Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid one-step solvent-free acid-catalyzed mechanical depolymerization of pine 1 sawdust to high-yield water-soluble sugars 2 3

14 One-step absolute solvent-free acid-catalyzed mechanical depolymerization of pine 15 sawdust (PSD) and commercially available α-cellulose to water-soluble sugars was 16 carried out using ball milling. For comparison purposes, the commonly reported “solvent17 free” mechanocatalytic depolymerization of lignocellulose method, which normally 18 involves three steps (acid impregnation in solvent, vacuum drying, and mechanical 19 depolymerization of lignocellulose), was performed. The 3,5-dinitrosalicylic acid (DNS) 20 method was used to measure the total reducing sugar (TRS) of the obtained sugar solution, 21 and major monosaccharides in the solution were analyzed by capillary electrophoresis 22 (CE). More than 90% of the PSD became water-soluble through milling. Furthermore, 23 most of the PSD was converted into TRS in approximately 30 min, and the highest TRS 24 yield obtained was 31%, based on the dry mass. Interestingly, the TRS solutions obtained 25 from the processed PSD were much darker than those obtained from α-cellulose due to 26 the chromophores that formed during the depolymerization of lignin. 27
Enhancement of Illumination scheme for Adult Image Recognition

Biometric-based techniques have emerged as the most promising option for individual recognition. This task is still a challenge for computer vision systems. Several approaches to adult image recognition, which include the deep neural network and traditional classifier, have been proposed. Different image condition factors such as expressions, occlusion, poses, and illuminations affect the facial recognition system. A reasonable amount of illumination variations between the gallery and probe images need to be taken into account in adult image recognition algorithms. In the context of adult image verification, illumination variation plays a vital role and this factor will most likely result in misclassification. Different architectures and different parameters have been tested in order to improve the classification’s accuracy. This proposed method contains four steps, which begin with Fuzzy Deep Neural Network Segmentation. This step is employed in order to segment an image based on illumination intensity. Histogram Truncation and Stretching is utilized in the second step for improving histogram distribution in the segmented area. The third step is Contrast Limited Adaptive Histogram Equalization (CLAHE). This step is used to enhance the contrast of the segmented area. Finally, DCT-II is applied and low-frequency coefficients are selected in a zigzag pattern for illumination normalization. In the proposed method, AlexNet architecture is used, which consists of 5 convolutional layers, max-pooling layers, and fully connected layers. The image is passed through a stack of convolutional layers after fuzzy neural representation, where we used filter 8 × 8. The convolutional stride is fixed to 1 pixel. After every convolution, there is a subsampling layer, which consists of a 2×2 kernel to do max pooling. This can help to reduce the training time and compute complexity of the network. The proposed scheme will be analyzed and its performance in accuracy and effectiveness will be evaluated. In this research, we have used 80,400 images, which are imported from two datasets - the Compaq and Poesia datasets - and used images found on the Internet.
Active learning for classifying long‐duration audio recordings of the environment

This paper presents an active learning (AL) framework for the classification of 1‐min audio recordings derived from long‐duration recordings of the environment. The goal of the framework was to investigate the efficacy of AL on reducing the manual annotation effort required to label a large volume of acoustic data according to its dominant sound source, while ensuring the high quality of automatically labelled data. We present a comprehensive empirical comparison through extensive simulation experiments of a range of AL approaches against a Random Sampling baseline for soundscape classification. Random Forest is used as a benchmark supervised approach to build classifiers in the AL framework. Also, 12 summary indices extracted for each 1 min of 13‐month recording are used as features for training the classifiers. Our experimental findings demonstrate that (a) among existing query strategies, those based on classifier confidence and diversity of samples are more effective for very large datasets where the classes are imbalanced in size; (b) by considering a practical target performance (i.e. F‐measure equal or greater than 0.8, 0.85 and 0.9) for AL, only 5–16 hr of manual annotation effort is required to build a classifier that automatically annotates a large amount (13 months) of unlabelled audio data. Active learning has a key role to play in alleviating the burden of manual annotation required to build classifiers which can support effective monitoring of species diversity in at‐risk ecosystems.
EU and developing countries : what is the impact of agricultural preferences ?

