Miracle-dz/UniFAR-FTUs huggingface dataset:
An interbasin comparison of tree‐ring reconstructed streamflow in the eastern United States

Tree‐ring reconstructions of streamflow are uncommon in the eastern United States compared with the western United States. Although the east has not experienced severe drought on the scale of the west over the last 100 years, multiyear droughts have stressed the water management systems throughout the east. Here, we reconstruct mean May–September streamflow of three rivers serving population centers in the northeast (Beaver Kill River serving New York, NY), mid‐Atlantic (Potomac River serving Washington, DC), and southeast (Flint River serving Atlanta, GA) to demonstrate the efficacy of reconstructing streamflow in the eastern United States. Then, we conducted an interbasin comparison to identify periods of common variability and examined the influence of the North Atlantic subtropical high on reconstructed streamflow. Our models explained 40–61% of the variance in the instrumental record and passed verification tests in all basins during the period 1675–2000 CE. Droughts and pluvials showed some synchrony across all basins, but the mid‐Atlantic region acted as a hinge, sometimes behaving more like the northeast, and other times like the southeast. Climatic analyses suggest a relationship exists between the North Atlantic subtropical high and reconstructed streamflow that influences the probability of drought and pluvial events. Given the many factors influencing tree growth in closed‐canopy systems, we have shown that careful standardization of individual tree‐ring series, nested regression models, and the use of multiple species can produce robust proxies of streamflow across the eastern seaboard.
Microbial ecology of the Iberian pyrite belt deep subsurface

The existence, characteristics and potential limits of the deep subsurface biosphere have been a matter of growing interest over the last decades as it poses unique challenges for the development and maintenance of life. Albeit preliminary studies made clear that the microbial abundance and diversity in the deep subsurface biosphere were much higher than originally anticipated, it remains almost completely unexplored due to the difficulty of obtaining reliable samples. Therefore, a series of wide-ranging research questions regarding the energetics, phylogenetic, metabolic diversity and ultimate origin of such ecosystems are still unanswered. The Iberian Pyrite Belt (IPB, southwest of Spain) is the most important volcanogenic massive sulphide district in the world and the largest sulphur anomaly in the Earth's crust. It has been long hypothesized that its underground ore bodies are responsible for the unique conditions of the extremely acidic Tinto River, which is born near the sulphide-rich Peña de Hierro mining site. Moreover, the IPB deep subsurface has been recognized as a terrestrial Mars analogue due to its geological similarities to known Martian environments. Its study can thus help to elucidate several key questions, including the degree of influence of deep subsurface life in surfacial events, the possibility of a lithoautotrophic early Earth, or the potential of life as we know it for thriving in apparently barren environments such as the present Mars. During the course of this thesis we have focused in the microbiological characterization of two boreholes (160 and 613 metres deep) drilled in the Peña de Hierro area, using both conventional and molecular ecology techniques. This has included the development of an optimized protocol for sterile DNA isolation and amplification from mineral samples, as well as the use of novel bioinformatic tools to minimize errors in 16S rRNA gene-based population studies and assess the degree of contamination introduced during sample retrieval and processing. The coupling of microbial taxonomy profiles with physicochemical data allowed us to describe the geomicrobiological processes occurring in the deep subsurface of the IPB. Based on this information, a 420 metres deep sample was selected for metagenomic analysis. Finally, a novel microbial species capable of iron biomineralization was isolated from a 297 metres deep sample. Together, our results indicate the presence of active microbial communities at several depths of the IPB deep subsurface, including those responsible for the genesis of the Tinto River conditions, and provide novel insights on the multiple metabolic strategies used by microorganisms to overcome the challenges posed by deep subsurface environments. Our results show that short-scale geological context determines microbial community composition in deep subsurface ecosystems, with water and oxygen availability being the key factors that select for different microbial metabolisms.
MeshLab and Arc3D : photo-reconstruction and processing 3D meshes

