Miracle-dz/UniFAR-FTUs huggingface dataset:
DNN-HMM based acoustic model for continuous pig cough sound recognition

To detect the respiratory disease through pig cough sound in the early stage, a novel method based on Deep Neural Networks-Hidden Markov Model (DNN-HMM) was proposed to construct an acoustic model for continuous pig cough sound recognition. Noises in the continuous pig sounds were eliminated by the Wiener algorithm based on wavelet thresholding the multitaper spectrum, and the experimental corpus was obtained from the denoised continuous pig sounds. The 39-dimensional Mel Frequency Cepstral Coefficients (MFCC) extracted from the corpus were considered as feature vectors. Sounds in pig farms were divided into pig coughs, non-pig coughs, and silence segments. In the HMM, the number of hidden states of pig cough, non-pig cough and silence segments were 5, 5 and 3 respectively, and the observation states represented the feature vectors of the continuous pig sound signal. Based on experiments and empirical theory, the DNN model with 3 hidden layers and 100 nodes per layer was used to describe the correspondence between hidden states and observation serials. Through experiments, the context frames of DNN input were set to 5. Under the condition of optimal parameter setting, the traditional acoustic model Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) was compared with DNN-HMM through a 5-fold cross-validation experiment. It was found that the Word Error Rate (WER) of each group in DNN-HMM was lower than that in GMM-HMM, and the average WER was 3.45% lower. At the same time, the best result of the DNN-HMM model was obtained with the lowest WER of 7.54%, and the average WER was 8.03%. The results showed that the method of DNN-HMM based acoustic model for continuous pig cough sound recognition was stable and reliable. Keywords: DNN-HMM, acoustic model, continuous pig coughs, recognition, pig industry DOI: 10.25165/j.ijabe. 20201303.4530 Citation: Zhao J, Li X, Liu W H, Gao Y, Lei M G, Tan H Q, et al. DNN-HMM based acoustic model for continuous pig cough sound recognition. Int J Agric & Biol Eng, 2020; 13(3): 186–193.
Characteristics Between Populations of Thorny Skate (Raja radiata) in the Northwest Atlantic

Sexual maturity of the thorny skate (Raja radiata) occurs at relatively small sizes off northern Iceland and West Greenland, off Baffin Island and Labrador, on the Northeast Newfoundland Shelf and in the Gulf of S1. Lawrence, and at considerably larger sizes on the Grand Bank and 81. Pierre Bank. In areas with sexual maturity at a small length, the maximum fish lengths weretypicallysmall, and in areas with sexual maturity only at much greater lengths, the maximum lengths were considerably larger. The largest thorny skate was a 104-cm male from the southern Grand Bank. The lengths at sexual maturity and the maximum lengths for males were greater than those for females. One hermaphrodite was noted. There was a higher maximal number of rows of alar spines in areas with sexual maturity at a larger size. From the great differences between areas in length at sexual maturity, it is concluded that no large-scale migrations of thorny skate occurred between these areas.
Effectiveness of Family Empowerment Therapy Based on Self-Compassion on Self-Care and Glycosylated Hemoglobin in Female Patients with Type 2 Diabetes Mellitus: A Randomized Controlled Clinical Trial

Background: Diabetes can be successfully controlled by metabolic, psychological, social, and interpersonal factors. The present study aimed to determine the effectiveness of family empowerment therapy based on self-compassion on self-care and glycosylated hemoglobin in female patients with type 2 diabetes mellitus. Methods: In a randomized controlled clinical trial with control group, pre-test, and post-test, 60 women with type 2 diabetes referred to the endocrinology department of Imam Hossein Hospital in Tehran province from December 2018 to April 2019 were randomly selected from the 73 patients narrowed down according to the inclusion criteria. Afterwards, they were randomly assigned to experimental and control groups. The experimental group (n=30) was treated with family empowerment therapy based on self-compassion for eight weekly 90-minute sessions while the control group (n=30) received usual hospital treatments. Data collection instrument was Summary of Diabetes Self-Care Activities; glycosylated hemoglobin measurements were performed in three phases of baseline, after intervention, and three-month follow-up and analyzed by multivariate repeated measures analysis of variance and Bonfroni post-hoc test using the SPSS-21 software. Results: The results of repeated measures analysis of variance showed significant differences after the intervention between the experimental and control groups regarding self-care (P0.001=, F=561.086) and glycosylated hemoglobin (P=0.001, F=304.953); furthermore, comparison of the means indicated the effectiveness of the treatment in improving self-care and reducing glycosylated hemoglobin levels in the experimental group compared to the control group. Moreover, the Bonferroni test results showed that in the pretest stage, there was no significant difference between the two groups in terms of self-care (P=0.447) and glycosylated hemoglobin (P=0.887); however, in the post-test and follow-up stages, the two groups showed were significantly different concerning self-care (P=0.001) and glycosylated hemoglobin (P=0.001), implying the effectiveness of the intervention and the sustainability of its effects. Conclusions: The results of the present study revealed that family empowerment therapy based on self-compassion can be effective in improving self-care and reducing glycosylated hemoglobin in women with type 2 diabetes.
Recruitment of Suitable Football Player by using Machine Learning Techniques