We assess the impact on trade of European Union (EU) preferences in the agricultural sector using a gravity approach. We use trade statistics at the 6-digit level and compute an explicit measure of the intensity of the preference margins showing the bias implied by the use of aggregate EU imports. As far as the estimation method is concerned, we consider the potential selection bias implied by the presence of zero-trade flows. Our results confirm an overall positive impact of preferences on trade although with significant differences across products.
Yeast community structure and its impact on flavor components during the fermentation process of Chinese Maotai-flavor liquor

[Objective] To analyze yeast community structure and its influence on brewing during the fermentation process of Chinese Maotai-flavour liquor. [Methods] The yeast community in the fifth round (higher yield and quality of the liquor) and the seventh round of fermentation process (lower yield and quality of the liquor) were analyzed using denaturing gradient gel electrophoresis (DGGE) 邵明凯等: 酱香型白酒发酵中酵母群落结构及其对风味组分的影响 2467 http://journals.im. ac.cn/wswxtbcn and real-time quantitative PCR (RT-qPCR). The metabolic components in the fermented grains were investigated through headspace-solid phase microextraction gas chromatograph-mass apectrometer (HS-SPME-GC-MS) and high performance liquid chromatography (HPLC). Finally, the effects of yeast communities on the yield and quality of Maotai-flavor liquor were analyzed. [Results] At least nine yeasts were detected in the fermented grains, including Issatchenkia orientalis, Torulaspora delbrueckii, Pichia galeiformis, Schizosaccharomyces pombe, Galactomyces geotrichum, Trichosporon asahii, Zygosaccharomyces bailii, Saccharomyces cerevisiae and Pichia fabianii. Among them, G. geotrichum and S. cerevisiae was dominant yeasts of the fifth round, while the other two yeast, I. orientalis and Z. bailii, were also advantage in seventh round and Sc. pombe was not detected. In addition, five kinds of yeasts declined significantly during latter period of the seventh round fermentation process, but the yeast community structure during the fifth round was relatively more stable. The total yeasts during the early stage of the fifth round fermentation is 2−5 times more than the seventh round’s, while ethanol content of fifth round fermented grains was 2.45 times as higher as the seventh’s at the end of fermentation. Besides, volatile metabolic component of fermented grains was rich in species. The content of esters of the fifth round was remarkably higher than the seventh round, while organic acids and fusel oils were lower than the seventh round. [Conclusion] During Maotai-flvour liquor brewing process, there is rich diversity in yeast species, and the dynamics of yeast community structure affect liquor’s yield and quality obviously, which provides the basis for the study of Maotai-flavour liquor brewing mechanism.
DEVELOPING FACILITIES FOR SNS CRYOMODULE PERFORMANCE IMPROVEMENTS*

Superconducting RF cavity facilities are currently being developed at SNS, aimed at addressing the limitations and availability of installed cavities and the direct support of the future power upgrade plans. Current efforts are directed towards development of in-situ repairs and developing processing techniques to increase available linac gradients. Procedures have been developed and implemented and the results will be presented for the repair of four cryomodules in the last year. Cryomodule testing facilities are being developed to further understand the collective limitations of installed cavities and spare cryomodule production is underway to develop and fabricate two high beta and one medium beta cryomodules. The direction and status of SRF facilities will be presented. SUPERCONDUCTING CRYOMODULE
A Review of Design and Control Approaches in Lower-Limb Prosthetic Devices

Lower-limb amputation often leads to biomechanical and neurological changes that can reduce mobility, increase pain levels, speed joint degeneration, and otherwise negatively impact quality of life. These changes are likely in part due to the fact that most commercially available prostheses are passive devices that do not generate power or actively adapt to the user. In recent decades, active prosthesis development has focused on identifying lightweight and powerful actuators, as well as eﬀective control approaches for a wide range of locomotor tasks. Here, we provide an overview of the state-of-the-art in lower-limb prosthesis design and discuss the various beneﬁts and limitations of prosthesis actuation and control approaches.
Feature Selection and Nuisance Attribute Projection for Speech Emotion Recognition

In speech emotion recognition, we need to maximize the variability of emotion features across emotion states and suppress the non-emotion variabilities. This paper investigates methods to achieve both of these goals. Specifically, utterance-level emotion features are extracted and the most relevant features are selected by ranking their Fisher discriminant ratios; nuisance attribute projection (NAP) is applied to project the emotion vectors to a subspace with minimum non-emotion variability. The projected vectors are classified by support vector machines or deep neural networks. Evaluations on Emo-DB and CHEAVD suggest that (1) among the 4000+ features defined in the Interspeech speaker-state challenge, many of them have low discriminative power and can be dropped without affecting performance; (2) NAP can remove some of the non-emotion variability; and (3) it is better to use a richer set of features and let the feature selection algorithm to select the relevant ones rather than starting with a compact feature set without feature selection.1 Source code: https://github.com/enmwmak/emotion recognition
Nonlinear density fluctuation field theory for large scale structure

