (TEC), technical change (TC) and scale efficiency change (SEC). Our major findings indicate that TC contributed most to Chinese agricultural TFP growth throughout the period of study. SEC and TEC exhibited negative effects on TFP growth for the advanced and low-technology provinces respectively. Most of the advanced-technology provinces exhibited higher TE than the low-technology provinces. The comparatively low TE scores in the low-technology provinces imply that the low-technology provinces were operating far from the metafrontier. The results also show that labour and fertiliser still make important contributions to output, and thus improving the quality of farmers and applying modern physical inputs is also crucial to TFP growth.
Knowledge Reasoning Based on Neural Tensor Network

Knowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way to supplement these missing relationships. This paper attempts to explore the model complexity of neural tensor network, a very important method of knowledge reasoning, and the reasoning accuracy. By increasing the number of slices in the tensor network layer, the number of parameters to be trained by the model is increased, thereby increasing the complexity of the model. The experimental results show that the number of slices is improved, which is helpful to increase the reasoning accuracy of the model, while the time consumption does not show obvious growth. The accuracy of the model on WordNet and FreeBase increased 2% and 3.2% respectively.
Real-Time, Video-Rate and Depth-Resolved Imaging of Radio-Frequency Ablation Using All-Optical Ultrasound

Radio-frequency (RF)ablation is an effective minimally invasive treatment for atrial fibrillation, where RF fields are delivered by a catheter within the heart. In current clinical practice, this procedure is performed without real-time feedback about the viability of tissue beneath the ablation lesion. As a result, it can be challenging to perform complete ablation of the target tissue whilst avoiding damage to adjacent structures. Recently, all-optical ultrasound imaging, where ultrasound is both generated and detected using light, has been demonstrated as a viable imaging modality exhibiting high image resolution, strong miniaturisation potential, and resilience to interfererence by electromagnetic (EM)fields from the RF ablation catheter. In this work, the suitability of all-optical ultrasound to monitoring RF ablation lesion delivery is investigated using a bench-top imaging system. This system is capable of real-time, video-rate 2D imaging at an image depth of 15 mm, a resolution of 100 μm, a dynamic range of 35 dB, and a sustained frame rate of 9 Hz. In this paper we present the first real-time, video-rate all-optical ultrasound images acquired during RF ablation, and demonstrate how the imaging system was able to clearly visualise the lesion growth at a contrast of up to 30 dB. In addition, we demonstrated how lesion growth in inhomogeneous tissue can progress in an erratic fashion. High-quality imaging during treatment will enable optimisation of the ablation parameters during treatment, and will minimise both treatment times and complication rates.
The Efficiency of Australian Schools: Evidence from the NAPLAN Data 2009-2011

This study examines the technical efficiency of schools in Australia and its determinants using NAPLAN test results of about 6,800 schools in 2009-2011 and other information from the “My School” website. For each school, we use the average growth of test scores for the same students between 2009 and 2011 as the measure of the school's output and four input measures: the student-teacher ratios, student-non-teaching staff ratios, recurrent income per student and (averaged) capital expenditure per student. We are also able to compare schools by type: including whether or not the school is a public school or a private school, a single sex or co-educational schools, a primary or secondary school, or a school that provides both primary and secondary schooling. In addition we control for several other environmental indicators for each school including: an index of social and educational advantage, the proportion of school children who identify as an Aborigine or Torres Strait Islander, the proportion of students from a non English-speaking background, the proportion of students female, as well as the region, state and territory in which the school is located. We estimate that the average technical efficiency score of Australian schools is 59 per cent and find evidence of input congestion for all of the inputs studied. On average, the growth target for schools in the sample to reach the efficiency frontier is 100 NAPLAN points. Our results suggest that eliminating inputs congestion could, in theory, reduce expenditure per school student by A$2,000. At the primary level, Catholic and independent schools are less efficient than public schools, but this story is reversed at the secondary level. We also find that schools with students from more advantageous social and economic backgrounds and schools with higher ratios of students from non-English speaking backgrounds tend to be more efficient. The results are robust to the choices about how to construct the frontier (e.g., in aggregate or for disaggregates by school type) and to our treatments of output and super-efficiency.
Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.
FE approach with Green's function as internal trial function for simulating bioheat transfer in the human eye