In football, player selection is the critical task which leads to a key of success. Coaches and managers form their team by selecting ﬁnest players all around the globe. Each player has his own identity in the team. When the team gets formed and due to some circumstances player moves from one club to another then ﬁnding replacement of such a player is again a big task. Finding closest match for the replaced player is the big headache that coaches and manager face. The problem illustrated in this paper is to ﬁnd closest match for the replaced player by using machine learning algorithm. The players will get classiﬁed based on their ratings. In this research six machine leaning algorithms namely SVM, LDA, Naive Bayes, Decision Tree, XGBoost, KNN, have been implemented. Research compares the performances of these machine learning algorithms by using evaluation metrics such as accuracy, precision etc. LDA and SVM performed the best with accuracy of 83.77% and 80.31%. Further, KNN function will give the closest match among the predicted players.
The global water resources and use model WaterGAP v2.2d: model description and evaluation

Abstract. WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output.
Determinants of Dividends in the Telecommunications Sector

Purpose – T elecommunications stocks are known to be defensive stocks, which are less susceptible to volatility in the global financial markets and usually pay higher dividends than in other industries. In this context, this paper intends to study the influence of some specific characteristics of these companies on the payment of dividends . Design/methodology/approach – To achieve the proposed objective, the panel data methodology was used, specifically the GMM estimation method for a sample consisting of 34 companies listed in Western Europe and North America in the period between 2007 and 2016 and in the sub-period between 2008 and 2013 , which comprises the global financial crisis. Findings – The results show that specific factors such as investment in fixed capital, indebtedness, price to book value ratio, free cash flow (FCF), liquidity , and dividends paid in the previous year are decisive in explaining the dividends distributed in the entire period. For the financial crisis period , the sign and significance of the variables remain almost unchanged, suggesting that these companies pay dividends regardless of economic cycles, covering the absence of FCF with adequate liquidity levels to satisfy investors during recessive periods. Originality/value – Our study contributes to a better understanding of the telecommunications sector and the dividend policy carried out by managers in this sector, and it can assist in the analyses of investors, other managers, financial analysts , and researchers on the subject.
Two new species of Liodessus Guignot, 1939 diving beetles from Northern Peru (Coleoptera, Dytiscidae, Hydroporinae)

The diving beetles Liodessus altoperuensis sp. nov. and Liodessus caxamarca sp. nov. (Dytiscidae, Hydroporinae, Bidessini) are described from the high altitudes of the Puna regions of north western Peru. They occur in shallow and exposed mossy peatland puddles. We delineate the two species using structures such as male genitalia, beetle size, shape and colour pattern. Mitochondrial Cox1 data were also generated, and revealed clusters congruent with morphological evidence. Altogether fourteen Liodessus species are now known from the Andean region.
Characterization of antibody response against 16kD and 38kD of M. tuberculosis in the assisted diagnosis of active pulmonary tuberculosis

