Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica

This study develops a site specific burn severity modelling using remote sensing techniques to develop severity patterns on vegetation and soil in the fire prone region of the Palo Verde National Park in Guanacaste, Costa Rica. Terrain physical features, soil cover, and scorched vegetation characteristics were examined to develop a fire risk model and to quantify probable burned areas. Spectral signatures of affected areas were captured through multi-spectral analysis; i.e., Normalized Burn Ratio (NBR), Landsat derived differenced Normalized Burn Ratio (dNBR) and relativized dNBR (RdNBR). A partial unmixing algorithm, Mixture Tuned Matched Filtering (MTMF) was used to isolate endmembers for scorched vegetation and soil. The performance of dNBR and RdNBR for predicting ground cover components was acceptable with an overall accuracy of 84.4% and Cohen’s Kappa 0.82 for dNBR and an overall accuracy of 89.4% and Cohen’s Kappa 0.82 for RdNBR. Landsat derived RdNBR showed a strong correlation with scorched vegetation (r2 = 0.76) and moderate correlation with soil cover (r2 = 0.53), which outperformed dNBR. The ecologically diverse and unique park area is threatened by wetland fires, which pose a potential threat to various species. Human induced fires by poachers are a common occurrence in such areas to gain access to these species. This paper aims to prioritize areas that are at a higher risk from fire and model spatial adaptations in relation to the direction of fire within the affected wetlands. This assessment will help wildlife personnel in managing disturbed wetland ecosystems.
Influence of lack of posterior occlusal support on cognitive decline among 80‐year‐old Japanese people in a 3‐year prospective study

Previous studies have reported significant associations between tooth loss or periodontal status and cognitive function; however, animal experimental studies have shown that occlusion might be a more important factor in cognitive decline. The purpose of the present study was to investigate the influence of a lack of posterior occlusal support by residual teeth on the decline of cognitive function over a 3‐year period among 80‐year‐old Japanese people.
On the Beam Width Optimization for the Ergodic Capacity of FSO Channels With Misalignment Errors Modeled by Beckmann Distributions

Despite the growing interest in underwater and space applications, terrestrial free-space optical (FSO) links still remain of main priority, and the performance of such systems is deeply dependent not only on weather conditions but also on atmospheric scintillation and misalignment errors. In this paper, a thorough investigation of the capacity performance of FSO systems in the presence of generalized pointing errors is carried out. Novel approximate closed-form solutions to estimate the capacity from low to high signal-to-noise ratio (SNR) are for the first time presented and carefully analyzed when pointing errors follows the well-known Beckmann distribution. By using the developed expressions, it is concluded that the capacity can be optimized according to a specific beam width, achieving an improvement on the order of $\approx10$ dB. The most striking thing is that the optimum beam width value depends on the SNR range, i.e., on atmospheric scintillation and misalignment errors, deriving different optimum values at low, medium, and high SNR. Analytical results are verified by Monte Carlo simulation results.
Channel Measurement, Simulation, and Analysis for High-Speed Railway Communications in 5G Millimeter-Wave Band

More people prefer to using rail traffic for travel or for commuting due to its convenience and flexibility. As the record of the maximum speed of rail has been continuously broken and new applications are foreseen, the high-speed railway (HSR) communication system requires higher data rate with seamless connectivity, and therefore, the system design faces new challenges to support high mobility. Millimeter-wave (mmWave) technologies are considered as candidates to provide wideband communication. However, mmWave is rarely explored in HSR scenarios. In this paper, channel characteristics are studied in the 5G mmWave band for typical HSR scenarios, including urban, rural, and tunnel, with straight and curved route shapes. Based on the wideband measurements conducted in the tunnel scenario by using the “mobile hotspot network” system, a 3-D ray tracer (RT) is calibrated and validated to explore more channel characteristics in different HSR scenarios. Through extensive RT simulations with 500-MHz bandwidth centered at 25.25 GHz, the power contributions of the multipath components are studied, and the dominant reflection orders are determined for each scenario. Path loss is analyzed, and the breakpoint is observed. Other key parameters, such as Doppler shifts, coherence time, polarization ratios, and so on, are studied. Suggestions on symbol rate, sub-frame bandwidth, and polarization configuration are provided to guide the 5G mmWave communication system design in typical HSR scenarios.
Increasing Boosting Effectiveness with Estimation of Distribution Algorithms

