Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Ecological Need for Prescribed Fire in Michigan Using GIS-Based Multicriteria Decision Analysis: Igniting Fire Gaps

In fire-suppressed landscapes, managers make difficult decisions about devoting limited resources for prescribed fire. Using GIS-based multicriteria decision analysis, we developed a model assessing ecological need for prescribed fire on Michigan’s state-owned lands, ranging from fire-dependent prairies, savannas, barrens, and oak and pine forests to fire-intolerant mesic forests, and including a diversity of wetlands. The model integrates fine-scale field-collected and broad-scale GIS data to identify where prescribed fire needs are greatest. We describe the model’s development and architecture, present results at multiple scales, introduce the concepts of “fire gaps” and “fire sink”, and rate the fire needs of more than 1.8 million hectares into one of six fire needs classes. Statewide, fire needs increase with decreasing latitude. The highest and lowest needs occur in southwestern Michigan and the Upper Peninsula, respectively, but actual fire application rates for these regions are inverted. The model suggests burn rates should be increased 2.2 to 13.4 times to burn all lands with greater than high fire needs. The model identifies regional patterns; highlights specific sites; and illustrates the disparity of fire needs and fire application. The modeling framework is broadly applicable to other geographies and efforts to prioritize stewardship of biodiversity at multiple scales.
CTOS: Compiler Testing for Optimization Sequences of LLVM

Optimization sequences are often employed in compilers to improve the performance of programs, but may trigger critical compiler bugs, e.g., compiler crashes. Although many methods have been developed to automatically test compilers, no systematic work has been conducted to detect compiler bugs when applying arbitrary optimization sequences. To resolve this problem, two main challenges need to be addressed, namely the acquisition of representative optimization sequences and the selection of representative testing programs, due to the enormous number of optimization sequences and testing programs. In this study, we propose CTOS, a novel compiler testing method based on differential testing, for detecting compiler bugs caused by optimization sequences of LLVM. CTOS first leverages the technique Doc2Vec to transform optimization sequences into vectors to capture the information of optimizations and their orders simultaneously. Second, a method based on the region graph and call relationships is developed in CTOS to construct the vector representations of the testing program, such that the semantics and the structure information of programs can be captured simultaneously. Then, with the vector representations of optimization sequences and testing programs, a “centroid” based selection scheme is proposed to address the above two challenges. Finally, CTOS takes in the representative optimization sequences and testing programs as inputs, and tests each testing program with all the representative optimization sequences. If there is an output that is different from the majority of others of a given testing program, then the corresponding optimization sequence is deemed to trigger a compiler bug. Our evaluation demonstrates that CTOS significantly outperforms the baselines by up to <inline-formula><tex-math notation="LaTeX">$24.76\% \sim 50.57\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>24</mml:mn><mml:mo>. </mml:mo><mml:mn>76</mml:mn><mml:mo>%</mml:mo><mml:mo>∼</mml:mo><mml:mn>50</mml:mn><mml:mo>. </mml:mo><mml:mn>57</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="jiang-ieq1-3058671.gif"/></alternatives></inline-formula> in terms of the bug-finding capability on average. Within seven month evaluations on LLVM, we have reported 104 valid bugs within 5 types, of which 21 have been confirmed or fixed. Most of those bugs are crash bugs (57) and wrong code bugs (24). 47 unique optimizations are identified to be faulty and 15 of them are loop related optimizations.
Estimation of chemical composition, in vitro gas production, metabolizable energy, net energy lactation values of different peanut varieties and line by Hohenheim in vitro gas production technique

This study was carried out to determine the nutritional value of some selected peanut varieties and line in the Eastern Mediterranean Agricultural Research Institute of Adana, Turkey. The peanut varieties used were Gazipasa, Sultan, NC7, Cihangir, and Halisbey; while the peanut line was DA335/2011. The chemical composition, metabolizable energy (ME), net energy lactation (NEL) and organic matter digestibility (OMD) of the selected peanut varieties and line were determined through Hohenheim in vitro gas production technique. Incubation times for Hohenheim gas production technique were 3, 6, 9, 12, 24, 48, 72 and 96 hours. The analysis of variance (General Linear Model) was carried out using the SPSS package program. The differences among groups in terms of nutrient contents were found to be significant (P<ou=0.05), except for dry matter (DM) and hemicellulose (HC). The highest crude protein (CP) (40.13%) was found in Sultan variety, while the crude oil (CO) content was found to be between 21.32 and 31.01%. The ADF, NDF, and ADL content of the peanut varieties and line were within the ranges of 2.32-7.91%, 4.85-9.88%, and 0.43-2.62%, respectively. Conversely, the Sultan variety had the highest crude cellulose (CC) value, Cihangir variety was determined to contain the highest hemicellulose (HC) value. The differences in 24 hour gas and methane production among different peanut varieties and line were found to be not-significant. (P > 0.05).
PPTM: A Privacy-Preserving Trust Management Scheme for Emergency Message Dissemination in Space–Air–Ground-Integrated Vehicular Networks