Background In view of the inability of traditional etiological methods to diagnose pulmonary tuberculosis rapidly and effectively, the antibody responses against 38kD and 16kD-antigens of Mycobacterium tuberculosis (M. tuberculosis) were both detected in order to obtain a better serological detection method for M. tuberculosis. Methods M. tuberculosis-secreted protein 38kD and membrane protein 16kD were prokaryotically expressed and purified, and then used as detection antigens. A novel evolved immunoglobulin-binding molecule (NEIBM)-ELISA method was used to detect antibody levels against 38kD and 16kD in active tuberculosis patients (confirmed tuberculosis cases and clinically diagnosed cases), to explore the significance of these two antigens in serological detection of M. tuberculosis, and to study the diagnostic value of the combined detection of the two antigens in active pulmonary tuberculosis. Results The results showed that the positive detection rates of the 16kD antigen and 38kD antigen of M. tuberculosis were higher (about 44%) in the confirmed cases of tuberculosis, and there was no significant difference in the positive detection rates of the two antigens (P=0.786). The combined detection of these two antigens showed that the positive detection rate could be increased to 61.5%, which was significantly better than the detection effect of the two antigens alone. The positive detection rates of 16kD and 38kD antigens were 26–30% in clinically diagnosed tuberculosis cases, which were lower than those in confirmed tuberculosis cases, and there was no significant difference in the positive detection rates of the two antigens (P=0.242). The detection effect of the two combined antigens was better than that of the 16kD and 38kD antigens alone, but the detection rate was still lower than that of the confirmed tuberculosis cases. Conclusions This study found that the detection effect of 16kD and 38kD antigens was similar in confirmed cases and clinically diagnosed cases of pulmonary tuberculosis, and that the detection effect needs to be further improved. The combined detection of the two antigens showed a significantly better detection effect than the two antigens alone, suggesting that the combined detection of multiple antigens can be used for serological diagnosis of M. tuberculosis infection in clinic.
Identifying Tools and Methods for Risk Identification and Assessment in Construction Supply Chain

The construction project is a business full of risk in every process due to its complexity, changes, and involvement from various stakeholders. One of the critical risks in the construction project is in the supply chain. Identifying and assessing the risk with the right tools and methods in that area will inevitably affect the success of the project. Unfortunately, the research for the tools and methods in a construction supply chain is still limited and scattered. This research objective is to analyze the gap between literature and to create improvement in tools and methods for risk identification and assessment in the construction supply chain. This research will use the systematic literature review method in finding and investigating the tools and methods. The four methods that were found are: Analytical Hierical Process (AHP), Failure Mode Effect Analysis (FMEA), Supply Chain Operation Reference (SCOR), and Hazard and Operational (HAZOP). Strength and weakness with their potential use as tools and methods for identifying and assessing the construction supply chain risk then summarized. The use of SCOR combined with FMEA methods has shown to be practical tools and methods for identifying and assessing the construction supply chain risk.
Determinants of changes in gold returns

This paper explains the behavior of logarithmic gold returns between 1995 and 2017 by using several conditional mean and variance models that incorporate asymmetry and heavy tails effects. For this purpose, we conduct, first, an analysis based on standard autoregressive vectors in order to identify the main external regressors and, later, a sort of adjustments with different specifications of the AR-GARCH type to forecast the volatility of gold-price fluctuations. The main conclusion is that these fluctuations can be adequately explained by the behavior of the USDEER and SP500 series according to the specification AR(1)-GARCH (1, 1) that has a Student t distribution associated to it. This means that the long-term determinants of gold-return volatilities are related to exchange rate hedging strategies and anti-cyclical protection against stock markets variations by investors. JEL code: C46, C51, D81
Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.
The Impacts of Trade Liberalization on Informal Labor Markets: A Theoretical and Empirical Evaluation of the Brazilian Case

Following trade liberalization, several developing countries experienced a sharp increase in the share of informal manufacturing employment. In this paper, I examine the impacts of trade liberalization on the labor markets of a small open economy, in an environment in which tariffs affect firms’ payroll tax compliance decisions. I demonstrate that a reduction in domestic import tariffs reduces the average formal wage and show that the direction of the effect on the share of informal employment depends on the initial labor market conditions. A cut in trading partner import tariffs decreases the share of domestic informal employment and increases the average formal wage. I confirm the model’s principal findings empirically, using data from the 1989–2001 Brazilian trade liberalization episode. I find the results robust to endogeneity and self-selection concerns, which are addressed, respectively, using instrumental variable and switching regression approaches.
Analysis of Leptin Gene Polymorphism in Assam Hill Goat and their Association with Growth

