Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble System of Deep Neural Networks for Single-Channel Audio Separation

Speech separation is a well-known problem, especially when there is only one sound mixture available. Estimating the Ideal Binary Mask (IBM) is one solution to this problem. Recent research has focused on the supervised classification approach. The challenge of extracting features from the sources is critical for this method. Speech separation has been accomplished by using a variety of feature extraction models. The majority of them, however, are concentrated on a single feature. The complementary nature of various features have not been thoroughly investigated. In this paper, we propose a deep neural network (DNN) ensemble architecture to completely explore the complimentary nature of the diverse features obtained from raw acoustic features. We examined the penultimate discriminative representations instead of employing the features acquired from the output layer. The learned representations were also fused to produce a new features vector, which was then classified by using the Extreme Learning Machine (ELM). In addition, a genetic algorithm (GA) was created to optimize the parameters globally. The results of the experiments showed that our proposed system completely considered various features and produced a high-quality IBM under different conditions.
Reading and Writing Relations Are Not Uniform: They Differ by the Linguistic Grain Size, Developmental Phase, and Measurement

We conducted a meta-analysis to investigate reading-writing relations. Beyond the overall relation, we systematically investigated moderation of the relation as a function of linguistic grain size (word reading and spelling versus reading comprehension and written composition), measurement of reading comprehension (e.g., multiple choice, open-ended, cloze), and written composition (e.g., writing quality, writing productivity, writing fluency, writing syntax), and developmental phase of reading and writing (grade levels as a proxy). A total of 395 studies (k = 2,265, N = 120,669) met inclusion criteria. Overall, reading and writing were strongly related (r = .72). However, the relation differed depending on the subskills of reading and writing such that word reading and spelling were strongly related (r =.82) whereas reading comprehension and written composition were moderately related (r =.44). In addition, the word reading-spelling relation was stronger for primary-grade students (r =.82) than for university students/adults (r =.69). The relation of reading comprehension with written composition differed depending on measurement of reading comprehension and written composition—reading comprehension measured by multiple choice and open-ended tasks had a stronger relation with writing quality than reading comprehension measured by oral retell tasks; and reading comprehension had moderate relations with writing quality, writing vocabulary, writing syntax, and writing conventions but had weak relations with writing productivity and writing fluency. Relations tended to be stronger when reliability was higher, and the relation between word reading and spelling was stronger for alphabetic languages (r = .83) than for Chinese (r = .71). These results add important nuances about the nature of relations between reading and writing.
Identifying patterns and hotspots of global land cover transitions using the ESA CCI Land Cover dataset

ABSTRACT Land use/land cover change is a continuing research focus, not only because of its ecological and environmental effects but also because of the difficulties with accurate change detection and analysis uncertainty. The principal difficulty is the lack of a long time series of annual global land cover maps at a fine resolution. A new global long-term time series of annual datasets called the European Space Agency (ESA) Climate Change Initiative Land Cover (CCI-LC) has been published, making it possible to detect the global land cover changes. Using this ESA CCI-LC product from 1992–2015, we quantified the annual transitions of land cover change globally with the trajectory analysis method, analyzed the changes patterns and identified the land cover change hotspots. The total land cover change area for the world was 5.99 million km2, amounting to only 3.36% of the total continental area. Most changes happened in forest and cropland, accounting 32% of all the land cover changes. Most land cover changes happened in tropical ecoregions. Grassland changes were mainly distributed in the temperate ecoregions, while cropland expansion occurred mainly in the tropical or subtropical ecoregions. The hotspots identified in this paper could provide target areas for further research.
Scytokine response to exercise and its modulations

Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Scytokine response to exercise and its modulations Autor(es): Suzuki, Katsuhiko Publicado por: Imprensa da Universidade de Coimbra URL persistente: URI:http://hdl.handle. net/10316.2/44064 DOI: DOI:https://doi.org/10.14195/2182-7087_ex2018_1
HUBUNGAN PERSEPSI PENGGUNA TERHADAP KEBERHASILAN IMPLEMENTASI REKAM MEDIS ELEKTRONIK ; TINJAUAN LITERATUR

Rekam Medis Elektronik (RME) merupakan inovasi teknologi guna mendukung integrasi sistem informasi dalam manajemen rumah sakit untuk menghadirkan pelayanan kesehatan yang berkualitas, efisien, efektif dan aman sesuai kebutuhan masyarakat. Implementasi RME tidak semudah membalikkan telapak tangan, penelitian di negara-negara yang sudah menerapkan RME menyebutkan penerapan RME menghadapi tantangan dari organisasi maupun pengguna. Studi literatur ini bertujuan mengetahui faktor-faktor yang berhubungan dengan penerimaan profesional kesehatan terhadap keberhasilan implementasi RME sehingga bermanfaat bagi para pemangku kepentingan dalam menerapkan dan mengembangkan RME di kemudian hari. Pencarian artikel dilakukan melalui database Scopus, Sciencedirect, Proquest dan GoogleScholar. Dari kata kunci dan boolean operator yang digunakan diperoleh sebanyak 173 artikel, berdasarkan kriteria inklusi dan eksklusi serta penilaian eligibilitas didapatkan 12 artikel yang sesuai. Hal utama yang terindentifikasi adalah persepsi manfaat dan persepsi kemudahan penggunaan berkaitan dengan penerimaan individu yang bisa dipengaruhi oleh variabel eksternal yaitu karakteristik individu dan faktor organisasi. Usia, jenis kelamin, tingkat pendidikan, jenis profesi, masa kerja, serta departemen/unit kerja tidak berpengaruh terhadap penerimaan penggunaan dalam menentukan keberhasilan implementasi. Dukungan organisasi berupa pelatihan dan simulasi yang tepat sasaran, serta kebijakan dan standar prosedur yang jelas berpangaruh terhadap persepsi pengguna dalam menerima dan menerapkan RME dipekerjaannya sehari-hari. Penting bagi rumah sakit untuk mengetahui faktor-faktor yang berhubungan dengan keberhasilan implementasi RME, salah satunya adalah faktor pengguna yang menjadi kunci keberhasilan penerapan rekam medis elektronik karena berkaitan dengan mengubah kebiasaan, dibutuhkan pemikiran terbuka untuk mau menerima dan beradaptasi dengan perubahan budaya kerja.
Technology Readiness Motivators and Online Behavioural Advertising: A Perceived Benefits-Risk Assessment Using Privacy Calculus Theory

Purpose: The present research intended to find whether the technology readiness motivators, Optimism, and innovativeness, impact the user's intention to accept Online Behavioural Advertising (OBA), an area that requires additional research. Theoretical framework: Even though these personalized advertisements provide an enhanced user experience, they provide users with advertisements based on their online surfing behavior, and unfortunately, at the same time, it also creates privacy concerns. At the same time, Privacy Calculus Theory (PCT) is considered an appropriate theory when it comes to a rational benefits-costs assessment of the privacy-centred decision-making by internet users. Design/Methodology: Based on an extensive literature review, a conceptual model is being proposed, and a scenario-based questionnaire was developed and pursued on internet users' experience of the subject matter. Six hundred ten valid responses were received, and PLS-SEM was used to analyze the results. Findings: The results show that Optimism and innovativeness have a direct and indirect effect on the intention to accept Online Behavioural Advertising (OBA). Technology readiness Motivators have a detrimental impact on the intention to accept OBA. Research, Practical and Social implications: Based on our findings, we may be able to gain a deeper understanding of the theory behind targeted, data-driven digital advertising and the way in which it pertains to online shopping. Presented here is evidence to support the theory that the predisposition of users toward new technology could be the reason for users ranking perceived benefits higher than risk. Originality/Value: This study attributes original work in the sense that it is the first time that an individual’s predisposition towards technology has been taken into consideration while analysing the acceptance of OBA grounded on the Privacy Calculus framework.
Phytochemical Screening and Antimicrobial Activity of Methanolic Extract of Cymbopogon schoenanthus (L.) (azkhar) Collected from Afif City, Saudi Arabia

In Saudi Arabia, Cymbopogon schoenanthus (L.) has been traditionally used to treat a variety of diseases. This study aimed to investigate the crude methanolic extract of Cymbopogon schoenanthus (L.) phytochemical, chemical composition, and antibacterial activity. Phytochemical analysis revealed the presence of tannins, poly-tannins, steroids, alkaloids, essential oils, terpenoids, and flavonoids. The presence of functional groups such as -COOH, -OH, -C=O, and CH2 was revealed via FTIR spectroscopy. 13C and 1H NMR (nuclear magnetic resonance) were used to determine the types and number of protons (hydrogen atoms) and their electronic states. Using an agar well diffusion assay, methanolic extract of Cymbopogon schoenanthus (L.) inhibited the growth of some foodborne pathogenic bacteria in zones ranging from 8 to 25 mm in diameter. The minimum inhibitory concentration (MIC) for Staphylococcus aureus was 12.5 mg/mL, whereas it was 25 mg/mL for Bacillus cereus, Klebsiella pneumoniae, and Escherichia coli. The time–kill assay revealed a sharp decline in Staphylococcus aureus and Klebsiella pneumonia after 2 h at a concentration of 150 mg/mL, while Bacillus cereus and Escherichia coli showed a gradual decline with constant concentrations of 75 to 150 mg/mL. The minimum bactericide concentration (MBC) value for Bacillus cereus, Staphylococcus aureus, and Escherichia coli was 50 mg/mL, while it was 25 mg/mL for Klebsiella pneumoniae. In conclusion, our study revealed that Cymbopogon schoenanthus (L.) methanolic extract has a significant antibacterial effect, suggesting that it could be used to treat various foodborne pathogens.
A competing round-robin prediction model for histologic subtype prediction of lung adenocarcinomas based on thoracic computed tomography

Adenocarcinomas (ADC) is the major subtype of non-small cell lung cancers. Currently, surgery is used as the main approach for the treatment of the early-stage ADCs. However, different histological subtypes of ADC classified by the IASLC/ATS/ERS system may potentially impact on the surgical management, which subsequently influence the prognosis of the surgery. Thus, preoperative determination of ADC subtypes is essential and highly desirable. Nevertheless, the histological subtypes of ADCs may be either unknown or incompletely determined by biopsy before the surgery. Alternatively, the histological subtypes of ADCs may be predicted from the pulmonary computed tomographic (CT) images. However, previous studies showed limitations on the prediction results due to the complex composition of ADC subtypes. One possible reason is the radiomic descriptors used to differentiate different subtypes could be very different. The conventional approaches based on the same set of descriptors to distinguish all subtypes are inherently infeasible. Another possible reason is the complex composition of multiple subtypes in a lung nodule may hinder the extraction of effective radiomic descriptors to characterize each subtype. To overcome these challenges, a competing round-robin prediction model was proposed to predict the histological subtypes of ADCs, which was composed of three key ideas, namely, pair-specific radiomic descriptors for differentiation of every pair of subtypes, inter-regional descriptors for characterization of complex composition of subtypes in a nodule, and a multi-level round-robin classifier. Based on 70 ADCs patients, the proposed model achieved an accuracy of 86.3% in predicting five histological subtypes of adenocarcinomas.
New Sufficient Conditions of Robust Recovery for Low-Rank Matrices

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery from a given linear system of equality constraints. Sufficient conditions are derived to guarantee the robust reconstruction in bounded $l_2$ and Dantzig selector noise settings $(\epsilon\neq0)$ or exactly reconstruction in the noiseless context $(\epsilon=0)$ of all rank $r$ matrices $X\in\mathbb{R}^{m\times n}$ from $b=\mathcal{A}(X)+z$ via nuclear norm minimization. Furthermore, we not only show that when $t=1$, the upper bound of $\delta_r$ is the same as the result of Cai and Zhang \cite{Cai and Zhang}, but also demonstrate that the gained upper bounds concerning the recovery error are better. Finally, we prove that the restricted isometry property condition is sharp.
Finite-Order VAR Representation of Linear Rational Expectations Models: With Some Lessons for Monetary Policy

This paper considers the characterization via finite-order VARs of the solution of a large class of linear rational expectations (LRE) models. I propose a unified approach that uses a companion Sylvester equation to check the existence and uniqueness of a solution to the canonical (first-order) LRE model in finite-order VAR form and a quadratic matrix equation to characterize it decoupling the backward- and forward-looking aspects of the model. I also investigate the fundamentalness of the shocks recovered. Solving LRE models by this procedure is straightforward to implement, general in its applicability, efficient in the use of computational resources, and can be handled easily with standard matrix algebra. An application to the workhorse New Keynesian model with accompanying Matlab codes is provided to illustrate the practical implementation of the methodology. I argue that existing empirical evidence on the transmission mechanism of monetary policy shocks from structural VARs (when the specification is inconsistent with theory due to the identification restrictions, lag specification, etc.) should be taken with a grain of salt as it may not have a proper structural interpretation.
Entropy-Guided Assessment of Image Retrieval Systems: Advancing Grouped Precision as an Evaluation Measure for Relevant Retrievability

The performance evaluation of Content Based Image Retrieval systems (CBIR), can be considered as a challenging and overriding problem even for human and expert users regarding the important numbers of CBIR systems proposed in the literature and applied to different image databases. The automatic measures widely used to assess CBIR systems are inspired from the general Text Retrieval (TR) domain such as precision and recall metrics. This paper proposes a new quantitative measure adapted to the CBIR particularity of relevant images grouping, which is based on the entropy of the returned relevant images. The proposed performance measure is easy to understand and to implement. A good discriminating power of the proposed measure is shown through a comparative study with the existing and well-known CBIR evaluation measures
Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency

In this thesis, we propose four Bayesian AR(1) models, namely an AR(1) with constant variance and constant slope coefficient, an AR(1) with stochastic variance and constant slope coefficient, an AR(1) with constant variance and time varying slope coefficient and finally an AR(1) with stochastic variance and time varying slope coefficient. These models were used to examine the daily predictability of the EUR/USD and USD/JPY exchange rate returns. These autoregressive models were fitted using the classic Metropolis Hastings (MH) within Gibbs algorithm as well as the more novel approach developed by Golightly and Wilkinson [2008]. We found that while all the models outperform a driftless random walk in terms of MSPE, they fail to capture the volatility of exchange rate returns. We also observed the superiority of the Golightly and Wilkinson technique compared to the MH within Gibbs algorithm regarding the speed of the inference procedure.
Transcriptomic Response of the Liver Tissue in Trachinotus ovatus to Acute Heat Stress

Simple Summary In this study, the transcriptomic response of Trachinotus ovatus liver was observed against high-temperature stress. Through differential expression and short time-series expression miner (STEM) analyses, some high-temperature-related genes and biological pathways were screened, which were mainly related to protein balance, hypoxia adaptation, and energy metabolism. Our results suggest that protein dynamic balance and function, hypoxia adaptation, and energy metabolism transformation are crucial in response to acute high-temperature stress. These results contribute to understanding the molecular response mechanism of T. ovatus under acute heat stress and provide novel insights into the selection and breeding of heat-tolerant cultivars and the high-quality development of aquaculture. Abstract Trachinotus ovatus is a major economically important cultured marine fish in the South China Sea. However, extreme weather and increased culture density result in uncontrollable problems, such as increases in water temperature and a decline in dissolved oxygen (DO), hindering the high-quality development of aquaculture. In this study, liver transcriptional profiles of T. ovatus were investigated under acute high-temperature stress (31 °C and 34 °C) and normal water temperature (27 °C) using RNA sequencing (RNA-Seq) technology. Differential expression analysis and STEM analysis showed that 1347 differentially expressed genes (DEGs) and four significant profiles (profiles 0, 3, 4, and 7) were screened, respectively. Of these DEGs, some genes involved in heat shock protein (HSPs), hypoxic adaptation, and glycolysis were up-regulated, while some genes involved in the ubiquitin-proteasome system (UPS) and fatty acid metabolism were down-regulated. Our results suggest that protein dynamic balance and function, hypoxia adaptation, and energy metabolism transformation are crucial in response to acute high-temperature stress. Our findings contribute to understanding the molecular response mechanism of T. ovatus under acute heat stress, which may provide some reference for studying the molecular mechanisms of other fish in response to heat stress.
High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression

We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on l1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an l-constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n. Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency.
Evaluation of Psoas Muscle Atrophy and the Degree of Fat Infiltration After Unilateral Hip Arthroplasty

Objective: Atrophy of the muscles around the hip and thigh has been reported in patients with hip osteoarthritis (OA). Total hip arthroplasty (THA) reduces pain and improves quality of life and activity levels. Muscle strength of the hip and thigh also improves after THA. This study aimed to determine whether there is significant psoas muscle atrophy and to evaluate the degree of fat infiltration after unilateral hip arthroplasty. Subject and methods: Patients who underwent unilateral total hip arthroplasty for primary hip osteoarthritis and who had lumbar vertebra magnetic resonance imaging (MRI) for any reason in the one-year preoperative and postoperative period were evaluated retrospectively. The degree of fat infiltration was also graded visually based on a modified Goutallier rating system. Results: The study was conducted with a total of 58 patients aged between 38 and 75, including 15 males and 43 females. Compared to the preoperative psoas muscle area values on the operated sides of the patients participating in the study, the decrease in the postoperative psoas muscle area was found to be statistically significant (p:0.000; p<0.05). Furthermore, the decrease in psoas muscle area on the non-operated side of the patients was also statistically significant (p:0.000; p<0.05). There was also a positive correlation between preoperative and postoperative psoas muscle areas (p:0.000; p<0.05). Conclusion: Early identification of psoas muscle mass reduction may allow for a more proactive psoas strength improvement program to improve post-operative function and mobility.
Treatment of Hepatocellular Carcinoma (HCC)

Hepatocellular carcinoma (HCC) or hepatoma is the most common type of primary liver cancer, followed by malignant lymphoma, fibrosarcoma, and hemangioendotelioma. In high-incidence areas, the most common cause is HBV transmitted at birth, and HCC is diagnosed about a decade earlier than in North America and Europe, where the most common etiology is HCV acquired later in life. This research uses the contrast review method, meaning the journal review method by finding differences between several research journals and then drawing conclusions, by taking several research journals that use the same sample but different research methods, different reagents used or different data applications used. In this study it was concluded that Hepatocellular carcinoma (HCC) or hepatoma is the most common type of primary malignant tumor of the liver. Hepatitis B and C infection, alcohol, aflatoxin B1, illicit drugs, cirrhosis, obesity, and diabetes mellitus are all risk factors for HCC.