Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic elaboration of trial function bases for the study of planar structures

This paper presents an integral method in combination with Green's functions and the boundary-element method to characterize a rectangular waveguide with electric or magnetic walls, loaded with a conductor of arbitrary cross section. The results provided by this method are in good agreement with available data in the literature. The modes of this type of waveguide are determined in the case of an arbitrarily shaped inner conductor with no consideration of size. The existence of the TEM mode has been verified. The modes calculated by this method are used as numerical basis functions in other applications. It is shown that they can be used to determine the resonant frequencies of a metallic patch or the input impedance of a planar antenna fed by a coaxial line. In this last case, the theoretical results are confronted with the experimental results.
Distributed adaptive fault section estimation system for large-scale power networks

The distributed radial basis function neural networks (RBF NN) can effectively solve fault section estimation (FSE) in large-scale power networks. However when the network expands or topology changes, the RBF NN has to be totally retrained, which is time-consuming and becomes a bottleneck in its applications. In this paper, functional equivalence between a RBF NN and a companion fuzzy system (CFS) is built up throughout the neural network training process, therefore the RBF NN retraining issue under network expansion and topology change can be solved effectively and efficiently through useful knowledge extraction from the old CFS and insertion back to the new CFS piece by piece. The corresponding FSE system has been implemented and tested in the IEEE 118-bus power system. The simulation results show that the suggested approach for RBF NN retraining works successfully and efficiently in the case of power network expansion and topology change, which significantly improves the application potential of RBF NN in FSE of real power systems.
Predicting Soil Water Content on Rainfed Maize through Aerial Thermal Imaging

Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this purpose. In this study, we investigated the relationship between canopy temperature retrieved from unmanned aerial vehicles (UAV) based thermal imagery with soil and plant attributes, using a rainfed maize field as the area of study. The flight mission was conducted during the late vegetative stage and at solar noon, when a considerable soil water deficit was detected according to the soil water balance model used. While the images were being taken, soil sampling was conducted to determine the soil water content across the field. The sampling results demonstrated the spatial variability of soil water status, with soil volumetric water content (SVWC) presenting 10.4% of variation and values close to the permanent wilting point (PWP), reflecting CT readings that ranged from 32.8 to 40.6 °C among the sampling locations. Although CT correlated well with many of the physical attributes of soil that are related to water dynamics, the simple linear regression between CT and soil water content variables yielded coefficients of determination (R2) = 0.42, indicating that CT alone might not be sufficient to predict soil water status. Nonetheless, when CT was combined with some soil physical attributes in a multiple linear regression, the prediction capacity was significantly increased, achieving an R2 value = 0.88. This result indicates the potential use of CT along with certain soil physical variables to predict crop water status, making it a useful tool for studies exploring the spatial variability of in-season drought stress.
System Characteristic Facilitates the Acceptance of Information Technology in Middle East culture

Purpose: In order for the organizations to realize the full benefits of the application of IT, it is important for those who have to use the technology to accept it. This study aims to investigate the factors influencing the acceptance of technology in public sector such as System characteristics (information quality) in the theory of technology acceptance in order to provide better understanding for the factors influencing the acceptance of information technology among the individual perceptions. In addition, The study is to test the success of the technology acceptance model in Yemen culture. Knowing what makes employees accept or resist to some technology is important so that the investment made on the IT application yields a good and satisfactory return. The study also aimed to highlight the role of System characteristics such as information quality in facilitating information transaction between top management and government. Methodology: survey questionnaire was distributed to 53 government utilities and 357 cases were used in the analysis. Structural Equition Modeling AMOS 18 was used for the analysis of the proposed model. Findings the study has provided empirical evidence for the positive effect of System characteristics such as information quality on preceived usefulness and ease of use wich in turn has positive effect on the intention behaviour to use towards the actual usage for the technology. the study confirmed the theory of TAM and showed its potential capability in the Middle East, particularly in Yemen. Empirical evidence has shown that the employees and managers have the capability to use the technology. Significance: This study has provided empirical evidence for the effects of new technology determinants in the government sector. In particular, it has successfully revealed that System characteristics such as information quality is important determinants in influencing the adoption of technologies. Also for the practicul usage, the study contributed to the empirical knowledge to increase the success rate for accepting or adopting the information technology in the government sector.
Shifting moods on Sina Weibo: The first 12 weeks of COVID-19 in Wuhan

Better understanding of social media uses in crisis situations can help improve disaster management by policy-makers, organizations, businesses, and members of the public. It can also build theoretical understanding of how social life and citizenship incorporate social media usage. This study tracks the evolution of public sentiment in Wuhan, China, during the first 12 weeks after the identification of COVID-19 on the Chinese microblogging platform Sina Weibo. Data consist of 133,079 original Sina Weibo posts dealing with the novel coronavirus. The relative prevalence of eight different emotion groups is traced longitudinally using the ROST Content Mining System and the Emotion Vocabulary of Dalian University of Technology. The study finds a progression from confusion/fear, to disappointment/frustration, to depression/anxiety, then finally to happiness/gratitude. It argues that this progression indexes the changing affective energies of digital medical citizenship, which in turn indicates the context for intervention in future crises.
Investigation of microRNA-10b values for the discrimination of metastasis due to melanoma