Ensemble learning is the machine learning paradigm that aims at integrating several base learners into a single system under the assumption that the collective consensus outperforms a single strong learner, be it regarding effectiveness, efficiency, or any other problem-specific metric. Ensemble learning comprises three main phases: generation, selection, and integration, and there are several possible (deterministic or stochastic) strategies for executing one or more of those phases. In this paper, our focus is on improving the predictive accuracy of the well-known AdaBoost algorithm. By using its former voting weights as starting point in a global search carried by an Estimation of Distribution Algorithm, we are capable of improving AdaBoost up to $\approx 11$ % regarding predictive accuracy in a thorough experimental analysis with multiple public datasets.
Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face Biometrics

Biometrics have become important for mobile authentication, e.g., to unlock devices before using them. One way to protect biometric information stored on mobile devices from disclosure is using embedded smart cards (SCs) with biometric match-on-card (MOC) approaches. However, computational restrictions of SCs also limit biometric matching procedures. We present a mobile MOC approach that uses offline training to obtain authentication models with a simplistic internal representation in the final trained state, where we adapt features and model representation to enable their usage on SCs. The pre-trained model can be shipped with SCs on mobile devices without requiring retraining to enroll users. We apply our approach to acceleration based mobile gait authentication as well as face authentication and compare authentication accuracy and computation time of 16 and 32 bit Java Card SCs. Using 16 instead of 32 bit SCs has little impact on authentication performance and is faster due to less data transfer and computations on the SC. Results indicate 11.4 and 2.4-5.4 percent EER for gait respectively face authentication, with transmission and computation durations on SCs in the range of 2 s respectively 1 s. To the best of our knowledge, this work represents the first practical approach towards acceleration based gait MOC authentication.
Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Green Leaf Area Index Retrieval

Vegetation biophysical parameter retrieval is an important earth remote sensing system application. In this paper, we studied the potential impact of the addition of new spectral bands in the red edge region in future Landsat satellites on agroecosystem canopy green leaf area index (LAI) retrieval. The test data were simulated from SPARC ‘03 field campaign HyMap hyperspectral data. Three retrieval approaches were tested: empirical regression based on vegetation index, physical model-based look-up-table (LUT) inversion, and machine learning. The results of all three approaches showed that a potential new spectral band located between the Landsat-8 Operational Land Imager (OLI) red and NIR bands slightly improved the agroecosystem green LAI retrieval accuracy (R2 of 0.787 vs. 0.810 for vegetation index approach, 0.806 vs. 0.828 for LUT inversion approach, and 0.925 vs. 0.933 for machine learning approach). The results of this work are consistent with the conclusions from previous research on the value of Sentinel-2 red edge bands for agricultural green LAI retrieval.
Sentence interpretation by typically developing Vietnamese–English bilingual children

ABSTRACT We examined developing bilinguals' use of animacy and word order cues during sentence interpretation tasks administered in each of their languages. Participants were 6- to 8-year-old children who learned Vietnamese as a first language and English as a second language (n = 23). Participants listened to simple sentences and identified the agent or “doer” of the action. English-only peers (n = 23) served as a comparison group. Results indicated that the bilingual group relied more on animacy than the English-only group when interpreting sentences in English and that the bilingual group used a blending or “amalgamation” of cues to interpret English and Vietnamese sentences. Significant within-group variation in cue preference was investigated as a function of age and proficiency in the first language and second language.
How Innovation Strategies Affect the Role of Openness under Knowledge Spillovers?

The current study testified the impact of enterprises' innovation openness on their innovative capacity. Drawing on dual-process model, we suggested that enterprises with higher openness held a stronger innovative capacity and knowledge spillovers played a mediating role between openness and innovative capacity. We further proposed that firms might suffer a decline of innovativeness provided attaching excessive importance to exploitation rather than exploration. In order to examine our predictions, we used investigation data answered by middle or senior managers from 205 emerging enterprises in China, and then performed analyses by means of hierarchical regression. Consistent with supposed framework, we found that the effects of knowledge spillovers mediated the positive relationship between openness and innovative capacity. In addition, exploitative strategy negatively moderated the direct relationship between knowledge spillover effects and innovative capacity and the indirect relationship between openness and innovative capacity. Finally, the academic and practical implications of these findings as well as future research orientations are discussed.
Polemics of pioglitazone: an appraisal in 2015

