Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenology of the Odonata assemblage in a Mediterranean stream in the north-eastern Iberian Peninsula

We investigated the Odonata species composition of larvae, final instar (F-0) exuviae, and adults occurring in a sequence of two different mesohabitats − pool and riffle − in a short stretch of a Mediterranean stream running down the Catalan Pre-Coastal Range (north-eastern Iberian Peninsula). We sampled them during the period 2017–2018 and recorded adults of 26 taxa and confirmed by the presence of final instar exuviae that at least 21 were breeding successfully in that environment. We describe, for the first time for this region and habitat type, larval growth patterns, timings of adult emergence, and flight periods of the most abundant species and characterize the life cycles of some of them. These life cycles can be classified into three types: i) univoltine with overwintering in the larval stage and emergence taking place in spring or summer of the following year ( Coenagrion mercuriale , C. puella , Ceriagrion tenellum , and Pyrrhosoma nymphula ); ii) predominantly univoltine of the previous type with a small fraction of the population being semivoltine ( Anax imperator and Orthetrum coerulescens , tentative conclusion); and iii) univoltine with overwintering in the egg stage, a short larval growth period in the following spring, emergence in early summer, a long maturation period, and reproduction postponed until late summer or early autumn ( Chalcolestes viridis and Sympetrum striolatum ). Our results may contribute to fill in gaps in the knowledge of voltinism and phenology of the life cycles of Odonata in correlation with latitude, geographical area, and habitat type.
Cowpea (VignaUnguiculata) Fast Establishment Enhances its Potential for Food Security and Short-Term Rotations in Semi-Arid Areas in a Changing Climate

Cowpea (Vignaunguiculata L. Walp.) is a drought resistant, multipurpose legume commonly used for food, fodder and soil fertility improvement. Cowpea and commonly cultivated multipurpose shrubs (Calliandracalothyrsus and Leucaenaleucocephala) were evaluated for their early growth establishment abilities by assessing nodulation, shoot and root biomass, root to shoot ratios and total plant biomass. The main objective of the study was to investigate how cowpea early growth compares with that of commonly grown multipurpose leguminous tree shrubs and assess the potential of the test plants for use in short term rotations in semi-arid areas. The experiment was laid down as a completely randomized block design replicated five times. Results obtained indicated that cowpea maintained higher values for all parameters measured in the test plants throughout the sampling period. For instance, cowpea accumulated highest shoot biomass of 1.2g compared to 1.08g and 1.04g accumulated by Leucaena and Calliandra, respectively. Root biomass was also highest for Cowpea 1.5g, followed by Leaucaena 1.2g and finally Calliandra. 0.9g. Cowpea root to shoot ratio was significantly (p<0.05) higher than that of Calliandra. These results indicated that cowpea could be a potential short rotation legume for cropping systems in the semi-arid areas in a changing climate.
Advances in Targeted Therapy and Immunotherapy for Non-small Cell Lung Cancer Based on Accurate Molecular Typing

The essence of precision medicine is to achieve the goal of “individualized treatment” through genotyping of patients and targeted therapy. At present, the pathogenic genes of non-small cell lung cancer (NSCLC) have been studied most thoroughly and targeted therapy based on genotyping has been the most successful. This paper focuses on the precision treatment of NSCLC based on genotyping, comparing gene detection methods and summarize the latest progress of NSCLC immunotherapy.
Review of novel energetic polymers and binders – high energy propellant ingredients for the new space race

ABSTRACT Current solid rocket propellant formulations still employ traditional ingredients utilized since the 1960s, such as hydroxyl terminated polybutadiene (HTPB). Recent advances in energetic polymer see many binders capable of providing higher specific impulse and burn rates over HTPB. As shown by calculations, even slight increases in specific impulse can drastically increase the maximum payload of a launch system. Therefore, replacing HTPB with energetic binders could provide heavy space missions the needed extra boost. Energetic binders could also be paired with chlorine-free energetic oxidizers to synergistically provide a specific impulse exceedingly higher than the current formulation while reducing pollution. A comprehensive evaluation of the synthesis, mechanical properties, and performance of various trending and overlooked energetic polymers is described. Several outstanding candidates show promising properties to replace HTPB.
Inflammatory biomarkers are associated with aetiology and predict outcomes in community-acquired pneumonia: results of a 5-year follow-up cohort study