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the space–air–ground-integrated network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, flexibility, reliability, and availability, which results in space–air–ground-integrated vehicular networks (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, and privacy preservation is also a primary concern in SAGIVNs. They have conflicting requirements and a satisfactory balance between them is urgently required. In this article, we propose a novel privacy-preserving trust management (PPTM) scheme for the emergency message dissemination in SAGIVNs. The proposed scheme can realize precise trust management and strong conditional privacy preservation simultaneously with low communication overhead, and can provide strong applicability, strong robustness, and multiple other attractive features. Furthermore, the exhaustive theoretical analysis and simulation evaluation are detailed. The results reveal that the proposed scheme is significantly superior to the existing schemes in several aspects.
Preliminary Multiple-Test Estimation, With Applications to k-Sample Covariance Estimation

Abstract Multisample covariance estimation—that is, estimation of the covariance matrices associated with k distinct populations—is a classical problem in multivariate statistics. A common solution is to base estimation on the outcome of a test that these covariance matrices show some given pattern. Such a preliminary test may, for example, investigate whether or not the various covariance matrices are equal to each other (test of homogeneity), or whether or not they have common eigenvectors (test of common principal components), etc. Since it is usually unclear what the possible pattern might be, it is natural to consider a collection of such patterns, leading to a collection of preliminary tests, and to base estimation on the outcome of such a multiple testing rule. In the present work, we therefore study preliminary test estimation based on multiple tests. Since this is of interest also outside k-sample covariance estimation, we do so in a very general framework where it is only assumed that the sequence of models at hand is locally asymptotically normal. In this general setup, we define the proposed estimators and derive their asymptotic properties. We come back to k-sample covariance estimation to illustrate the asymptotic and finite-sample behaviors of our estimators. Finally, we treat a real data example that allows us to show their practical relevance in a supervised classification framework.
Extreme Learning Machine Models for Predicting Student Performance

Predicting the individual performance of each student can provide valuable information as to which students are at greatest risk of failure or dropout, and consequently highlight which characteristics negatively influence the student’s academic life. Data mining provides the tools necessary to address this educational data in the search for knowledge and patterns that can be obtained. Therefore, this work uses an educational database obtained at the UCI machine learning repository related to students grades in Portuguese and proposes models using extreme learning machine networks, ensemble learning and optimization by particle swarm in order to predict students’ grades. In addition two simulated data sets were also used to verify the consistency of the results obtained through the proposed regression models. After obtaining the error value for each proposed model, hypothesis tests were performed to ascertain the veracity of the results. The results indicate a better performance of the model that combines the ensemble learning, particle swarm optimization and extreme learning machine networks.
Relationship Between Masked Hypertension Measured by Ambulatory Blood Pressure Monitoring and Left Ventricular Global Longitudinal Strain: A Retrospective Study

Purpose Masked hypertension (MHT), as an independent clinical entity, the cardiac dysfunction caused by it can be early detected through left ventricular global longitudinal strain (GLS), yet the quantitative relation between MHT and GLS is still unclear. Therefore, we tried to conduct a community-based retrospective study to define this relationship. Patients and Methods A total of 308 enrolled participants from Dongguan, China, were divided into non-hypertension (NHT) and MHT groups. Baseline characteristics were recorded, and echocardiography and 24-hour ambulatory blood pressure monitoring were performed. Linear regression analysis and receiver-operating characteristic (ROC) curve analysis were used to assess the associations between MHT and GLS in univariate and multivariate models, and the dose–response curve was plotted to demonstrate their relationship. Results The mean age of the NHT and MHT groups was 57 and 60 years, respectively. Signs of left ventricular diastolic function, E/A was reduced and E/e’ was increased in the MHT group while those of the NHT group were nearly normal. The MHT group also showed a significantly lower (“worse”) GLS than NHT (−15.2% vs −19.9%, P < 0.001) while left ventricular ejection fraction (LVEF) did not differ between the groups. Worse GLS was independently and significantly associated with MHT both in univariate (odds ratio [OR]: 1.97, P < 0.001) and stepwise multivariate regression analysis (OR: 1.99, P < 0.001). Comparison of ROC curve results showed that area under curve of GLS was larger than that of E/e’ both in unadjusted (0.8673 vs 0.6831) and adjusted model (0.9178 vs 0.8284). Further analysis showed adjusted nonlinear correlation between MHT and GLS. Conclusion Based on the relationship between MHT and GLS, in clinical practice, GLS measurement could facilitate diagnosis for suspected MHT patients and could define the extent of left ventricular dysfunction for diagnosed MHT patients.
Taxation Ethical Issues: Perspectives of Tax Professionals in Indonesia

