Miracle-dz/UniFAR-FTUs huggingface dataset:
Mini-Workshop: Statistical Methods for Inverse Problems Table of Contents

Inverse problems appear naturally in a broad range of applications. Numerical analysis and statistics have – often independently – developed methods for regularisation and inversion. The aim of this mini-workshop is to bring together these methods and to consider their use in applications, with a focus on mathematical finance. Mathematics Subject Classification (2000): 62Gxx, 65R32, 91Bxx. Introduction by the Organisers The mini-workshop on ”Statistical methods for inverse problems” gathered seventeen people from the area of statistics and numerical analysis. The goal of the workshop was to stimulate discussions around results and methods that are commonly used by different scientific communities. These methods mainly concern regularization of linear and nonlinear inverse problems in presence of deterministic or stochastic noise. The week was articulated around three main lectures, divided into two talks each. Yuri Golubev (Université of Marseille) started on Monday-Tuesday mornings to give an extended lecture on inverse problems from a statistical perspective. He was quickly followed by Thorsten Hohage (University of Göttingen) who gave lectures on Monday-Tuesday afternoon on inverse problems from a numerical analysis perspective. A third lecture was given on Wednesday-Thursday morning by Rama Cont (École Polytechnique) who addressed the issue of inverse problems in finance, with both deterministic and probabilistic points of view. The main talks were completed by informal contributed talks of approximately one hour each, at the approximate rate of two talks per day except for the mandatory walk in the Schwarzwald, which was unfortunately canceled due to bad weather conditions that concentrated on the only day of rest! 3058 Oberwolfach Report 51/2006 Round tables were planned to follow the lectures, but the friendly and informal atmosphere soon raised constant questions and discussions among the participants during the lectures so that the workshop progressively moved to informal, yet intense scientific discussions: particular focus was given on optimal tuning parameter choice, stochastic or deterministic error modeling and the complexity of numerical schemes. These discussions took place during several extended talks that skipped the initially planned schedule, fortunately tightened by the strict meal hours of the Oberwolfach center. The excellent atmosphere of the mini-workshop was made possible thanks to the exceptional working conditions at the MFO to which we would like to express our deep gratitude. Marc Hoffmann and Markus Reiß Mini-Workshop: Statistical Methods for Inverse Problems 3059 Mini-Workshop: Statistical Methods for Inverse Problems
Phenotypic Diversity of a Leafroller Archips podana (Lepidoptera, Tortricidae) Does Not Change along an Industrial Pollution Gradient

Simple Summary Populations of the large fruit-tree tortrix Archips podana consist of a mixture of four morphs that differ in male genitalia traits, ecology and sexual behaviour. Here, I tested whether these morphs differently respond to pollution. I found that the within-population diversity of A. podana around the industrial city of Lipetsk in central Russia correlated neither with the distance to the nearest industrial polluter nor with the number of moths captured by pheromone traps. Thus, the four phenotypes of A. podana similarly respond to pollution. Abstract Morphological polymorphism offers rich opportunities for studying the eco-evolutionary mechanisms that drive the adaptations of local populations to heterogeneous and changing environments. In this study, I explore the association between pollution load, abundance of large fruit-tree tortrix Archips podana and its within-species diversity (expressed in the presence of apical and/or lateral prongs on the phallus in male genitalia) across 26 study sites located 0.5 to 31 km from the industrial city of Lipetsk in central Russia. The Shannon diversity index, calculated from the frequencies of four morphs, correlated neither with the distance to the nearest industrial polluter (a proxy of pollution load) nor with the number of moths captured by pheromone traps (a measure of population abundance). The statistical power of the correlation analysis was sufficient (67%) to detect a medium effect (i.e., Pearson correlation coefficient with an absolute value of 0.40), if it existed. I conclude that the four phenotypes of A. podana do not differ in tolerance to industrial pollution and similarly respond to pollution-induced environmental disturbance. This is the first study of industrial pollution impacts on within-species diversity of insects expressed in the discrete traits of their male genitalia.
The Translation, Validation and Cultural Adaptation of Questionnaires Assessing the Quality of Life and Fatigue among Patients with Sjogren’s Syndrome for the Romanian Context