Background Biomarkers may facilitate clinical decisions in order to guide antimicrobial treatment and prediction of prognosis in community-acquired pneumonia (CAP). We measured serum C-reactive protein, procalcitonin (PCT) and calprotectin levels, and plasma pentraxin 3 (PTX3) and presepsin levels, along with whole-blood white cell counts, at three time-points, and examined their association with microbial aetiology and adverse clinical outcomes in CAP. Methods Blood samples were obtained at hospital admission, clinical stabilisation and 6-week follow-up from 267 hospitalised adults with CAP. Adverse short-term outcome was defined as intensive care unit admission and 30-day mortality. Long-term outcome was evaluated as 5-year all-cause mortality. Results Peak levels of all biomarkers were seen at hospital admission. Increased admission levels of C-reactive protein, PCT and calprotectin were associated with bacterial aetiology of CAP, while increased admission levels of PCT, PTX3 and presepsin were associated with adverse short-term outcome. In univariate and multivariate regression models, white blood cells and calprotectin at 6-week follow-up were predictors of 5-year all-cause mortality. Conclusions Calprotectin emerges as both a potential early marker of bacterial aetiology and a predictor for 5-year all-cause mortality in CAP, whereas PCT, PTX3 and presepsin may predict short-term outcome. In 267 adults with community-acquired pneumonia, systemic calprotectin emerges as an early marker of bacterial aetiology and a predictor of 5-year mortality, whereas systemic procalcitonin, pentraxin 3 and presepsin are predictors of short-term outcome http://ow.ly/dz6S30nAFvn
Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain

Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.
Employability Predictive Model Evaluator Using PART and JRip Classifier

Employability is the most challenging outcome of higher education institution. It is evident that there are a number of information technology (IT) graduates that failed to address the need of information technology industry, hence, result to job mismatch and high unemployment rate. This presents the Employability Predictive Model Evaluator using PART and JRip classifier algorithm. The study also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for employability of IT graduates using PART and JRip classifier algorithm. The 4- year taken courses of graduates from school year 2013-2016 and employability status extracted from e-Graduate Tracer Survey (eGTS) were used as dataset of the study. The academic courses from the university registrar were clustered based on Commission on Higher Education of the Philippines information technology courses which includes General Education, IT Common Courses, IT Professional Courses, IT Elective Courses and On-the-Job Training Grade. JRip generated two (2) rules while PART generated seven (7) rules, which were used as the logical conditions in the development of Employability Predictive Model Evaluator for IT graduates.
A Need Analysis Study in Developing Multimedia for Functional Grammar Course

In developing multimedia, the need analysis takes an important role. There are some instructional designs in developing and designing multimedia. Instructional design is usually started by analyzing the students’ need. This study aimed to find out the needs of students on multimedia on Functional Grammar Course. The subject of the study was the English Department students who have attended Functional Grammar Course at Universitas Negeri Padang. The sample of the study was selected by using cluster sampling, and Educational 3 class was the sample. The data were collected by using questionnaire. The questionnaire of need analysis contains three components namely necessity, lack, and want. The indicators of the needs analysis questionnaire consist of the necessities on multimedia, lack in the content of functional grammar course, and wants on multimedia. Based on the findings of needs analysis, it showed that the students strongly agreed that the use of multimedia is needed in teaching-learning process. The result of this study will be used as reference in the next study in developing multimedia.
Proceeding-Kuala Lumpur International Business, Economics and Law Conference 6, Vol. 1. April 18 – 19, 2015. Hotel Putra, Kuala Lumpur, Malaysia

This paper proposes a conceptual framework to investigate the relationship between board diversity, in terms of gender diversity and members' age, and the firm performance measured by return on assets and Tobin’s Q in shareholding firms listed in Amman stock exchange. Previous studies focused on the importance of board diversity in enhancing and improving the firms' performance by supported the board of directors by younger directors with make balance with older directors. Also, previous studies pointed out that the younger directors have the courage to take bravery decisions. Regarding gender diversity, previous studies argued that the presence of women at board of directors may influence firm performance. Also, the women directors can introduce new skills, new ideas and enhancing the firms' performance.
Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.
Educational intervention for promoting stretching exercise behavior among a sample of Iranian office employees: applying the Health Promotion Model

