Miracle-dz/UniFAR-FTUs huggingface dataset:
Decline of Leach’s Storm Petrels Hydrobates leucorhous at the largest colonies in the northeast Atlantic

Leach’s Storm Petrel Hydrobates leucorhous has undergone substantial population declines at North Atlantic colonies over recent decades, but censusing the species is challenging because it nests in burrows and is only active at colonies at night. Acoustic playback surveys allow birds present in nest sites to be detected when they respond to recordings of vocalisations. However, not all birds respond to playback on every occasion, response rate is likely to decline with increasing distance between the bird and the playback location, and the observer may not detect all responses. As a result, various analysis methods have been developed to measure and correct for these imperfect response and detection probabilities. We applied two classes of methods (calibration plot and hierarchical distance sampling) to acoustic survey data from the two largest colonies of breeding Leach’s Storm Petrels in the northeast Atlantic: the St Kilda archipelago off the coast of northwest Scotland, and the island of Elliðaey in the Vestmannaeyjar archipelago off the southwest of Iceland. Our results indicate an overall decline of 68% for the St Kilda archipelago between 2000 and 2019, with a current best estimate of ~8,900 (95% CI: 7,800–10,100) pairs. The population on Elliðaey appears to have declined by 40 –49% between 1991 and 2018, with a current best estimate of ~5,400 (95% CI: 4,300–6,700) pairs. We also discuss the relative efficiency and precision of the two survey methods.
Food availability aﬀects parental anti-predator behaviour in red kites

Parental investment theory proposes two non-mutually exclusive hypotheses to explain variation in anti-predator behaviour in relation to the age of oﬀspring: the “reproductive value of oﬀspring” hypothesis and the “harm to oﬀspring” hypothesis. The relative importance of the two factors underlying the hypotheses, reproductive value and harm, may change depending on environmental conditions such as food availability. To test the relative importance of the two hypotheses under diﬀerent food conditions, we conducted a supplementary feeding experiment in red kite (Milvus milvus) breeding pairs and used a live eagle owl (Bubo bubo) as decoy nest predator to trigger anti-predator behaviour. We used time-to-capture in mist nets mounted next to the decoy predator as proxy for mobbing intensity. Under natural food conditions we found a nearly constant mobbing intensity throughout the entire nestling period. However, under food-enhanced conditions mobbing intensity was reduced in parents with young nestlings and increased in parents with old nestlings. These results suggest greater importance of the “reproductive value of oﬀspring” hypothesis in situations of favourable food availability. Moreover, mobbing intensity depended on brood size and weather conditions. The results suggest that parental anti-predator investment increases with the reproductive value of the brood under favourable breeding conditions, but that this pattern is adjusted to the current context, including the vulnerability of the brood and environmental conditions.
Gray-box combinatorial interaction testing

The enourmous size of configuration spaces in highly configurable softwares pose challenges to testing. Typically exhaustive testing is neither an option nor a way. Combinatorial interaction techiques are a systematic way to test such enourmous configuration spaces by a systematic way of sampling the space, employed through covering arrays. A t-way covering array is a sampled subset of configurations which contains all t-way option setting combinations. Testing through t-way covering arrays is proven to be highly e ective at revealing failures caused by interaction of t or fewer options. Although, traditional covering arrays are e ective however, we’ve observed that they su er in the presence of complex interactions among configuration options, referred as tangled options. A tangled configuration option is described as either a configuration option with complex structure and/or nested in hierarchy of configuration options. In this thesis, we conjecture the e ectiveness of CIT in the presence of tangled options can greatly be improved, by analyzing the system’s source code. The analysis of source code reveals the interaction of configuration options with each other, this information can be used to determine which additional option setting combinations and the conditions under which these combinations must be tested. Gray-box testing methods rely on partial structural information of the system during testing. We’ve statically analyzed the source code of subject applications to extract the structure and hierachy of configuration options. Each configuration option has been structurally tested according to a test criterion against a t-way covering array and subsequently their t-way interactions. The criterion revealed the missing coverage of options which were employed to drive the additional testcase generation phase to acheive complete coverage. We present a number of novel CIT coverage criteria for t-wise interaction testing of configuration options. In this thesis, we’ve conducted a series of large scale experiments on 18 di erent real-world highly configurable software applications from di erent application domains to evaluate the proposed approach. We’ve observed that traditional t-way CAs can provide above 80% coverage for configuration options testing. However, they significantly su er to provide interaction coverage under high t and tangling e ects where coverage is dropped to less than 50%. Our work address these issues and propose a technique to acheive complete coverage.
Nonlinear Processes in Geophysics RE : “ An approach for projecting the timing of abrupt winter Arctic sea ice loss

