Miracle-dz/UniFAR-FTUs huggingface dataset:
Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape

Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (hereafter ‘drones’) are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and are facilitating the interpretation of coarse-grained remotely sensed data. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak growing season drone surveys with an RGB camera and a multispectral multi-camera array. We derived photogrammetric reconstructions of canopy height and normalised difference vegetation index (NDVI) maps along with in situ point-intercept measurements and aboveground vascular biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point-intercept methods compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point-intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point-intercept (R2 = 0.92) and drone-based methods (R2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Vascular plant biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.
Generalized Nonlinear Mixed-Effects Individual Tree Crown Ratio Models for Norway Spruce and European Beech

Tree crowns are commonly measured to understand tree growth and stand dynamics. Crown ratio (CR—crown depth-to-total height ratio) is significantly affected by a number of tree- and stand-level characteristics and other factors as well. Generalized mixed-effects CR models were developed using a large dataset (measurements from 14,669 trees of Norway spruce (Picea abies (L.) Karst. ) and European beech (Fagus sylvatica (L.)) acquired from permanent research plots in various parts of the Czech Republic. Among several tree- and stand-level variables evaluated, diameter at breast height, height to crown base, dominant height, basal area of trees larger in diameter than a focal tree, relative spacing index, and variables describing the effects of species mixture and canopy height differentiation significantly contributed to CR variation. We included sample-plot-level variations caused by randomness in the data and other stochastic factors into the CR models using the mixed-effects modeling approach. The logistic function, which predicts the values between 0 and 1, was chosen to develop the generalized CR mixed-effects model. A large proportion of the CR variation (R2adj ≈ 0.63 (Norway spruce); 0.72 (European beech)) was described by generalized mixed-effects model without significant residual trends. Testing the CR model against a part of the model fitting dataset confirmed its high prediction precision. Our CR model can be useful for growth simulation using inventory databases that lack crown measures. Other potential implications of our CR models in forest management are mentioned in the article.
Performance enhancement of safety message communication via designing dynamic power control mechanisms in vehicular ad hoc networks

In vehicular ad hoc networks (VANETs), transmission power is a key factor in several performance measures, such as throughput, delay, and energy efficiency. Vehicle mobility in VANETs creates a highly dynamic topology that leads to a nontrivial task of maintaining connectivity due to rapid topology changes. Therefore, using fixed transmission power adversely affects VANET connectivity and leads to network performance degradation. New cross‐layer power control algorithms called (BL‐TPC 802.11MAC and DTPC 802.11 MAC) are designed, modeled, and evaluated in this paper. The designed algorithms can be deployed in smart cities, highway, and urban city roads. The designed algorithms improve VANET performance by adapting transmission power dynamically to improve network connectivity. The power adaptation is based on inspecting some network parameters, such as node density, network load, and media access control (MAC) queue state, and then deciding on the required power level. Obtained results indicate that the designed power control algorithm outperforms the traditional 802.11p MAC considering the number of received safety messages, network connectivity, network throughput, and the number of dropped safety messages. Consequently, improving network performance means enhancing the safety of vehicle drivers in smart cities, highway, and urban city.
Task-based visual analytics for interactive multiobjective optimization

Abstract We study how visual interaction techniques considered in visual analytics can be utilized when implementing interactive multiobjective optimization methods, where a decision maker iteratively participates in the solution process. We want to benefit from previous research and avoid re-inventing ideas. Our aim is to widen awareness and increase the applicability of interactive methods for solving real-world problems. As a concrete approach, we introduce seven high-level tasks that are relevant for interactive methods. These high-level tasks are based on low-level tasks proposed in the visual analytics literature. In addition, we give an example on how the high-level tasks can be implemented and demonstrate this in the context of a real-world multiobjective optimization problem related to wastewater treatment plant operation. Finally, we make recommendations for implementations of interactive methods. We conclude that task-based visual analytics can help in implementing interaction between human decision makers and interactive multiobjective optimization methods.
DNA-based species delimitation reveals cryptic and incipient species in synchronous flashing fireflies (Coleoptera: Lampyridae) of Southeast Asia