The paper presents a complete free software pipeline for the 3D digital acquisition of Cultural Heritage assets based on the use of standard photographic equipment. The presented solution makes use of two main tools: Arc3D and MeshLab. Arc3D is a web based reconstruction service that using computer vision techniques are used to vision technique based on the automatic matching of image features compute for each photo a depth map. MeshLab is a tool that allow to import and process these range maps in order to obtain a ready to use 3D model. Through the combined use of these two systems it is possible to digitally acquire CH artifacts and monuments in affordable way
What's so local about global climate change? Testing social theories of environmental degradation to quantify the demographic, economic, and governmental factors associated with energy consumption and CO2 emissions in U.S. metropolitan areas and counties

This research investigates the consequence of a crucial and not yet fully explored problem: the reluctance of the United States to sign and ratify international agreements, like Kyoto, that aim to mitigate climate change and its underlying social and ecological impacts. This unwillingness has inspired local governments, mayors, metropolitan area governance consortia, state governments, and governors to take on the climate challenge without the directive of the federal government. Local areas of the U.S. are experiencing climate-changerelated impacts such as receding beach lines due to sea level rise and intense storms, fresh water shortages, and extreme weather events. As a result, researchers have begun to explore the human dimensions of climate change through an inquiry in: among many other topics, the vulnerability of local areas to the impacts of climate change and the forces shaping local areas’ contribution to climate change. This study addresses the latter issue using the STIRPAT framework – a reformulated version of the I=(P)(A)(T) formulation that relates environmental impacts (I) to population growth (P), affluence (A), and technology (T). I address three questions that have thus far been poorly answered in prior research: “across the U.S., do local areas differ in the extent of their contribution to climate change? ”, “what are the causes of variation in energy use and carbon dioxide (CO2)
Automated Extraction for Vulnerability Management on PSIRT: A Case Study of Automotive Industry

To ensure security of products, it is necessary to design and develop them securely on the basis of secure-by-design and handle newly discovered threats and vulnerabilities during the operation phase. The Product Security Incident Response Team (PSIRT) is the organisation responsible for handling threats and vulnerabilities during product operation phase. PSIRT gathers a lot of threat intelligence and vulnerability information daily and assesses their impacts on the products. However, the large amount of information makes it difficult for PSIRT analysts to check manually whether all the information is relevant to their products due to human resources and time constraints. In addition, it is also difficult to identify all the hardware or software components used in the products. To solve these problems, we propose modelling the composition of products using Common Platform Enumeration (CPE) dictionary and then building a text classification model that automatically identifies threat intelligence and vulnerability information related to the products. In this paper, we focus on the automobile as an example of products and use an XLNet model trained with vulnerability information published by the National Vulnerability Database (NVD) to extract automotive-related information. To verify the effectiveness of the constructed model, we evaluate it using vulnerability information without training data (i.e. NVD), Social Networking Service (SNS), and paper abstracts as test data. As evaluation metrics, we use the reduction rate for automotive-unrelated information because the amount of it that PSIRT analysts needed to check can be reduced. As a result, the amount of automotive-unrelated information could be reduced by 96.0% for NVD, 94.0% for SNS, and 68.6% for paper abstracts without missing some automotive-related information.
Mixtron: Bandit Online Multiclass Prediction with Implicit Feedback

The exploitation and exploration dilemma is a crucial issue in bandit online multiclass prediction. Conventional algorithms typically resort to either random sample or estimate uncertainty for exploration. In contrast, we propose a novel scheme that focuses solely on exploitation with implicit feedback. To ensure efficient information feedback even when predictions are incorrect, we introduce mixed losses into our proposed scheme. We derive two mixed versions of the multiclass hinge loss and the logistic loss, along with their corresponding algorithms. Context-free and context-aware experiments are conducted to evaluate the performance of our proposed algorithms. Experiment results demonstrate that random sampling are unnecessary if a reasonable loss function is employed. By compared with several state-of-the-art baselines on both synthetic and real-world datasets, our proposed algorithms show their superior performance. Remarkably, they even outperform the Perceptron algorithm with full-information feedback in some cases.
Contextual Target-Specific Stance Detection on Twitter: Dataset and Method