Please find below our response to each reviewer’s feedback, with references to our updated manuscript. We found both reviewers’ comments extremely helpful and have made significant revisions to our manuscript that we feel have clarified the scope and contribution of our project and have strengthened our analysis. In particular, we focused on: 1) better situating our analysis in the dynamical systems literature, clearly establishing where we draw on previous work and where we make new contributions, 2) providing more in-depth discussion on the applicability of our novel method to GCMs and other climate problems, and 3) clarifying the use and definitions of terms such as “hysteresis” and “tipping point” and eliminating the use of “transient hysteresis” (following the suggestion of Reviewer 1), such that the nonlinear processes discussed in this work are more precisely described. We have also further emphasized the main novel contribution of this work: a method that can estimate the existence and location of tipping points without running a model to steady-state.
Stochastic algorithms for solving linear and nonlinear inverse ill-posed problems for particle size retrieving and x-ray diffraction analysis of epitaxial films

Abstract We suggest stochastic simulation techniques for solving two classes of linear and nonlinear inverse and ill-posed problems: (1) recovering the particle nanosize distribution from diffusion battery measurements, and (2) retrieving the step structure of the epitaxial films from the x-ray diffraction analysis. To solve these problems we develop three stochastic based methods: (1) the random projection method, a stochastic version of the Kaczmarz method, (2) a randomized SVD method, and (3) stochastic genetic algorithm. Results of comparative simulations of the three methods are also presented.
Mixed-methods study of development and design needs for CommitFit, an adolescent mHealth App

Background Adolescent obesity remains a significant public health issue within the United States. Mobile application technology growth and popularity offer new opportunities for research and health improvement. The development of a consolidated mobile health application (mHealth app) for adolescents on these platforms has the potential to improve health outcomes. Thus, this study describes the co-development process working with adolescent users. The aims are as follows: (I) to explore the visual design and functional requirements when developing the CommitFit mHealth app, (II) to uncover the gamification techniques that incentivize adolescents to set and achieve healthy lifestyle goals, and (III) to identify adolescent expectations when using the CommitFit mHealth application. Methods In this mixed method study, we used semi-structured interviews/task analysis and surveys of adolescents (aged 13 to 15 years) to understand their user requirements and design preferences during the development of the CommitFit mHealth app. Interviews were conducted online, via Zoom. The survey included the user design industry-standard System Usability Scale (SUS) paired with a supplemental questionnaire on the specific features and functionalities of the CommitFit mHealth app. Participants were recruited from the electronic health record from the University of Missouri Healthcare system. Results Ten adolescents, aged 13 to 15 years (average of 13.6 years), were interviewed and surveyed to explore adolescent preferences with visual app design and functionality. Our inductive thematic analysis found that adolescents preferred colorful, user-friendly interfaces paired with gamification in the CommitFit mHealth app. Our analysis of SUS survey data validated our user-centered and human-system design and adolescents confirmed their design, feature, and functionality preferences. Overall, adolescent users were able to confirm their preference to have educational resources, goal recommendations, leaderboard, points, reminders, and an avatar in the app. Conclusions Adolescent feedback is crucial in the successful development of our adolescent-targeted mHealth app, CommitFit. Adolescents preferred vibrant colors, easy-to-use interface, gamification, customizable and personalized, and mature graphics. Adolescents were especially motivated by gamification techniques to maintain their interest in the application and their health behavior goals. Additional research is now needed to explore the clinical effectiveness of the CommitFit mHealth app, as a health and lifestyle intervention.
Spectrum measurement with the Telescope Array Low Energy Extension (TALE) fluorescence detector

The Telescope Array (TA) experiment is the largest Ultra High Energy cosmic ray observatory in the northern hemisphere and is designed to be sensitive to cosmic ray air showers above 1018eV. Despite the substantial measurements made by TA and AUGER (the largest cosmic ray observatory in the southern hemisphere), there remains uncertainty about whether the highest energy cosmic rays are galactic or extragalactic in origin. Locating features in the cosmic ray energy spectrum below 1018eV that indicate a transition from galactic to extragalactic sources would clarify the interpretation of measurements made at the highest energies. The Telescope Array Low Energy Extension (TALE) is designed to extend the energy threshold of the TA observatory down to 1016.5eV in order to make such measurements. This dissertation details the construction, calibration, and operation of the TALE fluorescence detector. A measurement of the flux of cosmic rays in the energy range of 1016.5 1018.5eV is made using the monocular data set taken between September 2013 and January 2014. The TALE fluorescence detector observes evidence for a softening of the cosmic spectrum at 1017.25±0.5eV. The evidence of a change in the spectrum motivates continued study of 1016.5 1018.5eV cosmic rays.
Efficient Remote Entanglement Distribution in Quantum Networks: A Segment-Based Method

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.
Data-driven estimation for Aalen's additive risk model

The proportional hazards model developed by Cox (1972) is by far the most widely used method for regression analysis of censored survival data. Application of the Cox model to more general event history data has become possible through extensions using counting process theory (e.g., Andersen and Borgan (1985), Therneau and Grambsch (2000)). With its development based entirely on counting processes, Aalen’s additive risk model offers a flexible, nonparametric alternative. Ordinary least squares, weighted least squares and ridge regression have been proposed in the literature as estimation schemes for Aalen’s model (Aalen (1989), Huffer and McKeague (1991), Aalen et al. (2004)). This thesis develops data-driven parameter selection criteria for the weighted least squares and ridge estimators. Using simulated survival data, these new methods are evaluated against existing approaches. A survey of the literature on the additive risk model and a demonstration of its application to real data sets are also provided.
Large-Resolution Difference Heterogeneous SAR Image Sea Ice Drift Tracking Using a Smooth Edge-Guide Super-Resolution Residual Network

Heterogeneous synthetic aperture radar (SAR) data contain more information, so the use of heterogeneous SAR images can potentially improve the performance of remote sensing applications. Feature tracking is crucial for using heterogeneous SAR data. However, feature tracking is a challenge using heterogeneous SAR images to harmonize high-resolution (HR) data with coarser data. In this article, we propose a smooth edge-guide super-resolution (SEGSR) recurrent residual learning network for uniform resolution of heterogeneous SAR image such that their features have more consistent representation. Our proposed framework contains a super-resolution (SR) network that aims to translate the low-resolution (LR) images into HR ones to reduce their feature differences. The generated HR final image and the HR raw image can be considered homogeneous for sea ice drift tracking. Through several examples, we demonstrate the effectiveness of the method in the feature matching of images with large-resolution difference images from different SAR sensors.
GreenAI – An NLP approach to ESG financing

Environmental, Social, and Governance (ESG) factors are critical for investors and financing institutions like the Brazilian Development Bank (BNDES). Such institutions are currently working on setting up a framework to assess companies' ESG factors in their financing evaluation. In this study, we identify an opportunity to use Natural Language Processing (NLP) to improve the framework. This opportunity stems from the fact that the key documents for ESG analysis, such as the company's activity report (RAA), Environmental Impact Study (EIA), and Environmental Impact Report (RIMA), undergo manual screening and decomposition whilst being analyzed by specialists. By incorporating NLP, we aim to automate the classification of text passages from these reports and enhance the efficiency of the analysis process.
Effect of Nutrient Management and Weeds on Incidence of Fungal Diseases in Rice

High-input, modern agriculture uses large amounts of energy, water, fertilizers, and pesticides to produce high crop yields. One of the major bottlenecks of the modern agriculture in the tropics is substantial yield losses due to fungal diseases including rice blast, leaf spots and leaf scald. The aim of this study was to compare the incidence of fungal diseases in judicious nutrient management systems, including organic, integrated, and conventional, under different weed categories during dry season (May to September 2020) and wet season (November 2020 to March 2021). Rice disease incidence were collected for both seasons from 48-84 days after sowing. Additionally, disease incidences on grasses and sedges weeds were also calculated. Brown spot, narrow brown leaf spot, leaf scald, and rice blast incidences were substantial in wet season, while the disease incidences during dry season in 2020 were negligible. The disease incidences were significantly higher (P<0.05) in organic and conventional input systems compared to the integrated input system. Disease incidences of brown spot and leaf scald were found in the dry season. Higher disease incidences were recorded in the wet season than in the dry season. The incidences of the brown spot were higher on sedges than in grasses and vice versa were observed for narrow brown leaf spot disease. Leaf scald incidences were positively correlated with the significant nitrogen status of the rice crop. Disease incidence was low in integrated input system compared to conventional and organic input systems, while weeds were reported as alternative hosts. It can be concluded that the integrated nutrient management with recommended dosage of nitrogen application with proper weed management can lead to low disease incidents, hence is ecologically more sustainable.
Implementation of a protocol for assembling DNA in a Teflon tube

Droplet based microfluidics continues to grow as a platform for chemical and biological reactions using small quantities of fluids, however complex protocols are rarely possible in existing devices. This paper implements a new approach to merging of drops, combined with magnetic bead manipulation, for the creation of ligated double-stranded DNA molecule using “Gibson assembly” chemistry. DNA assembly is initially accomplished through the merging, and mixing, of five drops followed by a thermal cycle. Then, integrating this drop merging method with magnetic beads enable the implementation of amore complete protocol consisting of nine wash steps,merging of four drop, transport of selective reagents between twelve drops using magnetic particles, followed by a thermal cycle and finally the deposition of a purified drop into an Eppendorf for downstream analysis. Gel electrophoresis is used to confirm successful DNA assembly.
Improved prediction of drug-induced liver injury literature using natural language processing and machine learning methods

Drug-induced liver injury (DILI) is an adverse hepatic drug reaction that can potentially lead to life-threatening liver failure. Previously published work in the scientific literature on DILI has provided valuable insights for the understanding of hepatotoxicity as well as drug development. However, the manual search of scientific literature in PubMed is laborious and time-consuming. Natural language processing (NLP) techniques along with artificial intelligence/machine learning approaches may allow for automatic processing in identifying DILI-related literature, but useful methods are yet to be demonstrated. To address this issue, we have developed an integrated NLP/machine learning classification model to identify DILI-related literature using only paper titles and abstracts. For prediction modeling, we used 14,203 publications provided by the Critical Assessment of Massive Data Analysis (CAMDA) challenge, employing word vectorization techniques in NLP in conjunction with machine learning methods. Classification modeling was performed using 2/3 of the data for training and the remainder for test in internal validation. The best performance was achieved using a linear support vector machine (SVM) model on the combined vectors derived from term frequency-inverse document frequency (TF-IDF) and Word2Vec, resulting in an accuracy of 95.0% and an F1-score of 95.0%. The final SVM model constructed from all 14,203 publications was tested on independent datasets, resulting in accuracies of 92.5%, 96.3%, and 98.3%, and F1-scores of 93.5%, 86.1%, and 75.6% for three test sets (T1-T3). Furthermore, the SVM model was tested on four external validation sets (V1-V4), resulting in accuracies of 92.0%, 96.2%, 98.3%, and 93.1%, and F1-scores of 92.4%, 82.9%, 75.0%, and 93.3%.
Unlocking the Potential of Stroke Blood Biomarkers: Early Diagnosis, Ischemic vs. Haemorrhagic Differentiation and Haemorrhagic Transformation Risk: A Comprehensive Review

Stroke, a complex and heterogeneous disease, is a leading cause of morbidity and mortality worldwide. The timely therapeutic intervention significantly impacts patient outcomes, but early stroke diagnosis is challenging due to the lack of specific diagnostic biomarkers. This review critically examines the literature for potential biomarkers that may aid in early diagnosis, differentiation between ischemic and hemorrhagic stroke, and prediction of hemorrhagic transformation in ischemic stroke. After a thorough analysis, four promising biomarkers were identified: Antithrombin III (ATIII), fibrinogen, and ischemia-modified albumin (IMA) for diagnostic purposes; glial fibrillary acidic protein (GFAP), micro RNA 124-3p, and a panel of 11 metabolites for distinguishing between ischemic and hemorrhagic stroke; and matrix metalloproteinase-9 (MMP-9), s100b, and interleukin 33 for predicting hemorrhagic transformation. We propose a biomarker panel integrating these markers, each reflecting different pathophysiological stages of stroke, that could significantly improve stroke patients’ early detection and treatment. Despite promising results, further research and validation are needed to demonstrate the clinical utility of this proposed panel for routine stroke treatment.