Background: Melanoma is one of the most invasive cutaneous cancers with characteristics such as rapid progression and distant metastasis. The early diagnosis and staging of melanoma can help better manage the patients. The current study is aimed to assess the values of microRNA-10b (miRNA-10b) in the discrimination of metastatic melanomas. Materials and Methods: The current cross-sectional study has been conducted on forty patients diagnosed with melanoma since 2011. Cell culture of melanoma cell lines derived from the cancerous tissue, including WM115, BLM, K1735, WM793, and A375M, was cultured. In order to assess miRNA-10b levels, the real-time polymerase chain reaction was utilized. The absence (n = 20)/presence (n = 20) of metastasis was diagnosed with chest computed tomography or chest X-ray. The values of miRNA-10b for the discrimination of metastasis incidence were assessed. Results: The demographic characteristics, including age and gender of the metastatic and nonmetastatic patients, were similar (P > 0.05). The specimen cultures were positive for miRNA-10b in 14 (35%) of the metastatic cases versus 4 (20%) of the nonmetastatic ones (P = 0.004). The quantitative analysis of miR-2b revealed significantly higher levels in metastatic cases (−1.59 ± 1.13 in metastatic vs. −0.16 ± 0.67 in nonmetastatic cases; P = 0.001). The measured area under the curve for the value of miRNA-10b was 0.923 (P < 0.001; 95% confidence interval: 0.811–1) with sensitivity and specificity of 100% and 94.4%. Conclusion: Based on this study, metastatic melanoma was associated with elevated levels of miRNA-10b. This marker had the sensitivity and specificity of 100% and 94.4% for the discrimination of metastatic melanoma from nonmetastatic ones.
A design procedure and predictive models for solution crystallisation processes

Useful components and deficiencies of conventional crystallisation design procedures and models are identified. A systematic procedure consisting of a hierarchical decomposition into design of the product, task, flowsheet and individual crystalliser is presented. At each level the relevant specifications, variables and necessary knowledge are identified. For the flowsheet and crystalliser level a predictive crystallisation model is presented to estimate kinetic parameters from experimental CSD data and to analyse the behaviour of design alternatives. Currently, the procedure is applicable to cooling, flashcooling and evaporative crystallisation. The developed model is restricted to systems with little agglomeration. Validation of the proposed design procedure and models requires industrial case studies.
Prognostic and Genomic Analysis of Proteasome 20S Subunit Alpha (PSMA) Family Members in Breast Cancer

The complexity of breast cancer includes many interacting biological processes, and proteasome alpha (PSMA) subunits are reported to be involved in many cancerous diseases, although the transcriptomic expression of this gene family in breast cancer still needs to be more thoroughly investigated. Consequently, we used a holistic bioinformatics approach to study the PSMA genes involved in breast cancer by integrating several well-established high-throughput databases and tools, such as cBioPortal, Oncomine, and the Kaplan–Meier plotter. Additionally, correlations of breast cancer patient survival and PSMA messenger RNA expressions were also studied. The results demonstrated that breast cancer tissues had higher expression levels of PSMA genes compared to normal breast tissues. Furthermore, PSMA2, PSMA3, PSMA4, PSMA6, and PSMA7 showed high expression levels, which were correlated with poor survival of breast cancer patients. In contrast, PSMA5 and PSMA8 had high expression levels, which were associated with good prognoses. We also found that PSMA family genes were positively correlated with the cell cycle, ubiquinone metabolism, oxidative stress, and immune response signaling, including antigen presentation by major histocompatibility class, interferon-gamma, and the cluster of differentiation signaling. Collectively, these findings suggest that PSMA genes have the potential to serve as novel biomarkers and therapeutic targets for breast cancer. Nevertheless, the bioinformatic results from the present study would be strengthened with experimental validation in the future by prospective studies on the underlying biological mechanisms of PSMA genes and breast cancer.
Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market

ABSTRACT This study proposes a methodology incorporating machine learning algorithms to predict stock returns and construct portfolios that beat the market. The performance evaluation is based on the statistical metrics as well as the return and Sharpe ratios of the portfolios. Additionally, a new performance evaluation metric, Safe-Side, is introduced to address the needs of conservative portfolio managers and investors. The results provide strong evidence that the machine learning algorithms can be used to predict the stock returns with approximately 86% classification accuracy. The proposed methodology also provides guidance for investors and portfolio managers for their portfolio selection problems.
Improving the Prediction of Asset Returns With Machine Learning by Using a Custom Loss Function