Introduction Health Promotion Model (HPM) is one comprehensive model by which health behavior predicting factors could be determined. Purpose This study aimed to investigate the effects of a designed interventional program based on HPM on promoting stretching exercise (SE) behavior among office employees. Patients and methods This interventional designed study was carried out on 87 office employees who were working in the three health networks affiliated to Shahid Beheshti University of Medical Sciences in Tehran, Iran. These participants were randomly divided into intervention group (N=44) who complied with educational program based on HPM and control group (N=43) who did not receive the program. The data were collected through a demographic questionnaire and a researcher-made questionnaire based on HPM at three times of baseline, 3- and 6-month follow-ups and analyzed using SPSS version 19. Results Totally, 40 office employees with mean age of (37.70±7.40) in intervention and 37 ones with mean age of (35.97±7.34) in control group were assessed. At baseline, both groups were the same regarding the HPM constructs of perceived barriers, perceived self-efficacy, commitment to plan, and interpersonal influences (P>0.05). However, following intervention, there was a significant difference between two groups during 6-month follow-up in all these predictor factors (P<0.0001). Furthermore, the mean score of SE behavior in intervention group (22.43±6.50) was significantly better than the control group (15.45±0.93) (P<0.001). The pain severity also in intervention group (1±1.22) also was significantly less than control group (2±2.03) after 6-month follow-up (P<0.001). Conclusion The SE educational programs applied in this study were effective for promoting SE among office employees.
Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

Data-driven fault diagnosis is considered a modern technique in Industry 4.0. In the area of urban rail transit, researchers focus on the fault diagnosis of railway point machines as failures of the point machine may cause serious accidents, such as the derailment of a train, leading to significant personnel and property loss. This paper presents a novel data-driven fault diagnosis scheme for railway point machines using current signals. Different from any handcrafted feature extraction approach, the proposed scheme employs a locally connected autoencoder to automatically capture high-order features. To enhance the temporal characteristic, the current signals are segmented and blended into some subsequences. These subsequences are then fed to the proposed autoencoder. With the help of a weighting strategy, the seized features are weight averaged into a final representation. At last, different from the existing classification methods, we employ the local outlier factor algorithm to solve the fault diagnosis problem without any training steps, as the accurate data labels that indicate a healthy or unhealthy state are difficult to acquire. To verify the effectiveness of the proposed fault diagnosis scheme, a fault dataset termed “Cu-3300” is created by collecting 3300 in-field current signals. Using Cu-3300, we perform comprehensive analysis to demonstrate that the proposed scheme outperforms the existing methods. We have made the dataset Cu-3300 and the code file freely accessible as open source files. To the best of our knowledge, the dataset Cu-3300 is the first open source dataset in the area of railway point machines and our conducted research is the first to investigate the use of autoencoders for fault diagnosis of point machines.
Optimization of the Load of Transition Metal Oxides (Fe2O3, Co3O4, NiO and/or PdO) onto CeO2 Nanoparticles in Catalytic Steam Decomposition of n-C7 Asphaltenes at Low Temperatures

The main objective of this work is the catalyst optimization of Fe2O3-, Co3O4-, NiO- and/or PdO- (transition element oxides—TEO) functionalized CeO2 nanoparticles to maximize the conversion of asphaltenes under isothermal conditions at low temperatures (<250 °C) during steam injection processes. Adsorption isotherms and the subsequent steam decomposition process of asphaltenes for evaluating the catalysis were performed through batch adsorption experiments and thermogravimetric analyses coupled to Fourier-transform infrared spectroscopy (FTIR), respectively. The adsorption isotherms and the catalytic behavior were described by the solid-liquid equilibrium (SLE) model and isothermal model, respectively. Initially, three pairs of metal oxide combinations at a mass fraction of 1% of loading of CeNi1Pd1, CeCo1Pd1, and CeFe1Pd1 nanoparticles were evaluated based on the adsorption and catalytic activity, showing better results for the CeNi1Pd1 due to the Lewis acidity changes. Posteriorly, a simplex-centroid mixture design of experiments (SCMD) of three components was employed to optimize the metal oxides concentration (Ni and Pd) onto the CeO2 surface by varying the oxides concentration for mass fractions from 0.0% to 2.0% to maximize the asphaltene conversion at low temperatures. Results showed that by incorporating mono-elemental and bi-elemental oxides onto CeO2 nanoparticles, both adsorption and isothermal conversion of asphaltenes decrease in the order CeNi1Pd1 > CePd2 > CeNi0.66Pd0. 66 > CeNi2 > CePd1 > CeNi1 > CeO2. It is worth mentioning that bi-elemental nanoparticles reduced the gasification temperature of asphaltenes in a larger degree than mono-elemental nanoparticles at a fixed amount of adsorbed asphaltenes of 0.02 mg·m−2, confirming the synergistic effects between Pd and Fe, Co, and Ni. Further, optimized nanoparticles (CeNi0.89Pd1.1) have the best performance by obtaining 100% asphaltenes conversion in less than 90 min at 220 °C while reducing 80% the activation energy.
Long-Lived Dark Exciton Emission in Mn-Doped CsPbCl3 Perovskite Nanocrystals