Synchronous flashing fireflies of the genus Pteroptyx are ubiquitous throughout Southeast Asia, yet fundamental knowledge about their biodiversity is lacking. Recent studies have revealed notable population-level phylogeographical structure within the Pteroptyx tener and P. bearni groups in Malaysia, suggesting that cryptic species may exist. Additionally, morphological and genetic similarities between P. balingiana and P. malaccae have raised questions about the former’s validity as a distinct species. We collected samples from previously unsampled populations and assembled the most comprehensive genetic dataset for Pteroptyx to date, to characterize species boundaries within the P. tener, P. bearni and P. malaccae groups. Using a suite of species delimitation analyses, we show that P. tener along the west coast of Peninsular Malaysia (PM) is distinct from populations from the east coast and Borneo despite the absence of morphological differentiation. However, analyses could not conclusively differentiate P. bearni from Borneo and eastern PM, nor identify P. balingiana and P. malaccae as distinct species, indicating that these populations may be conspecific or represent incipient species. This study underlines the need to increase geographical, taxonomic and genetic sampling of Southeast Asian fireflies to provide a better understanding of their biodiversity.
Salespeople Performance Evaluation with Predictive Analytics in B2B

Performance Evaluation is a process that occurs multiple times per year on a company. During this process, the manager and the salesperson evaluate how the salesperson performed on numerous Key Performance Indicators (KPIs). To prepare the evaluation meeting, managers have to gather data from Customer Relationship Management System, Financial Systems, Excel files, among others, leading to a very time-consuming process. The result of the Performance Evaluation is a classification followed by actions to improve the performance where it is needed. Nowadays, through predictive analytics technologies, it is possible to make classifications based on data. In this work, the authors applied a Naive Bayes model over a dataset that is composed by sales from 594 salespeople along 3 years from a global freight forwarding company, to classify salespeople into pre-defined categories provided by the business. The classification is done in 3 classes, being: Not Performing, Good, and Outstanding. The classification was achieved based on KPI’s like growth volume and percentage, sales variability along the year, opportunities created, customer base line, target achievement among others. The authors assessed the performance of the model with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an accuracy of 92.50% for the whole model.
An Initial Assessment of Lead Artefacts Used for Hull Repair and Maintenance on North Carolina Shipwreck 31CR314, Queen Anne's Revenge (1718)

Abstract Excavation of North Carolina shipwreck 31CR314, Queen Anne's Revenge, has yielded an assemblage of sheet lead artefacts that reflect hull maintenance and repair practices on the slave ship, turned pirate flagship. This article will outline the different types of lead artefacts recovered from QAR associated with this function. The findings are discussed in relation to prevailing trends in hull sheathing from antiquity through to the mid 18th century. Last, the known history of hull repair on QAR is examined to place these objects in historical context.
Experimental investigation of performance and emission characteristics of diesel engine with use of rape seed oil as biodiesel

In this paper, emission and performance characteristics of diesel fuel-engine was investigated using blending of diesel with bio diesel under steady state. Rape-seed oil was used as biodiesel and blended in diesel with different proportions of 10%, 20% and 30% by volume. The engine used in experiment was single cylinder 4-stroke diesel fuel-engine having water cooling system. Result shows that BTE decreases with more addition of bio-diesel in % fraction. For 10% blend, BTE of engine is almost same compared to diesel. The emission characteristics were remarkably improved by increasing volume fraction of rape seed oil except NOx emission. NOx emission was found maximum for 30% diesel-bio diesel blend.
DEEPAUTOENCF: A DENOISING AUTOENCODER FOR RECOMMENDER SYSTEMS

Recommender Systems are software techniques which can be used to filter out data from the volumes of data available online and provide recommendations to users in their area of interest. These techniques use information related to users and items in addition to the ratings given by users to various items or providing recommendations. In the last two decades, deep learning techniques have shown promising results in various areas of computer vision, video recognition, natural language processing etc. These techniques have been used for recommender systems in recent years and have shown improvement in performance. In this paper we propose a model, DeepAutoEnCF, that uses Denoising Autoencoder for predicting user ratings. It uses dropout for regularizing the model and adding noise to input for prediction of ratings. The model uses side information along with unique additional information for improving the performance.
Educators’ Perceptions of Performance-Based Approaches in Teaching Difference With Youth

Recent research into critical pedagogy supports the implementation of performance-based practices into the classroom. This qualitative research explored the pragmatic ways in which youth, ages 8-17, are taught in Canada’s, specifically Ontario’s, education system on topics of difference and power. Through semi-structured interviews, four elementary and high school educators described their experiences using performance-based teaching in the classroom. A thematic analysis revealed that teachers found including such practices empowered students and influenced their understanding of their own identities as well as the systemic oppression of marginalized groups.
Does Mandatory Disclosure Matter? The Case of Nonprofit Fundraising