We develop an effective field theory of density fluctuations for a Newtonian self-gravitating N-body system in quasi-equilibrium and apply it to a homogeneous universe with small density fluctuations. Keeping the density fluctuations up to second order, we obtain the nonlinear field equation of 2-pt correlation ξ(r), which contains 3-pt correlation and formal ultra-violet divergences. By the Groth–Peebles hierarchical ansatz and mass renormalization, the equation becomes closed with two new terms beyond the Gaussian approximation, and their coefficients are taken as parameters. The analytic solution is obtained in terms of the hypergeometric functions, which is checked numerically. With one single set of two fixed parameters, the correlation ξ(r) and the corresponding power spectrum P(κ) simultaneously match the results from all the major surveys, such as APM, SDSS, 2dfGRS, and REFLEX. The model gives a unifying understanding of several seemingly unrelated features of large scale structure from a field-theoretical perspective. The theory is worth extending to study the evolution effects in an expanding universe.
It’s a Matter of Perspective: The Role of Aging Expectations and Self-Efficacy Towards Engagement in Healthy Lifestyles Among Older Adults

Abstract This study aimed to investigate whether aging expectations predict engagement in healthy lifestyles. Furthermore, it aimed to determine whether self-efficacy mediates the relationship between aging expectations and engagement in healthy lifestyles. This study enlisted 95 respondents who were 60 years old and older in a large metropolitan area in the Philippines. A four-part instrument package was utilized to measure the respondent’s (1) demographic profile, (2) aging expectations using the Expectations Regarding Aging (ERA-12) Survey, (3) engagement in healthy lifestyles using Health-Promoting Lifestyle Profile II (HPLP-II), and (4) self-efficacy using the Self-efficacy for Self-direction in Health Scale. Data were analyzed using linear regression and mediation analysis. Results show that aging expectations and self-efficacy predicted engagement in healthy lifestyles. Moreover, self-efficacy was found to be a significant mediator between the variables. Programs that promote a positive aging expectation and high self-efficacy should be pursued by the government and non-government organizations.
Pattern of Obesity among Health Workers in Owerri, Imo State, Nigeria.

Aim: This study determined pattern of obesity among health workers in Owerri, Imo State Nigeria. Methodology: Three hundred and sixty health workers (108 males and 252 females) between the ages of 20 and 69 years participated in the study. Mixed method of data collection was used. Questionnaires were administered, anthropometric measurements of height, weight and waist-hip ratio obtained and used to assess obesity. Results: Showed that BMI classified as underweight, normal, overweight and obesity were 3.9%, 38.1%, 35.3% and 22.8% respectively for all participants; 2.4%, 33.7%, 37.6% and 31.2% for driving participants; 4.2%, 29.2%, 47.9% and 18.8% for cycling participants; and 7.2%, 55.7%, 27.8 and 9.3% for trekking participants. Obesity prevalence was 16.7%, 29.2%, 6.5%, 29.4% and 25.8% among doctors, nurses, laboratory scientists, pharmacists and others respectively; 0.8%, 8.1%, 6.4%, 1.4% and 0.6% for age groups 20 – 29, 30 – 39, 40 – 49, 50 – 59 and 60 – 69 years respectively. Waist to Hip Ratio classified as underweight, normal, overweight/obesity was 31.0%, 31.9%, 37.1% and 12.7%, 18.7%, 68.7% for males and females respectively. Conclusion: This study revealed high obesity prevalence among health workers in Owerri, nurses being most susceptible. It also revealed higher obesity prevalence among female health workers than males. Considering the perception of health workers as role models of healthy living and the obvious implications of obesity, we recommend implementation of workplace health promotion policies that focus on weight reduction through increased physical activities and healthy dieting. Key Words: Obesity, Body Mass Index, Waist to Hip Ratio, underweight, overweight, health workers.
FOR MULTISCALE KINETIC EQUATIONS WITH UNCERTAINTIES

Kinetic modeling and computation face the challenges of multiple scales and uncertainties. Developing efficient multiscale computational methods, and quantifying uncertainties arising in their collision kernels or scattering coefficients, initial or boundary data, forcing terms, geometry, etc. have important engineering and industrial applications. In this article we will report our recent progress in the study of multiscale kinetic equations with uncertainties modelled by random inputs. We first study the mathematical properties of uncertain kinetic equations, including their regularity and long-time behavior in the random space, and sensitivity of their solutions with respect to the input and scaling parameters. Using the hypocoercivity of kinetic operators, we provide a general framework to study these mathematical properties for general class of linear and nonlinear kinetic equations in various asymptotic regimes. We then approximate these equations in random space by the stochastic Galerkin methods, study the numerical accuracy and long-time behavior of the methods, and furthermore, make the methods “stochastically asymptotic preserving”, in order to handle the multiple scales efficiently.
Cooperative Edge Computing With Sleep Control Under Nonuniform Traffic in Mobile Edge Networks

Mobile edge computing (MEC) is one of the key technologies for fifth generation networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the nonuniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov-based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.
Tilburg University Question Quality in Community Question Answering

Community Question Answering websites (CQA) offer a new opportunity for users to provide, search and share knowledge. Although the idea of receiving a direct, targeted response to a question sounds very attractive, the quality of the question itself can have an important effect on the likelihood of getting useful answers. High quality questions improve the CQA experience and therefore it is essential for CQA forums to better understand what characterizes questions that are more appealing for the forum community. In this survey, we review existing research on question quality in CQA websites. We discuss the possible measures of question quality and the question features that have been shown to influence question quality.
Towards Automatically Detecting Promotional Attacks in Mobile App

Mobile app store is an important component of mobile ecosystem such as the Google Play Store. It is convenient for mobile users to gain information such as ratings/reviews (called user-generated content (UGC)) and the number of app downloads, category (called Metadata) from mobile app store. The populartiy of mobile app store is leading to a rapidly growing number of security and privacy problems. i.e., a miscreant may promote many malicious apps with fake ratings/reviews in a very short period of time in order to attract more victims to download/install a malicious app. To address these issues, we developed a system called PADetective that identifys miscreants who are likely to be performing promotional attacks. We collected a large-scale dataset of approximately 20 M unique users, 1 M apps and 57 M reviews from real world and demonstrated that PADetective system is able to achieve a high detection accuracy as 95.4% with a false-positive rate of 8%. We then conducted a large-scale analysis based on real world dataset and found that our system can be applied to detect malware that has not been discovered by anti-virus checkers.
An Efficient and Stable Method for Fluid-Rigid Body Interactions with Free Surface

In order to simulate fluid-body interaction, many studies have been conducted, and these are generally separated as several types according to its grid system such as body-fitted and Cartesian. When free surface is inserted in fluidbody interaction, three different continuums (air, water, body) should be considered. In this case, Cartesian grid based methodology is most suitable in terms of convenience and efficiency. A robust and efficient method simulating monolithic fluid-rigid body interactions in Cartesian grid was proposed by Gibou and Min [1]. This approach solves the incompressible flow using semi-Largrangian time discretization with projection method, and strongly couples the fluid and rigid body via projection step by applying fractional time stepping to solid equations. Interestingly, this methodology is proved to be stable in the sense that its kinetic energy does not increase. We employ this unconditionally stable scheme to simulate fluid-rigid body interaction and combine this method with free surface utilizing volume-of-fluid (VOF) approach. To demonstrate applicability of our numerical method, several benchmark problems are simulated, and all results are validated.
Analytical Study Based Optimal Placement of Energy Storage Devices in Distribution Systems to Support Voltage and Angle Stability

ii TABLE OF CONTENTS . iv LIST OF FIGURES . v LIST OF TABLES . vii CHAPTER 1 OVERVIEW . 1 CHAPTER 2 LITERATURE REVIEW. 5 2. 1 Distribute Generation . 5 2. 2 Voltage Angle Stability . 8 2. 3 Jacobian-Based Sensitivity Analysis. 10 2. 4 Optimal Placement of Storage . 12 2.6 Conclusion . 16 CHAPTER 3 JACOBIAN MATRIX AS A SENSITIVITY ANALYSIS TOOL . 18 3. 1 Power flow analysis . 18 3. 2 Newton-Raphson load flow . 21 3. 3 Sensitivity analysis. 27 3. 4 The definition of indices . 30 3. 5 IEEE 14-node test feeder as a case study for sensitivity indices . 33 CHAPTER 4 EVALUATION OF INDICES . 52 CHAPTER 5 INDICES OF IEEE 123-NODE TEST FEEDER . 60 5. 1 IEEE 123-node test feeder . 60 5. 2 Sensitivity indices of IEEE 123-node test feeder . 63 CHAPTER 6 CONCLUSIONS AND FUTURE WORKS . 76 6. 1 Conclusion . 76 6. 2 Future works . 79 REFERENCES . 80
Improved grazing activity of dairy heifers in shaded tropical grasslands

Trees in the production systems can effectively reduce hot weather-induced stress in the Brazilian Midwest. High temperatures cause changes in animals daily routine, and trees into pastures can promote benefits. The aim of this research was to evaluate the behavior of dairy heifers in silvopastoral systems in the state of Mato Grosso, Brazil. A herd of 24 crossbreed heifers (3/4 and 7/8 Holstein/Zebu), 350kg average weight, was evaluated over three seasons. Piata grass was managed under three shade levels: full-sun, moderate-shade, and intensive-shade provided by 10 to 12m high Eucalyptus trees. Behavior data were collected every 15 minutes from 8:30h to 16h. Shade availability significantly impacted heifer behavior, mainly affecting grazing frequency and time during the hottest hours. Grazing behavior was affected by shade levels during the different seasons. Heifers showed preferred grazing times. Heifers in the intensive-shade system visited shady areas during the hottest hours throughout the seasons. Heifers in the full sun-system avoided grazing during the warmer times, ceasing feeding activities. Our results from the Brazilian Midwest showed that shade availability causes breed heifers to change their daily routine.