The unusual temperature dependence of exciton emission decay in CsPbX3 perovskite nanocrystals (NCs) attracts considerable attention. Upon cooling, extremely short (sub-ns) lifetimes were observed and were explained by an inverted bright–dark state splitting. Here, we report temperature-dependent exciton lifetimes for CsPbCl3 NCs doped with 0–41% Mn2+. The exciton emission lifetime increases upon cooling from 300 to 75 K. Upon further cooling, a strong and fast sub-ns decay component develops. However, the decay is strongly biexponential and also a weak, slow decay component is observed with a ∼40–50 ns lifetime below 20 K. The slow component has a ∼5–10 times stronger relative intensity in Mn-doped NCs compared to that in undoped CsPbCl3 NCs. The temperature dependence of the slow component resembles that of CdSe and PbSe quantum dots with an activation energy of ∼19 meV for the dark–bright state splitting. Based on our observations, we propose an alternative explanation for the short, sub-ns exciton decay time in CsPbX3 NCs. Slow bright–dark state relaxation at cryogenic temperatures gives rise to almost exclusively bright state emission. Incorporation of Mn2+ or high magnetic fields enhances the bright–dark state relaxation and allows for the observation of the long-lived dark state emission at cryogenic temperatures.
Knowledge Adaptation for Efficient Semantic Segmentation

Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolution feature maps for preserving the detailed knowledge in dense estimation. Although reducing the feature map resolution (i.e., applying a large overall stride) via subsampling operations (e.g., polling and convolution striding) can instantly increase the efficiency, it dramatically decreases the estimation accuracy. To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride. To handle the inconsistency between the features of the student and teacher network, we optimize the feature similarity in a transferred latent domain formulated by utilizing a pre-trained autoencoder. Moreover, an affinity distillation module is proposed to capture the long-range dependency by calculating the non local interactions across the whole image. To validate the effectiveness of our proposed method, extensive experiments have been conducted on three popular benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly competitive baseline, our proposed method can improve the performance of a student network by 2.5% (mIOU boosts from 70.2 to 72.7 on the cityscapes test set) and can train a better compact model with only 8% float operations (FLOPS) of a model that achieves comparable performances.
THE IMPACT OF THE ACCOUNTING INFORMATION SYSTEM USAGE ON COMPANYS' E-BUSINESS EFFICIENCY

In this paper an analysis of the impact of the Accounting Information System usage on the company’s ebusiness efficiency is made. The analysis is based upon the secondary data on the application of the e-business in the companies in 29 European countries, as well as on the primary data obtained by a survey on 252 Croatian companies. We conducted a relevant empiric research (using Hi-square and Levene’s Test analyses) which gives the up-to-date information on the impact of Accounting Information System usage on ebusiness efficiency and made it possible not to reject the hypothesis that indeed Accounting Information System usage affects the e-business efficiency.
ACCEPTED FOR PUBLICATION IN JOURNAL OF MEMORY AND LANGUAGE Talker-specificity and adaptation in quantifier interpretation

Linguistic meaning has long been recognized to be highly context-dependent. Quantifiers like many and some provide a particularly clear example of contextdependence. For example, the interpretation of quantifiers requires listeners to determine the relevant domain and scale. We focus on another type of contextdependence that quantifiers share with other lexical items: talker variability. Different talkers might use quantifiers with different interpretations in mind. We used a webbased crowdsourcing paradigm to study participants’ expectations about the use of many and some based on recent exposure. We first established that the mapping of some and many onto quantities (candies in a bowl) is variable both within and between participants. We then examined whether and how listeners’ expectations about quantifier use adapts with exposure to talkers who use quantifiers in different ways. The results demonstrate that listeners can adapt to talker-specific biases in both how often and with what intended meaning many and some are used.