Do donors seek out potentially adverse information about organizations making fundraising appeals? Do they react when it is readily available? Do they draw negative inferences when critical information is not available? To answer these questions, we consider previously unexamined large-scale natural experiments involving U.S. charitable organizations—tax-exempt organizations that file Internal Revenue Service (IRS) Form 990. Using standard difference-in-differences designs, we find that donors penalize organizations with high fundraising costs when there is mandatory disclosure or involuntary disclosure by a third-party reporter. Organizations with lower fundraising costs fundraise more successfully in the presence of these disclosures. The contrast with donors’ behavior when such information is not available suggests that donors do not draw correct inferences when potentially consequential information is not disclosed. Disclose-on-request requirements, in contrast, apparently do not have any significant impact on donors’ or organizations’ behavior. We then sketch implications for the regulation of donations to charities and their modern cousins, such as crowdfunding and social enterprise organizations.
Improving a comprehensive remote sensing drought index (CRSDI) in the Western part of Iran

Abstract Numerous remote sensing indices have been introduced to investigate the drought. Hence, the purpose of this study was to develop a Comprehensive Remote Sensing Drought Index (CRSDI) using the strengths of the previous Remote Sensing Drought Indices (RSDIs). Eleven mostly used RSDIs were used to develop CRSDI. Aras and Karkheh basins were studied as representative of Western Iran. Vegetation indices and surface temperature were derived from the products of the Moderate Resolution Imaging Spectroradiometer (MODIS). Gravity Recovery and Climate Experiment (GRACE (and Global Land Data Assimilation System (GLDAS) data were used to extract groundwater information. The performances of RSDIs were evaluated using the Standardized Precipitation Index (SPI) acquired from 13 synoptic stations. The correlation coefficients between CRSDI against six-month SPI and nine-month SPI were obtained as 0.66 and 0.56, respectively. Then, the results proved the proper efficiency of the CRSDI to investigate the drought in the study area.
Interaction between Mycobacterium tuberculosis and Human Host: Role of Cytokines in Pathogenesis and Treatment Monitoring

Tuberculosis is one of the most prevalent infections of human beings. According to WHO Global tuberculosis report 2016, there were 10.4 million new incidents of TB cases world-wide, and 580,000 new cases of multidrug resistant (MDR) tuberculosis. Monitoring the effectiveness of tuberculosis treatment and timely diagnosis of latent tuberculosis is an important problem for immunological research. Interaction of M. tuberculosis and the human immune system begins with phagocytosis of mycobacteria by macrophages and activate the immune response through the cytokines and chemokines release. The balance of proinflammatory and immunoregulatory cytokines and chemokines production may reflect the level of host-parasite interaction (e.g., elimination or persistence of the microbe). The review presents current clinical trends in studies on proinflammatory (IL-12, IL-1, INF-II, TNF-α) and immunoregulatory (IL-10 and TGF-β) cytokines, as well as matrix metalloproteases and hemoxygenase 1 to characterize the success of antituberculous che -motherapy. Monitoring the effectiveness of tuberculosis treatment will require the use new combinations of cytokines, chemokines, and nonspecific inflammatory factors which com binations have not yet been determined. The most promising area is studying of immuno - regulatory cytokines, (e.g., IL-10, TGF-β), cell migration factors (e.g., IP-10/CXCL-10, MIG/ CXCL/9), and markers of nonspecific inflammation (e.g., HO-1, SAA and MMP-1,3,9). The scientific studies carried out to date show a significant increase in the effectiveness of IGRAs-test in combination with the determination of chemokine CXCL/IP-10 for the diag nosis of latent forms of tuberculosis. Monitoring the effectiveness of treatment of tubercu losis will require the use of other combinations of cytokines, chemokines, and nonspecific inflammatory factors. This will depend on the sensitivity and resistance (MDR-TB) of myco bacteria to antituberculosis drugs and the nature of the course of tuberculous inflamma tion (positive, torpid or progressive dynamics of the disease) associated with genetically determined parasite-host interactions. The optimal combination of such factors has not yet been determined, but probable search vectors indicate on the definition of dynamics immu noregulatory cytokines (e.g., IL-10, TGF-β), chemokines characterizing cell migration (e.g., IP-10/CXCL-10, MIG/CXCL/9) and factors nonspecific inflammation (e.g., HO-1, SAA and MMP-1,3,9). Comparability of the results of these studies will depend on the proximity of the methodological platforms.
Physiological and Molecular Changes across the Lifespan Following Developmental Chemical Exposure