Not all errors from models predicting asset returns are equal in terms of impact on the efficiency of the algorithm: a small error could trigger poor investment decisions while a significant error has no financial consequences. This economic asymmetry, critical for assessing the performance of algorithms, can usefully be replicated within the machine learning algorithms itself through the loss function to improve its prediction capability. In this article: (a) we analyze symmetric and asymmetric loss functions for deep learning algorithms. We develop custom loss functions that mimic the asymmetry in economic consequences of prediction errors. (b) We compare the efficiency of these custom loss functions with MSE and the linearexponential loss “LinEx”. (c) We present an efficient custom loss function that significantly improves the prediction of asset returns with improved risk-return metrics (like Sharpe ratio twice better), and which we confirm to be robust.
A review of the fabrication and properties of erbium-doped fibers for optical amplifiers

Erbium-doped fiber has become the central component of nearly all optical amplifiers. Applications reported include repeaters, power amplifiers, preamplifiers, and distributed amplifiers. To date, nearly all the fiber used in these devices has been silica based and fabricated by variations on the major telecommunications fiber technology. Disadvantages of the silica-based host glass, such as low solubility of the rare-earth ions and narrowband fluorescence, have been carefully addressed and solutions have been found to overcome these potential drawbacks. Details of the current status of fabrication methods, matching particular fibers for specific applications, together with optimizing the fiber for high efficiency, are presented. >
EQUILIBRIUM TOTAL RADIATION MEASUREMENTS IN AIR AT SUPERORBITAL ENTRY VELOCITIES

Abstract : An experimental investigation was carried out to obtain data on the total radiance of air at elevated temperatures. The experiment was performed in a 3-in. I.D. combustion-driven shock tube. The heat-transfer model used in this investigation was a flat-nosed cylinder approximately 1.13 in. in diameter. The radiation from the gas in the shock layer was measured by a thinfilm gage placed behind a quartz window. The gage was placed about 0.5 in. behind the quartz window so that the radiating gas which the gage sees is a truncated cone with height equal to the shock-detachment distance. The data obtained are in good agreement with both the theoretical predictions by Nardone, Breene, Zeldin, and Riethof of General Electric and those by Meyerott, Sokoloff, and Nicholls of Lockheed Missiles and Space Company. (Author)
Effects of Noninvasive Low-Intensity Focus Ultrasound Neuromodulation on Spinal Cord Neurocircuits In Vivo

Although neurocircuits can be activated by focused ultrasound stimulation, it is unclear whether this is also true for spinal cord neurocircuits. In this study, we used low-intensity focused ultrasound (LIFU) to stimulate lumbar 4–lumbar 5 (L4–L5) segments of the spinal cord of normal Sprague Dawley rats with a clapper. The activation of the spinal cord neurocircuits enhanced soleus muscle contraction as measured by electromyography (EMG). Neuronal activation and injury were assessed by EMG, western blotting (WB), immunofluorescence, hematoxylin and eosin (H&E) staining, Nissl staining, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), somatosensory evoked potentials (SEPs), motor evoked potentials (MEPs), and the Basso–Beattie–Bresnahan locomotor rating scale. When the LIFU intensity was more than 0.5 MPa, LIFU stimulation induced soleus muscle contraction and increased the EMG amplitudes (P < 0.05) and the number of c-fos- and GAD65-positive cells (P < 0.05). When the LIFU intensity was 3.0 MPa, the LIFU stimulation led to spinal cord damage and decreased SEP amplitudes for electrophysiological assessment (P < 0.05); this resulted in coagulation necrosis, structural destruction, neuronal loss in the dorsal horn by H&E and Nissl staining, and increased expression of GFAP, IL-1β, TNF-α, and caspase-3 by IHC, ELISA, and WB (P < 0.05). These results show that LIFU can activate spinal cord neurocircuits and that LIFU stimulation with an irradiation intensity ≤1.5 MPa is a safe neurostimulation method for the spinal cord.
Latent Profiles of Reading Motivation in Adolescent Native-Spanish-Speaker Multilingual Learners

Abstract Reading motivation may elucidate discrepancies in reading achievement among multilingual high school students whose first language is Spanish. Utilizing a person-centered approach to investigate underlying reading motivation profiles among N = 174 adolescent native Spanish speakers (NSS), analyses yielded three latent profiles of reading motivation, including (a) an “Average” (AVG) class characterized by slightly above-average scores on the reading self-concept (RSC) factor and slightly below-average scores on the reading attitude (RA) factor, (b) an “Above Average” (AA) class characterized by above average scores on both RSC and RA factors, and (c) a “High RSC-Low RA” (HRSC-LRA) class characterized by the highest RSC factor scores and below-average RA factor scores. The three reading motivation profiles were significantly associated with demographic covariates (e.g., gender and length of residency in the U.S.), consistent with previous research. Further, latent profile membership was a significant predictor of English reading proficiency scores on the Assessing Comprehension and Communication in English State-to-State for English Language Learners (ACCESS for ELLs). We discuss practical implications and provide suggestions for future research.
Inequalities in Health: Methodological Approaches to Spatial Differentiation