Simulation of bioheat transfer in a human eye model is performed, using a newly developed Green’s-function-based finite element formulation, named HFS-FEM. Nonlinear radiation conditions are first treated by introducing an effective convection coefficient, and then two independent temperature fields are assumed within the element and along its boundary, respectively. Subsequently, a hybrid variational functional including the convection effect is constructed to guarantee the inter-element field continuity and to establish a linkage between the two independent fields. By virtue of the use of Green’s functions as trial functions, the resulting nonlinear system contains only the element boundary integrals and is solved with an iteration technique. The results obtained are compared with those from ABAQUS and a good agreement is observed. Subsequently, the effect of control parameters is investigated to determine the temperature variation in the eye model; the results show that the proposed hybrid computational method is an effective tool for investigating the thermal performance of the human eye.
Predictive Pulse Pattern Current Modulation Scheme for Harmonic Reduction in Three-Phase Multidrive Systems

 Abstract —The majority of the industrial motor drive systems are equipped with the conventional line-commutated front-end rectifiers, and being one of the main sources of harmonics in the power line. While a parallel combination of these drive units elevates current quality issues, a proper arrangement of them can lead to the cancellation of specific harmonics. This paper proposes a new cost-effective harmonic mitigation solution for multi-drive systems using a predictive pulse pattern current modulation control strategy. The proposed technique applies suitable interaction among parallel drive units at the rectification stage to synthesize sinusoidal input currents. The input voltage sensing is avoided in order to minimize the number of required sensors, and the grid synchronization also has been implemented based on a common Phase-Locked-Loop (PLL) using the DC-link capacitor voltage ripple. Experimental results validate the effectiveness of the proposed strategy.
Creative-Based Strategies in Small and Medium-Sized Cities: Key Dimensions of Analysis

Abstract The present article intends to contribute to the literature and research agenda regarding the importance of the creative economy as a driver for the revitalisation and development of small and medium-sized cities in Europe. Several studies argue that these territorial areas are necessarily doomed to failure and decline due to the lack of critical mass and density in terms of economic and institutional resources. However, some small towns are changing their development path towards a new ‘urban’ paradigm. The present research aims to identify and analyse a set of dimensions that have to be taken into consideration in the implementation of creative-based strategies in small and medium-sized cities, namely: governance factors, endogenous factors, and territorial embeddedness.
Optical loss estimation in a Bragg fiber

We present the fiber loss estimation, contour loss curves, and fiber loss spectra for the TE0μ mode transmission in a Bragg fiber; numerical results are especially focused on the TE01 mode. A fiber loss of the order of 0.01-0.1dB/km can be achieved using cladding material of 10 dB/km in loss. The loss of the hollow core fiber is simply formulated in terms of the constituting cladding losses, fiber structural parameters, and operation wavelength λ0 under the quarter-wave stack (QWS) condition. It is effective for decreasing the loss of the cladding lower-index layer to get a low-loss Bragg fiber. The fiber loss is minimized at an optimum cladding lower-index nb,opt. The nb,opt can be expressed by n∞b,opt, which is a function of cladding higher-index na, core index nc, and cladding layer loss ratio for a sufficiently large value of normalized core radius, rc/λ0, under the QWS condition. The optimized fiber loss is inversely proportional to the product of (na−nc)3/2 and (rc/λ0)3 for a sufficiently large rc/λ0.
The sources of volatile and fluid‐mobile elements in the Sunda arc: A melt inclusion study from Kawah Ijen and Tambora volcanoes, Indonesia