Pioglitazone is an inexpensive and effective oral drug for the treatment of Type 2 diabetes. It addresses insulin resistance, one of the core pathophysiological defects in Type 2 diabetes, at both the adipose tissue and skeletal muscle level. As a majority of Type 2 diabetics classically exhibit higher insulin resistance, pioglitazone may strike exactly at the Achilles heel in this core pathogenesis. However, with the emerging association of bladder cancer with pioglitazone, French and German regulators were the first to ban or restrict pioglitazone use in 2011. The Indian regulators also suspended pioglitazone, although this ban was revoked within a month. Recently, a 10-year longitudinal study commissioned by US FDA found no association between bladder cancer and pioglitazone. Nevertheless, this controversy created a huge outcry in the medical fraternity. This review article is an overview of the development of this topic and an attempt to provide perspective on this contemporary issue.
Reciprocal relationships between self‐efficacy, outcome satisfaction, and attendance at an exercise programme

OBJECTIVES The objectives of this study were to (i) investigate directional influences of self-efficacy, outcome satisfaction, and attendance during an exercise programme and (ii) examine the predictive capabilities of self-efficacy and outcome satisfaction on participant dropout. METHODS Adults aged 35-65 years were recruited to a 12-month exercise programme. Self-efficacy was collected at baseline, three, six, nine, and 12 months, and outcome satisfaction at the same time-points except baseline. Cross-lagged panel analyses and logistic regression analyses were conducted to examine the primary and secondary objectives, respectively. RESULTS Coping and scheduling self-efficacy had stronger reciprocal relationships with outcome satisfaction than task self-efficacy, although the strength of these relationships varied across time. Initially, task self-efficacy predicted programme attendance. Midway through the programme, outcome satisfaction predicted attendance, and by the end of the programme, coping self-efficacy was the strongest predictor of attendance. Self-efficacy and outcome satisfaction did not predict programme dropout. CONCLUSIONS The results support the reciprocal relationship between outcome satisfaction and scheduling and coping self-efficacy and highlight the importance of sequentially targeting different beliefs and skills to facilitate successful initiation and maintenance of exercise behaviour. Statement of contribution What is already known on this subject? Self-efficacy and outcome satisfaction are related to exercise behaviour. The relationship between self-efficacy and outcome satisfaction are typically considered unidirectional. The longitudinal relationships among self-efficacy, outcome satisfaction, and physical activity participation are unclear. What does this study add? Outcome satisfaction had reciprocal relationships with both coping and scheduling self-efficacy for exercise. The importance of self-efficacy and outcome satisfaction to exercise attendance changes over time. Task self-efficacy was the strongest predictor of initial programme attendance. Midway through the programme, outcome satisfaction and task self-efficacy were the strongest predictors of attendance, and by the end of the programme, coping self-efficacy was the strongest predictor.
Reimagine Teacher Training for Performing in Information-Oriented Society (FabLab)

The changes in today's society have great impact on teacher training system in higher education. A graduate is to be ready to manage learning and teaching in highly technological, excessively informative and dynamic educational environment. New approaches to teacher training and delivery are aimed at creating facilities contributing to high level of teachers' ICT competency, their experience in project activities and creative thinking. The article describes the results of implementing FabLab (Students‘ Project Bureau “Centre for Interactive Learning”) in teacher training at Far Eastern Federal University (School of Education). The article is focused on the delivery models of the FabLab as well as on its benefits in the system of teacher preparation.
Assessing the level of institutionalization of donor-funded anti-retroviral therapy (ART) programs in health facilities in Uganda: implications for program sustainability

ABSTRACT Background: In the context of declining international assistance for ART scale-up in Sub-Saharan Africa, the institutionalization of ART programs through integrating them in the organizational routines of health facilities is gaining importance as a program sustainability strategy. Objective: The aims of this study were; (i) to compare the level of institutionalization of ART programs in health facilities in Uganda and (ii) to explore reasons for variations in the degree of program institutionalization. Methods: In Phase One, we utilized Level of Institutionalization Scales developed by Goodman (1993) to measure the degree of institutionalization of ART interventions in 195 health facilities across Uganda. The 45-item questionnaire measured institutionalization based on four sub-systems (production, maintenance, supportive, managerial) theorized to make up an organization assessed against two levels of institutionalization; routines (lowest) niche saturation (highest). In Phase Two, four health facilities were purposively selected (2 with the highest and 2 with the lowest institutionalization scores) for a multiple case-study involving semi-structured interviews with ART clinic managers(n = 32), on-site observations and document review. Results: The two highest scoring health facilities had a longer HIV intervention implementation history of between 8 and 11 years. The highest scoring cases associated intervention institutionalization with sustained workforce trainings in ART management, the retention of ART-trained personnel and generating in-house ART manuals. The turnover of ART-proficient staff was identified as a barrier to intervention institutionalization in the lowest-ranked cases. Significant differences in organizational contexts were identified. The two highest-ranked health facilities were well-established, higher-tier hospitals while the lowest scoring health facilities were lower-level health facilities. Conclusions: The level of institutionalization of ART interventions appeared to be differentiated by level of care in the Ugandan health system. Interventions aimed at strengthening program institutionalization in lower-level health centers at the level of human resources for health could enhance ART scale-up sustainability.
Spatially-weighted Anomaly Detection