Early life environment can impact disease risk later in life, a concept known as the developmental origins of health and disease (DOHaD). Furthermore, the effects may persist across several generations of offspring. The underlying molecular dysregulation driving these phenotypes, however, remain unknown. Using the ubiquitous endocrine disrupting chemical (EDC) bisphenol A (BPA) as a model estrogen, we investigated the physiological and molecular consequences of chronic, low-dose developmental BPA exposure across the lifespan, using a mouse model system. To assess the transmission of phenotypes across generations, we examined physiological endpoints across two generations of offspring in each study. In Chapter 2, we report a depressive-like behavioral phenotype in BPA-exposed offspring that is exclusive to males in the first filial (F1) generation. Molecular assessments in the adult hippocampus using RNA-sequencing and HPLC revealed sexspecific reductions in the neurotransmitter serotonin, a critical regulator of mood. Additionally, levels of dehydroepiandrosterone, a sex steroid intermediate, were reduced in serum and hippocampal tissue, which could also explain the observed behavioral changes. In Chapter 3, we report impaired metabolic health in F1 and F2 generation adult male offspring that may be partially due to increased expression of the imprinted insulin-like growth factor 2 (Igf2) gene. Gestational glucose intolerance could also be contributing to the phenotype in F1, but not F2, generation offspring. Finally, in Chapter 4, we describe our early findings on BPA exposure-associated skeletal changes. Developmental BPA exposure is associated with thinner and weaker bones in F1 male mice, while F2 males exhibit an adaptive response. Whether the sex-specific phenotypes reported in these studies manifest at a younger age, prior to puberty, remains an ongoing area of study. Taken together, work in this dissertation demonstrates sex-, doseand generation-specific effects associated with early life BPA exposure. Understanding the exposure-associated effects and mechanism(s) of BPA and other EDC actions is critical for assessing chemical safety. Identifying dysregulated pathways may also allow for targeted intervention strategies to reduce or prevent EDC exposure-associated effects. Ultimately, findings from these studies and others utilizing models representative of human exposure may aid in future regulatory decision-making. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Cell & Molecular Biology First Advisor Marisa S. Bartolomei
Dynamic evolution of sentiments in Never Let Me Go: Insights from multifractal theory and its implications for literary analysis

The moods, feelings, and attitudes represented in a novel will resonate in the reader by activating similar sentiments. It is generally accepted that sentiment analysis can capture aspects of such moods, feelings, and attitudes and can be used to summarize a novel’s plot in a story arc. With the availability of a number of algorithms to automatically extract sentiment-based story arcs, new approaches for their utilization becomes pertinent. We propose to use nonlinear adaptive filtering and fractal analysis in order to analyze the narrative coherence and dynamic evolution of a novel. Using Never Let Me Go by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an illustrative example, we show that: (1) nonlinear adaptive filtering can extract a story arc that reflects the tragic trend of the novel; (2) the story arc displays persistent dynamics as measured by the Hurst exponent at short and medium timescales; (3) the plot’s dynamic evolution is reflected in the time-varying Hurst exponent. We argue that these findings are indicative of the potential that multifractal theory has for computational narratology and large-scale literary analysis. Specifically that the global Hurst exponent of a story arc is an index of narrative coherence that can identify bland, incoherent, and coherent narratives on a continuous scale. And, further, that the local time-varying Hurst exponent captures variation of a novel’s plot such that the extrema have specific narratological interpretations.
Teachers’ Self-Efficacy Based on Symptoms of Attention Deficit Hyperactivity Disorder in Primary School Pupils

Both the quality and quantity of teachers’ experiences of self-competence in dealing with pupils with symptoms of Attention Deficit Hyperactivity Disorder have been the subject of a great deal of research. The permanent monitoring of the levels at which teachers accomplish such competencies, which have a positive effect on the improvement of teaching, is one of the tasks of educational science. The present paper determines how teachers self-assess their efficacy in teaching pupils with behavioural difficulties based on the pupils’ symptoms of attention disorder and hyperactivity. Primary school teachers from 12 counties of the Republic of Croatia participated in the research. The teachers provided data for a total of 1,383 pupils in whom they subjectively detected behavioural difficulties. The research reveals that the characteristics of the pupil best predict the teacher’s self-efficacy. More time spent in the classroom with the teacher, better academic achievement, and a lower school grade indicate higher self-efficacy in teachers.