Subduction zone recycling of volatiles (H2O, Cl, S, F) is controlled by the nature of subducted materials and the temperature‐pressure profile of the downgoing slab. We investigate the variability in volatile and fluid‐mobile trace element enrichment in the Sunda arc using melt inclusion data from Kawah Ijen and Tambora volcanoes, together with published data from Galunggung, Indonesia. Combining our results with data from other arcs, we investigate the mobility of these elements during slab dehydration and melting. We observe correlations between Sr, H2O and Cl contents, indicating coupling of these elements during subduction zone recycling. Sulfur is more variable, and fluorine contents generally remain at background mantle values, suggesting decoupling of these elements from H2O and Cl. Partial melting and dehydration models constrain the source of Sr and the volatiles and suggest that the altered oceanic crust (AOC) is the main source of the hydrous component that fluxes into the mantle wedge, in agreement with thermo‐mechanical models. Sediment melt remains an important component for other elements such as Ba, Pb, Th and the LREE. The Indonesian volcanoes have variable concentrations of volatile and fluid‐mobile elements, with Kawah Ijen recording higher AOC‐derived fluid fluxes (Sr/Nd and H2O/Nd) compared to Galunggung and Tambora. Kawah Ijen has H2O/Ce ratios that are comparable to some of the most volatile‐rich magmas from other cold slab subduction zones worldwide, and the highest yet measured in the Sunda arc.
Azimuthal AVO inversion by simulated annealing

Fractures influence the permeability pathways and mechanical properties associated with a rock mass and therefore, are a crucial aspect in the characterization of the subsurface. In this study we develop an azimuthal AVO inversion algorithm using a simulated annealing optimization technique. The parameterization of the problem is in terms of an isotropic background with the inclusion of fractures through an addition of excess compliances to the medium. Preliminary inversion results demonstrate a reasonable estimate of the model parameters in addition to an excellent match between the data and synthetic data. Associated errors in the estimated model parameters are attributed to variable sensitivities of the model parameters to the objective function. Future work will attempt to address these issues through different parameterizations of the problem and additional constraints in the objective function.
Blur kernel estimate in single noisy image deblurring

Restoring blurred images is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely under constrained. Recently many single image blind deconvolution methods have been proposed, but these state-of-the-art single image deblurring techniques are still sensitive to image noise, and can degrade their performance rapidly especially when the noise level of the input blurred images increases. In this work, we estimate the blur kernel accurately by applying a series of directional low-pass filters in different orientations to the input blurred image, and effectively constructing the Radon transform of the blur kernel from each filtered image. Finally, we use a robust non-blind deconvolution method with outlier handling, which can effectively reduce ringing artifacts, to generate the final results. Our experimental results on both synthetic and real-world examples show that our method achieves comparable quality to existing approaches on blurry noisy-free images, and higher quality outputs than previous approaches on blurry and noisy images.
Triple-Frequency GPS Precise Point Positioning Ambiguity Resolution Using Dual-Frequency Based IGS Precise Clock Products

With the availability of the third civil signal in the Global Positioning System, triple-frequency Precise Point Positioning ambiguity resolution methods have drawn increasing attention due to significantly reduced convergence time. However, the corresponding triple-frequency based precise clock products are not widely available and adopted by applications. Currently, most precise products are generated based on ionosphere-free combination of dual-frequency L1/L2 signals, which however are not consistent with the triple-frequency ionosphere-free carrier-phase measurements, resulting in inaccurate positioning and unstable float ambiguities. In this study, a GPS triple-frequency PPP ambiguity resolution method is developed using the widely used dual-frequency based clock products. In this method, the interfrequency clock biases between the triple-frequency and dual-frequency ionosphere-free carrier-phase measurements are first estimated and then applied to triple-frequency ionosphere-free carrier-phase measurements to obtain stable float ambiguities. After this, the wide-lane L2/L5 and wide-lane L1/L2 integer property of ambiguities are recovered by estimating the satellite fractional cycle biases. A test using a sparse network is conducted to verify the effectiveness of the method. The results show that the ambiguity resolution can be achieved in minutes even tens of seconds and the positioning accuracy is in decimeter level.
Image Encryption Technique Combining Compressive Sensing with Double Random-Phase Encoding