This study aims to identify the ranking of taxation ethical issues in terms of frequency of occurrence and importance based on the perspectives of tax professionals. Additionally, this study also examines the ethical climate of tax professionals in their practice. Questionnaires were distributed through a web-based survey and seminar. One hundred and seventy-six useable questionnaires with a 29% respondent rate were analyzed and the results showed that tax avoidance is perceived as the most important and frequent issue in Indonesia. Comparing the top 10 rankings of importance and occurrence issues, there are eight issues that are included in both of the rankings, such as continuing to act, reporting position, tax audit, loophole seeking, tax minimization, technical competence, supervision of audit, and tax avoidance. The results also demonstrated that the examination of tax professionals’ ethical climate through peer reviews revealed that tax professionals have been living in an environment that they considered ethical, even though they believe that many opportunities exist for them to do unethical actions.
Strain-sensing fiber with a core–sheath structure based on carbon black/polyurethane composites for smart textiles

Fiber-shaped sensors have great potential for real-time monitoring of human physiological signals thanks to the merging of electronic and textile technologies. This work reports on the fabrication of a core–sheath structured strain-sensing fiber based on the wet-spinning method. The sensing fiber is composed of a core of non-conducting polyurethane and a conducting sheath of carbon black in a polyurethane matrix. Microscopic observation reveals the irregular shape or scattered appearance of the core as well as the porous structure of the fiber, the diameter of which is in the range 200–500 μm. The electro-mechanical properties and their dependence on carbon black concentration in the sheath and draw ratio between the spinneret and first drafting roller are experimentally investigated. It has been found that the percolation threshold of the fiber is in the range 15–16 wt%. The resistance of the fiber rises stably as the fiber is stretched up to a strain of 120% and increases with the increase of draw ratio between the spinneret and first drafting roller. In the cyclic tensile tests, the resistance of the fiber exhibits good repeatability in subsequent loading–unloading cycles after pre-stretching, despite partial recovery of the resistance in the first few cycles. The integration of the strain-sensing fiber into textiles is demonstrated by the core-spun yarn fabricated based on a modified vortex spinning method. The results of this study indicate the fiber could be a promising candidate for a sensor for smart textiles.
Impacts of land use change on thermodynamic and dynamic changes of precipitation for the Yangtze River Basin, China

Many studies found that land use change (LUC) had great impacts on regional precipitation, due to thermodynamic and dynamic responses. However, the relative contributions of these two factors to changes in precipitation due to LUC are rarely investigated. This study quantifies the relative contributions of thermodynamic and dynamic to the changes of mean and extreme precipitation due to LUC based on simulations of the Weather Research and Forecasting (WRF) model. The Yangtze River Basin (YRB) is used as a case study, as it has experienced great LUC during the past decades. Four land use scenarios (two factual cases and two hypothetical reforestation scenarios) were used to test the sensitivity of precipitation changes to LUC. Changes in mean and extreme precipitation over the YRB for all seasons are attributed to thermodynamic and dynamic changes using 500‐hPa geopotential height and precipitation data derived from the simulations of WRF. The results show that the factual process of LUC results in a decrease in summer mean daily precipitation and precipitation extremes in most areas of the YRB (except for the middle reaches of the YRB), while the hypothetical reforestation contributes to an increase in summer mean and extreme precipitation, and the impacts of reforestation on increasing precipitation are limited. The thermodynamic change contributes to an increase in seasonal mean daily precipitation, extreme precipitation totals and occurrence frequency of precipitation extremes with contributions ranging from 94 to 102%, which is the main contributor of changes in precipitation due to LUC. The dynamic change only makes a small contribution to the change of precipitation. Because the contributions of thermodynamic can be offset by dynamic changes to changes in precipitation, it could result in minimal changes in precipitation and occurrences of precipitation extremes.
Direct Dimethyl Carbonates Synthesis over CeO2 and Evaluation of Catalyst Morphology Role in Catalytic Performance

In recent years, direct synthesis of dimethyl carbonate (DMC) from carbon dioxide (CO2) has received considerable attention due to green and sustainable technology. Here, we report a production of DMC from major greenhouse gases and CO2 using various morphologies of cerium oxide (CeO2). Time-dependent synthesis of CeO2, with controlled morphology having various shapes including sphere, nanorods and spindle shape, along with its formation mechanism is proposed. The experimental results indicate the morphology of CeO2 was mostly dependent on the reaction time where crystal growth occurred through Ostwald ripening. The morphology, size and shape of CeO2 were observed using transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM). The crystallographic analysis using X-ray diffraction (XRD) shows cubic fluorite phase of CeO2 with crystallite size ~72.0 nm using the Debye–Scherrer equation. The nitrogen adsorption desorption technique suggested the formation of the highly mesoporous framework of CeO2 and the excellent surface area around 104.5 m2/g obtained for CeO2 spindles by Brunauer–Emmett–Teller (BET) method. The DMC synthesis reactions were studied over CeO2 catalyst with different morphologies. The results of catalytic reactions specify that the morphology of catalyst plays an important role in their catalytic performances, where spindle shape CeO2 was the most active catalyst producing of up to13.04 mmol of DMC. Furthermore, various dehydrating agents were used to improve the DMC production at optimized reaction parameters. The overall results reveal that the higher surface area and spindle shape of CeO2 makes it a useful, reusable catalyst for one-pot DMC synthesis.
Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to the
 Deepwater Horizon
 Oil Spill and Hurricane Katrina