About 70% of patients with Sjogren’s syndrome suffer from fatigue, and for a large proportion of patients, it is one of the most noticeable manifestations leading to disability. To date, no study has been conducted in Romania to determine the quality of life of patients and the impact of fatigue in patients with Sjogren’s syndrome. The present work proposes the translation, cultural adaptation, and validation of two questionnaires for the Romanian context, namely the ‘Profile of Fatigue and Discomfort–Sicca Symptoms Inventory’ (PROFAD-SSI-SF) and ‘Primary Sjogren’s Syndrome—Quality of Life’ (PSS-QoL), whose purpose is to assess quality of life and fatigue in patients with Sjogren’s syndrome. These two questionnaires were administered to 52 patients with Sjogren’s syndrome diagnosed according to the 2016 ACR-EULAR criteria. Subsequently, the conceptual, semantic, and operational analyses of the data were performed with the aim of cultural adaptation. The data obtained were statistically analyzed using indices of measurement accuracy such as internal consistency. Based on statistical analyses, this pilot study shows that the Romanian versions of the PROFAD-SSI and PSS-QoL questionnaires are as reliable as their English counterparts.
Predicting Student Performance in Online Learning During Pandemic Using Machine Learning Techniques

Any educational institution's main objective is to raise educational standards through the academic success of its students. To do this, schools can use Educational Data Mining (EDM), a growing field of research that builds on the core concepts of Data Mining (DM), to learn important facts like how students felt about the Covid-19 outage's impact on their online education. With EDM, educators have experimented with a variety of strategies for predicting students' behaviors in order to create optimal learning environments. Students' learning behaviors and academic outcomes can be predicted using techniques gleaned from the field of Educational Data Mining. The lockdown declared after the emergence of Covid-19 pushed several universities to embrace digital educational resources. Adoption of e-learning has expanded, as has the availability of data connected to online learning, in the decade after the COVID-19 epidemic. As a result, scientists have been motivated to create ML-based models to foretell students' achievements. This research study analyzes how the Covid-19 outbreak and its aftermath affected academic performance predictions. A standard dataset is used for this purpose; it is compiled online using a questionnaire and draws from a wide range of primary and secondary sources. In addition, four machine learning classification algorithms were employed to make predictions about the student's performance and then compared with state of art models. Researchers found that students' academic performance and mental health suffered when they used electronic learning aids like cellphones, computers, and tablets too much.
Optimal Configuration Strategy of Condensers for Suppressing Large-Scale Wind Power Generation Low Voltage Ride Through Short-Time Active Power Impact

Given the significant percentage of renewable energy sources in the power grid, the problems of frequency stability and voltage stability are becoming more and more prominent. By configuring the synchronous condenser (SC), it has the capability to furnish dynamic reactive power assistance, thereby enhancing its ability to maintain voltage support and reduce the active power impact caused by low voltage ride through (LVRT) of wind turbine (WT). However, the current power grid lacks an effective configuration strategy for the SC. This paper analyzes the impact of the access of the SC on the voltage support capacity based on the active power current impulse (APCI). The results show that the node with the lowest APCI is the optimal configuration position of the SC after the access of the SC. Based on the above conclusions, this paper proposes an optimal configuration scheme for a SC based on APCI. The simulation results show that the method proposed in this paper can effectively suppress the short-term active power impulse caused by large-scale wind power LVRT and avoid the transformation from local voltage problem to global frequency problem.
The effect of activation functions and the number of layers on the identification of citrus leaf diseases using CNN

The identification of citrus diseases is crucial for maintaining the well-being and efficiency of citrus cultivation. The present study aims to examine the efficacy of various activation functions in the identification of citrus diseases through the application of deep learning methodologies. The activation functions being evaluated comprise Sigmoid, tanh, and Rectified Linear Unit (ReLU). The present investigation employs the Citrus Leaves Data set, comprising 58 images of healthy citrus leaves and 551 images of ailing citrus leaves afflicted by different diseases, including Black Spot, Melanose, Canker, and Greening. The experimentation involves the utilisation of four distinct neural network architectures, namely a network with 2 convolution layers, a network with 4 convolution layers, a network with 6 convolution layers, and the VGG16 model. The experimental findings indicate that the utilisation of a neural network comprising four convolution layers and tanh activation function results in the highest level of accuracy. Specifically, this model achieved an accuracy rate of 71.35% in the detection of citrus diseases.
COVER SHEET

The Information Retrieval and Web Intelligence (IR-WI) research group is a research team at the Faculty of Information Technology, QUT, Brisbane, Australia. The IR-WI group participated in the Terabyte and Robust track at TREC 2005, both for the first time. For the Robust track we applied our existing information retrieval system that was originally designed for use with structured (XML) retrieval to the domain of document retrieval. For the Terabyte track we experimented with an open source IR system, Zettair and performed two types of experiments. First, we compared Zettair’s performance on both a high-powered supercomputer and a distributed system across seven midrange personal computers. Second, we compared Zettair’s performance when a standard TREC title is used, compared with a natural language query, and a query expanded with synonyms. We compare the systems both in terms of efficiency and retrieval performance. Our results indicate that the distributed system is faster than the supercomputer, while slightly decreasing retrieval performance, and that natural language queries also slightly decrease retrieval performance, while our query expansion technique significantly decreased performance.
A comparative analysis of machine learning approach for optimizing antenna design

With the increasing demand for smarter antenna design in advanced technology applications, well-designed antennas have been an important factor in enhancing system performance. Most traditional antenna design requires multiple iterations and extensive testing to produce a final product. Machine learning (ML) algorithms have been used as an alternative to predict the optimal design parameters, but the outcome depends highly on the ML model efficiency. With recent development in machine learning algorithms and the availability of data for antenna design, we investigated different machine learning algorithms for optimizing the output strength of three basic antennae by analyzing the signal strength of the antenna for various antenna parameters. Different regression-based ML models were used to learn the behaviors and efficiency of three different antennas and to predict the output strength (S11) for different ranges of frequencies. The experiment compared and analyzed these ML regression algorithms for three different antennas: shot antenna, patch antenna, and bowtie antenna. In addition, the paper also provides comparison of ensemble ML models for performance analysis using the best three ML algorithms from the preliminary study. This study optimizes antenna parameters and quicker and smarter antenna design procedure using ML algorithms as compared to traditional design methods.
The Stream‐Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States

We developed an extensive database of landscape metrics for ~2.65 million stream segments, and their associated catchments, within the conterminous United States (U.S.): The Stream‐Catchment (StreamCat) Dataset. These data are publically available (http://www2.epa.gov/national-aquatic-resource-surveys/streamcat) and greatly reduce the specialized geospatial expertise needed by researchers and managers to acquire landscape information for both catchments (i.e., the nearby landscape flowing directly into streams) and full upstream watersheds of specific stream reaches. When combined with an existing geospatial framework of the Nation's rivers and streams (National Hydrography Dataset Plus Version 2), the distribution of catchment and watershed characteristics can be visualized for the conterminous U.S. In this article, we document the development and main features of this dataset, including the suite of landscape features that were used to develop the data, scripts and algorithms used to accumulate and produce watershed summaries of landscape features, and the quality assurance procedures used to ensure data consistency. The StreamCat Dataset provides an important tool for stream researchers and managers to understand and characterize the Nation's rivers and streams.
Biosorption of Cr(III) from aqueous solution using an agricultural by-product jute stick powder: Equilibrium and kinetic studies

The adsorption capacity of chromium(III) from synthetic waste water solution by a low cost biomaterial, Jute Stick Powder (JSP)was examined. A series of batch experiments were conducted at different pH values, adsorbent dosage and initial chromium concentration to investigate the effects of these experimental conditions. To analyze the metal adsorption on to the JSP, most common adsorption isotherm models were applied. To study the reaction rate, the kinetic and diffusion models were also applied. The morphological structure and variation of functional groups in the JSP before and after adsorption was examined by scanning electron microscope (SEM) and Fourier transform infrared spectrometry (FT-IR). Maximum chromium removal capacities of JSP was 84.34%with corresponding equilibrium uptake 8.4 mg/g from 50 mg/L of synthetic metal solution in 60 minutes of contact time at pH = 6.0 and 28 °C with continuous stirring at 180 rpm. The percent sorption of the biomass decreased with increasing concentration of metal ion but increased with decreasing pH, increasing contact time and adsorbent doses. Data for this study indicated a good correspondence with both isotherms of Langmuir and Freundlich isotherm. The analysis of kinetic indicated that Chromium was consistent with the second-order kinetic adsorption model. The rate of removal of Cr(III) ions from aqueous solution by JSP was found rapid initially within 5-30 minutes and reached in equilibrium in about 40 minutes. The investigation revealed that JSP, a low cost agricultural byproduct, was a potential adsorbent for removal of heavy metal ions from aqueous solution.
Environmental Policy: New Directions for the Twenty-First Century

Scholars of environmental policy, policy in general, and political science examine some of the most important developments in environmental policy and politics since the 1960s and central issues of today. They focus on the underlying trends, institutional strengths and shortcomings, and policy dilemmas that all policy actors face in trying to resol
Spatial and Temporal Dynamics of Disturbance Within and Between Forest Regions of the U.S.

Title of Dissertation: SPATIAL AND TEMPORAL DYNAMICS OF DISTURBANCE WITHIN AND BETWEEN FOREST REGIONS OF THE U.S. Katelyn Anne Dolan, Doctor of Philosophy, 2015 Dissertation directed by: Professor. George C. Hurtt, Geographical Sciences Forest disturbances play a critical role in shaping forest structure and influencing the ecosystem services that forests provide. However, the rates, patterns and consequences of disturbance remain largely uncertain. How do disturbance rates vary within and between regions and how vulnerable are forests to changes in disturbance? This research takes a tiered approach to quantifying the spatial and temporal patterns and impacts of disturbance within and between diverse forested landscapes of the contiguous U.S. First an intraregional characterization of the patterns and process of disturbance, as captured by over a quarter century of Landsat imagery was performed over the highly forested northeastern state, New Hampshire U.S. Next an interregional comparison of disturbance rates, trends and size distributions were conducted across three regions representing diverse forested landscapes in the U.S. with different dominant disturbance regimes. Finally, a framework was developed to assess the vulnerability of forested ecosystems to disturbance and how vulnerability may change in the future. Results showed that disturbance is not homogenous but varies both spatially and temporally within and between regions. Further ecosystem vulnerability to disturbance varies strongly across the U.S., with western forests generally exhibiting greater sensitivity and vulnerability to disturbance under current climates. Under a potential climate scenario, the majority of U.S. forest area was estimated to increase in resiliency to disturbance, which may buffer some of the impact of intensified forest disturbance. The challenge and opportunities going forward is to continue to quantify and integrate the complex rates, patterns and processes of disturbance into ecosystem models and field study designs that link impact assessment of changes to ecosystem function and services. SPATIAL AND TEMPORAL DYNAMICS OF DISTURBANCE WITHIN AND BETWEEN FOREST REGIONS OF THE U.S.
Mini-Symposium : Non-CF Bronchiectasis Pathogenesis of bronchiectasis §

Non-cystic fibrosis bronchiectasis is a heterogeneous condition and its pathogenesis is still not well defined. A combination of a defect in host defense and bacterial infection allows microbial colonization of the airways resulting in chronic inflammation and lung damage. An ongoing cycle of infection and inflammation may be established. Typically, the walls of the small airway are infiltrated by inflammatory cells causing obstruction whilst mediators, such as proteases released predominantly by neutrophils, damage the large airways resulting in bronchial dilatation. Adjacent parenchyma is also involved in the inflammation. Lung function testing generally demonstrates mild to moderate airflow obstruction that progresses over time. There are a large number of different aetiologic factors associated with bronchiectasis. A variety of different microbial pathogens is involved and they change as disease progresses. 2010 Elsevier Ltd. All rights reserved.
Erigeron mancus elevational density gradient as a baseline to detect future climate change in LaSal Mountain habitats

The LaSal daisy, Erigeron mancus, is endemic to timberline and alpine habitats of the LaSal Mountains in Utah (Nesom 2006)(Fig. 1), an insular, laccolithic mountain range on the Colorado Plateau in southeastern Utah (Blakey and Ranney 2008). Erigeron mancus occurs in alpine herbaceous communities from timberline to the crestline of the Middle Group of the LaSals (Smith 2008) and is on the Forest Service Sensitive Plant Species List (USFS 2003). Prior to the current study little was known about the population biology of this species.
Probabilistic Hazard Assessment of Tsunamis Induced by the Translational Failure of Multiple Submarine Rigid Landslides

Probabilistic Hazard Assessment of Tsunamis Induced by the Translational Failure of Multiple Submarine Rigid Landslides. (August 2011) Arturo Jimenez Martinez, B.S., Jackson State University Chair of Advisory Committee: Dr. Patrick J. Lynett A numerical study aimed at probabilistically assessing the coastal hazard posed by tsunamis induced by one-dimensional submarine rigid landslides that experience translational failure is presented. The numerical model here utilized is the finitedifference recreation of a linear, fully dispersive mild-slope equation model for wave generation and propagation. This recreated model has the capability to simulate submarine landslides that detach into multiple rigid pieces as failure occurs. An ad-hoc formulation describing the combined space-time coherency of the landslide is presented. Monte Carlo simulations are employed, with an emphasis on the shoreward-traveling waves, to construct probability of exceedance curves for the maximum dimensionless wave height from which wave statistics can be extracted. As inputs to the model, eight dimensionless parameters are specified both deterministically in the form of parameter spaces and probabilistically with normal distributions. Based on a sensitivity analysis, the results of this study indicate that submarine landslides with large width to thickness ratios and coherent failure behavior are most effective in generating tsunamis. Failures modes involving numerous slide pieces that fail in a very compact fashion, however, were observed to induce bigger waves than more coherent landslides. Rapid weakening in tsunami generation potential for some of the parameter combinations suggests that the hazard posed by submarine landslide tsunamis is strongly dependent on source features and local conditions and is only of concern for landslides of substantial dimensions.
Session Topic

Life is fantastic. It has " ultrafine " structures, " highly-integrated " systems, and " tremendous " functions. Thus, life should be a kind of best teacher for us. In fact, the concept of " biomimetic " has an old history. We can say that an airplane is a biomimetic product of a bird! However, the history of characterization of bio-molecules is not so old. At last, we have recently obtained much information about bio-system. In addition, due to the recent progress of chemistry and nano-sized materials, fabrication accuracy of artificial materials finally becomes similar level to natural bio-molecules and bio-systems. Therefore, many ideas, hints, and suggestions come to material science from bio-systems nowadays. And, biomimetic materials are a hot topic in science. Also, a bio-process itself is fascinating. Its nonlinearity is a key to make ultrafine structures of bio-molecules and their assembly. Thus, the interdisciplinary between mathematics and biomimetic materials are interesting in future. In this session, we would like to introduce our two research examples, " Biomimetic gating membrane " and " Bio-fuel cell " .