A new image compression-encryption method based on compressive sensing and double random-phase encoding is proposed, which can complete image compression and encryption simultaneously. We utilize the hyperchaotic system to generate a measurement matrix and two random-phase masks first. Then the original image is measured by measurement matrix to accomplish encryption and compression at the same time, next the compressed image is reencrypted by the double random-phase encoding technique with the two random-phase masks, and lastly the resulting image is confused and diffused by using hyperchaotic system simultaneously. Some numerical simulations verify the validity and the reliability of the proposed algorithm.
Detecting cry1Ac by loop mediated isothermal amplification by SYBR green-I

Detection of Genetically Modified Organisms (GMOs) is an important part of GMO labelling, as without detection methods the traceability of GMOs would rely solely on documentation. Efficient detection strategies for GM crops need to be in compliance with regulatory frameworks and address consumer concerns. The DNA based techniques are currently the major detection methods that are widely used due to their ease and accuracy Loop mediated isothermal amplification (LAMP) is a simple, rapid, specific and cost effective nucleic acid amplification method. This method employs a DNA polymerase and a set of four specially designed primers that recognize a total of six distinct sequences on the target DNA. A set of six specific primers was designed to recognize six distinct sequences on the target cry1Ac gene, including a pair of inner primers, a pair of outer primers, and a pair of loop primers. The optimum reaction temperature and time were optimised to be 65°C for 60 min respectively and stained with SYBR Green –I for visual detection of GMO.
Micelle-Catalyzed Domain Swapping in the Cytoplasmic Domain of E. coli Rhomboid Protease

ii Acknowledgements.iii Statement of Contribution.iv List of Abbreviations.vii List of Figures.viii List of Tables.ix Chapter 1: Introduction.1 1.1 Domain Swapping.1 1.2 Domain Swapping and Deposition Diseases.2 1.3 Factors Affecting Domain Swapping Propensities.5 1.4 Domain Swapping Mechanisms.8 1.5 Domain Swapping Conditions.9 1.6 E coli Rhomboid Protease and its N-terminal Cytoplasmic Domain (NGlpG). 11 1.7 Thesis Goals.15 1.8 Theoretical Background for NMR and CD Experiments.17 1.8. 1 Investigating Protein Dynamics on the Picosecond to Nanosecond Timescale by Solution NMR.17 1.8. 2 Hydrogen Exchange Experiments.18 1.8. 3 Circular Dichroism (CD) Spectroscopy.21 Chapter 2: Materials and Methods.23 2.1 NGlpG Expression.23 2.2 Purification of NGlpG.24 2.3 SDS-PAGE.25 2.4 Determination of Protein Concentration.25 2.5 Circular Dichroism Spectroscopy.26 2.6 Thermal Denaturation Experiments.27 2.7 Domain Swapping Rate Measurements.29 2.8 Thermodynamics of Fos16-Catalyzed Domain Swapping.31 2.9 NMR Spectroscopy.32 2.10 Backbone Amide Hydrogen-Deuterium Exchange.32 2.11 Heteronuclear steady-state {H} N NOE.32 2.12 Determination of Kd for binding of Fos16 micelles to monomeric NGlpG.33
Intelligent system for non-technical losses management in residential users of the electricity sector

The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company.
An Alternative Approach to Estimate the Economic Loss of Porcine Epidemic Diarrhea (PED) via Data Envelopment Analysis: The Case in Taiwan

This study focuses on investigating how the PED (Porcine Epidemic Diarrhea) virus influenced the production efficiency of swine industry in Taiwan. A total 96 valid sample data were collected during March of 2014. The Data Envelopment Analysis (DEA) was adopted to evaluate production efficiency before, and after, the PED events. Results show that the PED events in Taiwan had weakened overall technical efficiency (TE) about 8.6%. Large scale farms, older farms, and Central area appeared to be the most heavily impacted. Meanwhile, the reproductive rate of Taiwanese hog farms was found to exhibit a polarization in that some farms were producing at a high efficiency, while others were not. Lastly, the percentage change in production efficiency in the DEA estimation are very close to the percentage changes in inventory on farms reported by government. This may imply that the DEA can be further discussed regarding the capability of estimation.