Many types of anomaly detection methods have been proposed recently, and applied to a wide variety of fields including medical screening and production quality checking. Some methods have utilized images, and, in some cases, a part of the anomaly images is known beforehand. However, this kind of information is dismissed by previous methods, because the methods can only utilize a normal pattern. Moreover, the previous methods suffer a decrease in accuracy due to negative effects from surrounding noises. In this study, we propose a spatially-weighted anomaly detection method (SPADE) that utilizes all of the known patterns and lessens the vulnerability to ambient noises by applying Grad-CAM, which is the visualization method of a CNN. We evaluated our method quantitatively using two datasets, the MNIST dataset with noise and a dataset based on a brief screening test for dementia.
EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs

Background: Polycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic sub-unit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are character-ized by high tissue-specificity; however, little is known about the tissue profile of the EZH2-interacting lncRNAs. Objective: Here we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno-precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In ad-dition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, ma-jority of the lncRNAs were firstly reported to be associated with EZH2. Conclusion: Our findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation
Learning Depth with Convolutional Spatial Propagation Network

In this paper, we propose the convolutional spatial propagation network (CSPN) and demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operations, in which the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). Compare to the previous state-of-the-art (SOTA) linear propagation model, i.e., spatial propagation networks (SPN), CSPN is <inline-formula><tex-math notation="LaTeX">$\mathbf 2$</tex-math><alternatives><mml:math><mml:mn mathvariant="bold">2</mml:mn></mml:math><inline-graphic xlink:href="cheng-ieq1-2947374.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathbf 5\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant="bold">5</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="cheng-ieq2-2947374.gif"/></alternatives></inline-formula> faster in practice. We concatenate CSPN and its variants to SOTA depth estimation networks, which significantly improve the depth accuracy. Specifically, we apply CSPN to two depth estimation problems: depth completion and stereo matching, in which we design modules which adapts the original 2D CSPN to embed sparse depth samples during the propagation, operate with 3D convolution and be synergistic with spatial pyramid pooling. In our experiments, we show that all these modules contribute to the final performance. For the task of depth completion, our method reduce the depth error over 30 percent in the NYU v2 and KITTI datasets. For the task of stereo matching, our method currently ranks <inline-formula><tex-math notation="LaTeX">$\mathbf 1st$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant="bold">1</mml:mn><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="cheng-ieq3-2947374.gif"/></alternatives></inline-formula> on both the KITTI Stereo 2012 and 2015 benchmarks.
Construction and characterization of an OmpH-deficient mutant of Pasteurella multocida strain X-73

ABSTRACT A capsule-defective mutant strain PBA129 of Pasteurella multocida was constructed by electroporation of phagemid containing the coding region of the antisense RNA of the ompH gene into the wild type strain X-73 (serovar A:1) of P. multocida. The pathogenicity and protective potency of the mutant against homologous and heterologous challenge in mice and chickens were characterized. Greyish colonies of the mutant, indicating lower capsule thickness, on selective dextrose starch agar were observed under an obliquely transmitted light stereomicroscope and compared to iridescent colonies of the wild type strain X-73. Strain PBA129 had lower capsule thickness than the wild type strain as observed with an electron microscope. Strain PBA129 was apparently attenuated, as mice and chickens inoculated with the bacteria at 108 CFU survived. Protection was observed in both mice and chickens inoculated with strain PBA129 upon challenge exposure to avian P. multocida strains X-73 and P-1059 (serovar A:3), respectively. In conclusion, the mutant strain PBA129 of P. multocida strain X-73 was completely attenuated, and it was possible to induce sufficient protection against avian P. multocida strains.