Determining how to allocate resources in order to prevent and prepare for disruptions is a challenging task for homeland security officials. Disruptions are uncertain events with uncertain consequences. Resources that could be used to prepare for unlikely disruptions may be better used for other priorities. This chapter presents an optimization model to help homeland security officials determine how to allocate resources to prevent and prepare for multiple disruptions and how to allocate resources to respond to and recover from a disruption. In the resource allocation model, prevention reduces the probability of a disruption, and preparation and response both reduce the consequences of a disruption. The model is applied to the US Gulf Coast region and considers a Deepwater Horizon‐type oil spill and a hurricane similar to Hurricane Katrina.
The Experience of Situations Before and During a COVID-19 Shelter-at-Home Period

Undergraduate participants described their experience of an ordinary situation before (N = 544) and during (N = 123) a COVID-19 shelter-at-home period using the Riverside Situational Q-sort (RSQ). They also rated the experience’s positivity and completed a Big Five Personality Inventory. RSQ items placed higher before the sheltering period included “new relationships could develop,” “femininity can be expressed,” and “talking is expected. ” Items placed higher during the sheltering period included “family is important” and “people are disagreeing. ” Average positivity ratings did not differ, but relatively positive ratings before sheltering correlated with Extraversion, Agreeableness, Conscientiousness, and Openness; negative ratings correlated with Negative Emotionality. During sheltering, positive ratings again correlated with Agreeableness; negative ratings correlated with Conscientiousness. Sheltering conditions accentuated the importance of family, could lead to conflict, and limited opportunities for social interaction. Agreeableness was associated with positivity at both time periods; other traits varied in relevance as the situation changed.
Text-Linguistic Analysis in Forensic Authorship Attribution

Authorship analysis of anonymous texts is one of the more frequently required tasks in a forensic setting. Its main purpose is either to gain information on the author’s linguistic and social background to support an investigation or to match an anonymous text to a suspect’s previous writings. One approach to authorship attribution is pragmatic stylistic analysis, which is grounded in text-linguistic research, cf. Brinker (2002), Sandig (2006), Puschel (2009), and Brinker et al. (2018), and holds a broad and holistic view on style. In its analyses, it focuses on the functional and pragmatic aspects of style as part of a communicative strategy. A central element, especially in Brinker’s approach, is the thematic text pattern. How individuals argue, how they arrange textual patterns and how they express their demands most certainly reveal aspects of their individuality (Brinker, 2002; Brinker et al., 2018), suggesting that these cannot be easily suppressed or disguised. The paper applies Brinker’s approach to three very short anonymous extortion letters illustrating how text-linguistic analysis can contribute to authorship attribution. Firstly, the text structure of the letters will be analyzed and secondly, the relations between text pattern and stylistic features will be examined in detail, and the relevance of the text patterns will be discussed with reference to the author’s idiolect. The paper aims to point out the possibilities of a more in-depth textual analysis beyond the analysis of surface structures, especially in cases of very small data sets. Cite as: Fobbe , JLL 9 (2020), 93–114, DOI: 10.14762/jll.2020. 093
Improving children’s oral vocabulary with a dynamic intervention programme

ABSTRACT Objective: Two agendas guided this study. The first is to report on a dynamic intervention programme that utilizes key developmental domains and strategies, such as emotional expression opportunities, motor activity, learning in groups, playful learning, and shared reading. The second is to explore the possibility that dynamic intervention can improve vocabulary growth in a low socioeconomic status pre-K cohort and to examine whether the intervention can mitigate the impact of negative factors that impede vocabulary growth. Method: To explore the programme’s impact, 62 children were recruited (28 girls and 34 boys) from seven different kindergartens in northern Israel. Their vocabulary skills were gauged pre- and post-intervention, and a MANOVA test was implemented to investigate the programme’s contribution and its potential to mediate impeding factors. Results: The results indicate that the intervention improved the children’s vocabulary scope in all domains and moderated the negative impact of lower socioeconomic status, low print exposure, and sibship size. Conclusion: We conclude by discussing how the dynamic intervention programme can address children’s needs and what learning strategies might further enhance their vocabulary development.