To understand different aspects of online human behaviors, e.g., the public stances toward various social and political issues, contextual target-specific stance detection has become one of the most important studies on social media. Considering the lack of appropriate data for the studies of contextual target-specific stance detection on Twitter, which is one of the most popular online social platforms worldwide, we introduce CTSDT, a new dataset that consists of a large number of annotated target-specific conversations collected from Twitter. Furthermore, we propose a new contextual target-specific stance detection model called ConMulAttn, which is the first method that can learn both the contents of the posts and the concrete relationships between the posts in a conversation. We conduct extensive evaluation using CTSDT as well as another two popular datasets, CreateDebate and ConvinceMe, for contextual target-specific stance detection. The evaluation results validate the necessity of introducing our dataset CTSDT. Besides, according to the evaluation results, our proposed model ConMulAttn can outperform the state-of-the-art contextual target-specific stance detection method by up to 25% in F1 score, indicating the effectiveness and superiority of our solution. Our study has the potential to assist policymakers in utilizing conversation data from online social platforms to efficiently gain real-time insights into public stances on target topics, such as vaccination.
Research on Ground Object Echo Simulation of Avian Lidar

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced.
Optimal Application of Mobile Substation Resources for Transmission System Restoration Under Flood Events

This article studies the Transmission Restoration Problem with Mobile Substation Resources, a novel mixed-integer linear programming model that prescribes the most effective usage of mobile-substation resources to enhance the resilience of a power transmission system against a particular, widespread flood event. The model is a two-stage stochastic program in which each scenario captures a different potential progression of flood heights at substations over the event horizon. The first stage concerns the pre-event selection and positioning of mobile-substation resources. The second stage concerns the coordination of mobile-substation resource deployment and permanent-substation restoration to maintain and recover service within the horizon. Experiments in the IEEE 24-Bus System and a synthetic Houston grid confirm the efficacy of the model. Even when isolated from effects related to restoration of permanent substations, the effect of four mobile transformers and eight mobile breakers for a realistic set of flood scenarios in the synthetic Houston grid was found to be an average total-cost reduction of approximately ${\$}$ 35MM (i.e., approximately 8% of a default optimal objective value). Additionally, a novel, parallel heuristic is designed that can efficiently solve the problem as well as, with minor modifications, similar stochastic problems on pre-selection of mobile resources or placement of static ones. For a 40-scenario model instance in the IEEE 24-Bus System, the extensive form was not able to find an integer-feasible solution in six hours, yet the heuristic achieved an optimality gap no worse than 4.5% in two hours.
College Student’s Financial Well-Being on Java Island: The Role of Financial Literacy, Financial Behavior, and Financial Socialization

After graduating from college, a student's financial situation is critical since it impacts their general level of contentment with life. This study used financial behavior among university students on Java Island to investigate the impact of financial socialization and literacy on financial well-being. This kind of study uses a non-probability sampling method and quantitative methods, involving 134 students in Java Island data processing procedures with Smart PLS. The findings revealed that financial socialization and financial literacy are favorably related to financial well-being. Financial literacy and financial socialization positively affected both financial behavior and financial well-being. Financial conduct acts as a mediating variable between financial well-being, financial literacy, and financial socialization, which both have a favorable impact.
The posterior limit of the area pellucida (pPL) as a reliable proxy for the end of the primitive streak in chick elongation studies