The prevalence of inequalities in the general health position of communities can be assessed by using selected determinants. The aims of this article are three-fold: (1) to apply a comprehensive approach to the assessment of inequalities in the general health position of communities, (2) to determine the spatial differentiation of determinants, and (3) to present selected assessment methods and their impact on the results. To present a quantitative assessment of these inequalities in health status in communities, a composite indicator (Health Index) was developed. This Health Index is composed of 8 areas of evaluation and 60 indicators which include, amongst others, determinants of health status and healthcare at district level (LAU 1) in the Czech Republic. The data are evaluated using multicriteria decision-making methods (the WSA and TOPSIS methods). Findings suggest that, when all eight domains are assigned the same weight of one, the spatial differentiation among the districts is similar when using both methods. If different weightings are assigned to the districts, changes occur in both the index values and the rankings of the analyzed districts. For example, the allocation of weightings in both methods results in a rearrangement of the ranking of districts for which the Health Index is around the average.
The Optimal Cutoff Value of D-dimer Levels to Predict in Hospital Mortality in Severe Cases of Coronavirus Disease 2019

Introduction: Coronavirus disease 2019 (Covid-19) can cause coagulation parameters abnormalities such as an increase of D-dimer levels especially in severe cases. The purpose of this study is to determine the differences of D-dimer levels in severe cases of Covid-19 who survived and non-survived and determine the optimal cut-off value of D-dimer levels to predict in-hospital mortality. Method: Data were obtained from confirmed Covid-19 patients who were treated from June to September 2020. The Mann-Whitney U test was used to determine differences of D-dimer levels in surviving and non-surviving patients. The optimal cut-off value and area under the curve (AUC) of the D-dimer level in predicting mortality were obtained by the receiver operating characteristic curve (ROC) method. Results: A total of 80 patients were recruited in this study. Levels of D-dimer were significantly higher in non-surviving patients (median 3.346 mg/ml; minimum – maximum: 0.939 – 50.000 mg/ml) compared to surviving patients (median 1.201 mg/ml; minimum – maximum: 0.302 – 29.425 mg/ml), p = 0.012. D-dimer levels higher than 1.500 mg/ml are the optimal cut-off value for predicting mortality in severe cases of Covid-19 with a sensitivity of 80.0%; specificity of 64.3%; and area under the curve of 0.754 (95% CI 0.586 - 0.921; p = 0.010). Conclusions: D-dimer levels can be used as a predictor of mortality in severe cases of Covid-19.
Production and Health Management from Grazing to Confinement Systems of Largest Dairy Bovine Farms in Azores: A Farmers’ Perspective

Simple Summary This study aimed to evaluate differences and critical factors in production and health management between dairy cattle farms with fixed milk parlours (FMP), and mobile milk systems (MMS) from Azorean grasslands. According to the farmers’ perspective, calf diarrhea, calf pneumonia, infertility/poor reproductive management, and mastitis were the main problems that farms faced in 2020. FMP was associated with more advanced and mechanized production systems, with a higher adherence to preventive and biosecurity control programs, than traditional MMS farms. MMS farms also showed a greater vocation for dual-purpose farming (beef and milk), smaller herd sizes and more grazing time for cows. In conclusion, inherent and non-inherent differences in production and health management between FMP and MMS were quantified by authors. These results indicate that a greater adoption of preventive veterinary medicine and biosecurity measures should be taken, especially among MMS farms. The education of farmers should also be improved and stimulated. Abstract The intensification of bovine milk production in the Azores has led farmers to increase farm size and specialization in grasslands, implementing confined and semi-confined production systems. Fixed milking parlours (FMP) have progressively gained more popularity, at the expense of conventional mobile milking systems (MMS). The present study aimed to evaluate the associations between production and health management in dairy cattle farms, with FMP or MMS, in grasslands (São Miguel, Azores), according to the farmers’ perspective. A total of 102 questions about production and health management were surveyed in 105 farms with >30 dairy cows each. Farms with FMP were associated (p ≤ 0.05) with larger herd size, better facilities, and specialized management, however, the adoption of preventive and biosecurity measures should be improved by these farmers. MMS farms implemented a lower level of disease prevention or control programs, less frequent transhumance, and showed a wider vocation to dual-purpose (milk and cross beef) than FMP farms. In conclusion, MMS and FMP farms tried to optimize yield and economic viability in different ways using grasslands. Several biosecurity and health prevention constraints were identified for improvement.