Goat substantially contributes to the rural economy. It plays an important role in providing livelihood to the poor sections of the society. India possesses the second largest goat population in the world with 135.17 million goats (26.40% of the country’s total livestock) which is 15.68% of total goat population in the world (Basic Animal Husbandry and Fisheries Statistics, 2014). Eastern and North-Eastern part of India is rich in breeds of goat like Assam Hill goat, SumiNe, Ganjam, Black Bengal etc which are known to be of good quality meat type. Almost the entire population of Northeastern region of India is non-vegetarian and chevon is the meat of choice having no religious International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 7 (2020) Journal homepage: http://www.ijcmas. com
Interactive comment on “Response of near-inertial energy to a supercritical tropical cyclone and jet stream in the South China Sea: modeling study” by Hiu

stream. This jet stream plays an important role in this study and more descriptions and explanations on the stream is necessary. One reference (Gan and Qu, 2008) has been cited by the author on Line 61, but no further description on the jet. Under what condition will the jet stream exist? Is the intensity/velocity of the stream changes a lot? The jet stream is lumped with the NIO generated by the TC, and this interaction is nonlinear. The intensity/velocity of the stream will significantly affect the interaction. Is the stream located in its typical location with its typical velocity during the typhoon season? If a reader wants to know how much KEni will be generated and how the NIO propagates, which is the main topic of the present study, he would want to know whether the scenario used in this study is applicable.
Asphalt Layer Density and Air Voids Content: GPR and Laboratory Testing Data Reliance

The assurance of asphalt pavement layer compaction, expressed by ratio between field and laboratory bulk density and air voids content, is one of the main criteria of the durability of asphalt road pavement. Destructive measures should be applied and cores should be taken from the asphalt pavement seeking to determine the representative compaction level of the constructed asphalt layers. New methods are constantly being sought for fast, non-destructive and accurate asphalt layer density and air void determination on road. Ground Penetrating Radar (GPR) can allow determining the qualitative characteristics of asphalt pavement across the entire length of the road without causing damage to the road structure. Relative dielectric permittivity, usually called dielectric value or constant, is the leading property used in GPR applications on road pavement surveys. This article presents GPR measurement results from asphalt base and binder layers of four test sections. GPR measurements were conducted immediately after the end of asphalt layer compaction process. Test points on each layer were selected and density, air void content were determined by drilling cores and testing them in the laboratory. To estimate asphalt layer density and air void content, GPR data were analysed using different existing mathematical models. To justify the reliability of the data measured by GPR, results were checked by comparing them with the results measured directly on cores taken from the asphalt pavement layers.
Distributed Compressive Sensing Algorithm for Photoacoustic Tomography

Biomedical imaging techniques are playing an essential role in diagnosing different kinds of diseases, which always motivates the search for improving their sensitivity and accuracy. Photoacoustic Tomography (PAT) is one of the most powerful techniques. PAT has many advantages as it is less expensive and faster than Magnetic Resonance Imaging (MRI). It combines the advantages of optical imaging and ultrasound imaging as it provides high contrast, high penetration and high resolution images for biological tissues. Also, it uses non-ionizing radiation which is very safe for human health. The main challenge in PAT is that human tissues can be exposed only to a limited amount of radiation, so a full-view of PAT requires many transducers and a great number of measurements. This thesis aims to develop an efficient reconstruction algorithm of Photoacoustic (PA) images that uses few number of transducers, few number of measurements and offers low computational complexity while maintaining high quality of recovered images. The proposed reconstruction algorithm depends on Compressive Sensing (CS) theory which is a signal processing technique that is capable of forming a fullview PAT images (under certain prerequisites) with few number of measurements. The proposed algorithm solves the CS problem using a distributed and parallel implementation of the Alternating Direction Method of Multipliers (ADMM). ADMM is a well-known method for solving convex optimization problems. A group of local processors that work in parallel with one global processor are used to form the images. The iterative algorithm of ADMM is distributed over local processors in such a way perfect reconstruction of images is possible. vii Simulation results show that the proposed algorithm is powerful and successful in reconstructing different kinds of PA images with very high quality and significantly reduced computational complexity. Reducing the computational complexity is reflected on a much lower reconstruction time. Also, the algorithm requires lower cost and shorter acquisition time since the CS theory is used which allow the recovery of images from few number of samples and sensors. Although the idea of distributed ADMM has been introduced before in literature but to the best of our knowledge, this is the first work to apply distributed ADMM method in recovering photoacoustic images by distributing the iterative algorithm among multiple processors working in parallel.
Response to Reviewers

The authors also find an increase in the doubling time along the epidemic, that would be extremely interesting in terms of a slowdown due to the non-pharmaceutical interventions implemented in France. However, I wonder if either the non-uniform sampling rate or the geographical spread of the strains could have affected this result. Intuitively, both could cause an upward bias to the inferred growth rate in the early part of the tree. In my opinion, while very suggestive, the evidence presented here is not conclusive