Early embryo elongation involves coordinated cellular and tissue behaviors that are readily observable in the chick embryo vertebrate model system. Easily identifiable morphological landmarks are crucial to obtain reliable morphometric data, particularly when assessing tissue elongation over time. The posterior end of the primitive streak marks the caudal end of the chick embryonic tissue. However, the identification of its precise location is ambiguous, especially to the untrained eye. Herein, we assessed if the posterior limit of the area pellucida (pPL), which is readily recognizable due to the optical contrast with the area opaca, is a valid proxy for the caudal limit of the primitive streak. Measurements of total embryo length were performed in multiple images of chick embryos over time using both caudal landmarks. We found that the pPL offered greater precision and a higher degree of inter-user reproducibility, when compared to the end of the primitive streak. Importantly, our work uncovers a quantitative proportionality between embryo length measurements using the end of the primitive streak and the pPL as caudal landmarks. We have thus validated the pPL as a reliable morphological proxy for the end of the primitive streak in chick embryo elongation studies.
The Geology of the Wicken Vision Area, lower Cam valley, Cambridgeshire, UK

Abstract The geology of the Wicken Vision Area in the lower Cam valley is described with reference to a stacked series of ten cross-sections through Holocene and Pleistocene deposits, and Cretaceous/Jurassic bedrock across the region. A detailed cross-section through a Holocene palaeochannel, with radiocarbon dating and pollen analyses is also presented. The structural geology of the bedrock and the stratigraphic arrangement of the Pleistocene and Holocene deposits are used to describe landscape development for the area. This study offers a new understanding of the stratigraphy and context of the Holocene, Weichselian and earlier Pleistocene deposits of the lower Cam valley, and provides a unique view of sediment architecture in valley-fill sediments at the fen edge.
Semi-Automated Equipment for CO2 Purification and Graphitization at the A.E. Lalonde AMS Laboratory (Ottawa, Canada)

Abstract New computer-controlled, semi-automatic systems were designed and built for CO2 purification and graphitization at the A.E. Lalonde Accelerator Mass Spectrometry (AMS) Laboratory with consideration for user friendliness and high throughput. The stainless steel vacuum lines are orbitally welded to ensure clean seams with low memory. The insulated graphitization ovens with plug-in electrodes provide a hazard-free environment for operators. The closed-loop cooling system circulating low-viscosity Dynalene at –40°C provides highly efficient water trapping. The LabVIEWTM software features (1) pressure and temperature recording for QA/QC; (2) safety interlocks to preclude operator errors resulting in sample loss, cross-contamination, or damaging a vacuum pump; and (3) automation for leak checking, iron conditioning, and running samples. Results from the first year of routinely measured standards, reference, and background materials are reproducible and within acceptance values. In the first year of operation (commissioned in spring 2014), over 1000 targets (~60% unknowns) were produced. With new tube sealing and CO2 purification lines, and two more graphitization lines now operational, the Lalonde AMS Laboratory is able to provide routine radiocarbon analysis (>200 µg carbon) at a capacity of more than 7000 targets per year. Most importantly, the equipment is safe and intuitive, making it ideal for education and training students to run their own samples.
Predicting Time to Diabetes Diagnosis Using Random Survival Forests

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder with increasing population incidence. However, T2DM takes years to develop, allowing onset prediction and prevention to be a clinically effective treatment strategy. In this study we propose and assess a novel approach to diabetes prediction which integrates a specialized extension of the random forest algorithm known as random survival forest (RSF). Rather than predicting a binary outcome, this machine learning model incorporates survival analysis methodology to predict the time until a patient will receive a diabetes diagnosis if their current lifestyle is maintained. We trained a baseline model on 7,704 electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with 14 biomarker and comorbidity features across different measurement dates. Although tuning parameters were purposefully chosen for quick training rather than for predictive performance, our model exceeded expectations with a concordance index of 0.84. Thus, RSF models have been shown to produce accurate timelines of diabetes onset trajectory, providing patients with quantifiable and relatable risks that are easy to understand. The results of our study have substantial implications for advancing machine learning in clinical decision support and patient outcome predictions, emphasizing the role of innovative models in improving predictive accuracy.
Spatial and temporal evolution of urban resilience in China and analysis of barriers: Based on a sustainable development perspective

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China’s urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a “high-high-low-low” clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure.