Miracle-dz/UniFAR-FTUs huggingface dataset:
Widespread microplastic pollution in Indiana, USA, rivers

Microplastics (i.e., plastic particles <5 mm in size) are aquatic contaminants of emerging concern but are poorly quantified in flowing waters of the midwestern USA. Microplastics enter streams and rivers through a variety of pathways (e.g., wastewater effluent, breakdown of larger plastic debris, atmospheric deposition) and can potentially harm aquatic organisms through both direct consumption and indirect contamination from sorbed toxins. In this study, we quantified microplastic concentrations and types (i.e., beads, fibers, films, foams, fragments) in nine Indiana watersheds representing a gradient of dominant land use (i.e., agricultural, urban, and forested). We predicted that microplastic concentration would be higher in watersheds with higher percentages of urban and agricultural land use than in forested watersheds. Our results revealed measurable quantities of microplastics in samples from all watersheds, but microplastic concentration did not vary significantly with land use or longitudinally within watersheds. Fibers were the dominant form of microplastic at all sites, suggesting that fibers may be transported primarily through atmospheric deposition rather than via direct runoff from the surrounding landscape. We conclude that rivers have a different microplastic “signature” than large lakes, likely due to retention characteristics of flowing water ecosystems, unique microplastic sources, and a shorter legacy of microplastic pollution.
Simplified approach for evaluating bioavailability of neutral organic chemicals in sediment

Abstract : This note outlines a tiered approach for evaluation of bioavailability of neutral organic contaminants in sediment and provides a method for the numerical expression of bioavailability. The first tier is a simple mathematical calculation, from sediment chemistry, of maximum potential bioaccumulation. If Tier I calculations indicate potential bioaccumulation of neutral organic contaminants to concentrations of concern, then Tier II laboratory tests could be conducted to determine the actual amount of bioaccumulation. In the second tier, bioaccumulation is assessed in laboratory exposures of organisms to the contaminated sediment. Comparison of the actual bioaccumulation at projected steady state to the calculated maximum potential bioaccumulation results in a measure of bioavailability. pg1. JMD
Improved Fabric Defect Detection Using a Vision Transformer and ResNet Hybrid Model

Fabric defect detection plays a vital role in ensuring the production of high-quality textiles. Manual inspection methods are time-consuming, subjective, and error-prone, necessitating the development of automated systems. This research paper proposes a novel approach to fabric defect detection by combining the strengths of Vision Transformer (ViT) and ResNet-50 architectures into a hybrid model. A notch filter coupled with a fast Fourier transform is also used to improve the quality of the training dataset. The hybrid model aims to capture both local and global information within fabric images, thereby enhancing defect detection accuracy. Extensive experiments on a publicly available fabric defect dataset demonstrate that the proposed hybrid model outperforms the individual ViT and ResNet-50 models and other state-of-the-art fabric defect detection approaches. The results showcase a superior accuracy of 98.5% for our proposed architecture, which is significantly higher than the 93.4% and 96.5% achieved by ResNet and ViT, respectively
Comparative study on conventional and advanced techniques MPPT algorithms for solar energy systems

: The importance of an efficient maximum power point tracking (MPPT) algorithm for a photovoltaic (PV) power generation system is undebatable. It enables the system to achieve its maximum throughput in power generation and generate the best revenue under given meteorological conditions. The non-linear relationship between output power and output voltage of a solar system gives rise to the presence of maximum power point (MPP) at the power voltage curve, which needs to be tracked well through a proficient algorithm. This paper presents a comprehensive overview of MPPT algorithm’s basic operation and the options available for its practical implementation. At first, it delineates some popular conventional MPPT algorithms including the perturbation and observation (P&O) method, incremental conductance (IC), and ripple correlation control (RCC) method. Later, the possibility of integrating state-of-the-art intelligent techniques such as fuzzy logic control (FLC), artificial neural network (ANN), particle swarm optimization (PSO), supervised, unsupervised, and reinforcement machine learning (ML) algorithms for MPPT purposes has been investigated. Operational strategies, advantages, and drawbacks of each algorithm have also been discussed. Consequently, advanced intelligence-based algorithms are found to be outperforming their conventional counterparts in terms of tracking precision, convergence speed and fluctuations at steady state. However, computational and implementational complexities associated with the most intelligence-based methods are motivating researchers to investigate hybrid solutions merging benefits of both conventional and advanced algorithms.
A surface temperature and moisture parameterization for use in mesoscale numerical models

A modified multi-level soil moisture and surface temperature model is presented for use as in defining lower boundary conditions in mesoscale weather models. Account is taken of the hydraulic and thermal diffusion properties of the soil, their variations with soil type, and the mixing ratio at the surface. Techniques are defined for integrating the surface input into the multi-level scheme. Sample simulation runs were performed with the modified model and the original model defined by Pielke, et al. (1977, 1981). The models were applied to regional weather forecasting over soils composed of sand and clay loam. The new form of the model avoided iterations necessary in the earlier version of the model and achieved convergence at reasonable profiles for surface temperature and moisture in regions where the earlier version of the model failed.
Post-activation Potentiation: Effect of Recovery Duration and Gender on Countermovement Jump, Agility, and Linear Speed in Team-Sport Athletes

Background: Studies assessing post-activation potentiation (PAP) responses comparing male and female athletes are conflicting. Objectives: This study investigated whether differences exist in the duration for optimal post-preload stimulus measures on performance in male and female team sport athletes. Methods: Twenty-four participants (12 males and 12 females) participated in the study. Two familiarization sessions were conducted with each participant. Then, three experimental conditions were implemented, incorporating a standardized warm-up, followed by back squats (conditioning exercises) and varying passive recovery times of 4 min (PAP4), 8 min (PAP8), or 12 min (PAP12). Following the recovery, players performed three physical performance measures related to team sports: A countermovement jump, a modified agility t-test, and a 20-m linear sprint. The significance level was set at P < 0.05. Results: All performance measures were significantly greater in PAP12 than in PAP4 and PAP8 conditions in both males (1.50 to 2.95%) and females (1.09 to 5.79%) (P < 0.05). The PAP12 condition also had significantly lower values for HR (3.18 to 5.15 beats.min-1; P < 0.0005) and ratings of perceived exertion (RPE) (0.63 to 1.02; P < 0.05) than PAP8 and PAP4. Males performed better on all the performance tests (19.33 to 26.34%) compared to their female counterparts (P < 0.0005). Conclusions: A pre-load stimulus consisting of one set of 5 repetitions of back squat at 85% one-repetition maximum can elicit a PAP response. A 12-min passive rest after the pre-load stimulus was most beneficial in improving physical performance measures in both male and female team-sport athletes.
Ignition Method Development and First Field Demonstration of In Situ Smouldering Remediation

ii Co-Authorship . iii Intellectual Property . v Acknowledgements . vi Dedication . vii List of Tables . x List of Figures . xi List of Appendices . xv List of Abbreviations . xvi
Solubilization of ibuprofen: Computational and predictive tools

ABSTRACT Herein, some trained models based on the vant’ Hoff and Jouyban-Acree model were proposed for predicting ibuprofen solubility in the mono-solvents and binary systems. The predictive models were obtained by the combination of the vant’ Hoff and Jouyban-Acree model with variables derived from the Abraham, Hansen, and Catalan parameters. The prediction power of the proposed equations was evaluated by computing the overall mean relative deviation values of the solubility data for ibuprofen in 16 mono-solvents and 17 binary mixtures and discussed. The developed equations enable the researchers to predict the solubility trend of ibuprofen in mono- and binary mixtures at different temperatures, which can be helpful in research and industrial applications.
Capital Flows to Developing Countries

This article focuses on the determinants of the large portfolio flows from the United States to Latin American and Asian countries during 1988-92. Cointegration techniques reveal that both domestic and global factors explain bond and equity flows to developing countries and represent significant long-run determinants of portfolio flows. The article also investigates the dynamics of portfolio flows by estimating seemingly unrelated error-correction models. Global and country-specific factors are equally important in determining the long-run movements in equity flows for both Asian and Latin American countries, while global factors are much more important than domestic factors in explaining the dynamics of bond flows. U.S. interest rates are a particularly important determinant of the short-run dynamics of portfolio, especially bond, flows to developing countries.
Dark diaspora tourism: experiences and meanings in liminal spaces

Abstract Few tourism sites possess the combined attributes of dark tourism and diaspora tourism, and journeys to these liminal spaces can unearth complex and nuanced emotions. While previous studies have added knowledge to the experiences of diaspora tourists, little is known about the liminal experiences at dark diaspora tourism sites. While most studies attempt to explore the experiences of tourists to destinations, there is neoteric interest in understanding the hermeneutics of these experiences by offering insights into the contextual interpretation of events. To investigate these, we integrate liminality and stimulus organism theories within a framework of interpretive phenomenology methodology. Forty interviews were conducted with dark diaspora tourists. The study revealed minor and major themes including enlightenment, visceral conflicts, identity dilemmas, and renegotiation of diasporic experiences. Ultimately, it is only by probing the interpretations of dark diaspora experiences from visitors’ own narratives that deep concealed meanings to liminal experiences can be uncovered. The research indicates that experiences at dark diaspora sites vary and result in diverse effects on individuals’ experiences and subsequent interpretations.
The Music Identity Project.

At MayDay Group Colloquium 24 in East Lansing, MI, Sandra Stauffer (2012) charged that: "If we want change, we need to start telling different stories . we work with beginning teachers, and we worry about teacher identities. We tell them a story.one that does not serve them well. A story that they will be prepared. Maybe we should tell stories of self-making, of re-making and replacing ourselves. Of preparation as a constantly evolving teacher story. Maybe then transformation can be the norm. " Sandy’s comments of transformation resonated strongly with the very project I was presenting at the same colloquium on the lived-experiences and music identities of six preservice teachers in a Secondary Music Education Methods course during the spring semester of 2012. This paper emerged out of my presentation and it is my hope that by sharing the stories of these six preservice teachers, as well as my own, that we can show the type of self-making, re-making and replacing ourselves for which Sandy was advocating. [excerpt]
Sequence analysis: new methods for old ideas

A wide variety of work in social science concerns sequences of events or phenomena. This essay reviews concepts of sequence and methods for analyzing sequences. After a brief definitional discussion, I consider sequence literatures from various areas. I then discuss recent methodologies for sequence analysis. I review stepwise approaches like Markovian and event history analysis as well as whole sequence approaches resting on new developments in biology and other fields.
When Language Rewrites History: A Narrative Discourse Analysis of Rory McCarthy's Nobody Told Us We Are Defeated: Stories from the New Iraq

When people communicate, they tend to tell stories, narrate events and place themselves in place and time. It is through narration that social identities are built (Forchtner 2021). Narrative Discourse Analysis has been implemented in critical discourse studies in 1960s. Main events which shape histories of countries and nations have tremendously occupied narrative texts. In 2003, with the USA announcing its invasion to Iraq, real life experiences have been narrated. The present study analyses one of the major novels written on the 2003 Iraq which is Rory McCarthy's (2006) Nobody Told Us We Are Defeated: Stories from the New Iraq . The analysis is carried out through the lens of Narrative Discourse Analysis following Labov and Waletzky (1967) and Johnstone (2001). The study addresses the following questions: (1) in what way have the events that the novelist witnessed acted as the stimulus of the selected narrative piece; (2) how does the language of the novel manage to portray the war on Iraq and the devastating life conditions of Iraqis; (3) what are the features of narrative discourse that the selected novel exhibits; (4) in what way does the narrative structure of stories told by a first-hand writer differ from that written by a novelist who did not witness the events. The study refers to McCarthy's narrative piece as A Novel of Witness that exhibits the functional narrative features which reflect the devastating conditions of a country in war. The novel is not orderly due to the gloomy context of narration. Two types of complication actions are identified here: Main Complicating Actions (MCAs) and Commentary Complicating Actions (CCAs). The study also suggests that McCarthy's text presents a type of narrative referred to as Descriptive Narrative Commentary (DNC).
FACTORS INFLUENCING SURFACE ATMOSPHERE EXCHANGE OF GASEOUS ELEMENTAL MERCURY IN WESTERN MARYLAND

Title of Document: FACTORS INFLUENCING SURFACE ATMOSPHERE EXCHANGE OF GASEOUS ELEMENTAL MERCURY IN WESTERN MARYLAND Christopher Warren Moore, Doctor of Philosophy, 2011 Directed By: Dr. Mark S. Castro Program of Marine, Estuarine, and Environmental Science The purpose of this study was to examine the dynamics of the surface – atmosphere exchange of gaseous elemental mercury (GEM) in western Maryland. The site studied was the Piney Reservoir Ambient Air Monitoring Station (PRAAMS) in Garrett County. I used several different techniques to quantify the exchange, including a soil pore air sampling system that I developed, dynamic flux chambers, and the modified Bowen ratio (MBR) technique. I found that GEM exchange dynamics vary widely from the small scale (< 1 m) of the flux chambers to the large scale of the MBR technique. When I scaled the flux chamber measurements up to the annual time scale, I predicted a net GEM emission of 2.0 to 5.3 μg m yr to the atmosphere from PRAAMS, while with the MBR technique I estimated a GEM deposition of 3.3 μg m y. The differences in these estimates highlight the difficulties with measuring the exchange of a substance that can be both deposited to and emitted from soils. It also shows how uncertain the current estimates of this exchange can be. On the other hand, I was able to quantify soil redox potential as an important driver of GEM fluxes and soil pore total gaseous mercury (TGM) concentrations. This had not been previously reported. I was also able to show the importance of soil organic matter (SOM) and soil pore TGM concentration gradients on the surface –atmosphere exchange of GEM. This work opens the door for other studies on the dynamics of GEM fluxes in all sites with background concentrations of GEM. FACTORS INFLUENCING SURFACE ATMOSPHERE EXCHANGE OF GASEOUS ELEMENTAL MERCURY IN WESTERN MARYLAND
Distinguishing between parallel and serial accounts of multiple object tracking

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Eigenvalue-regularized covariance matrix estimators for high-dimensional data

Covariance regularization is important when the dimension p of a covariance matrix is close to or even larger than the sample size n. This thesis concerns estimating large covariance matrix in both low and high frequency setting. First, we introduce an integration covariance matrix estimator which is a linear combination of a rotation-equivariant and a regularized covariance matrix estimator that assumed a specific structure for true covariance Σ0, under the practical scenario where one is not 100% certain of which regularization method to use. We estimate the weights in the linear combination and show that they asymptotically go to the true underlying weights. To generalize, we can put two regularized estimators into the linear combination, each assumes a specific structure for Σ0. Our estimated weights can then be shown to go to the true weights too, and if one regularized estimator is converging to Σ0 in the spectral norm, the corresponding weight then tends to 1 and others tend to 0 asymptotically. We demonstrate the performance of our estimator when compared to other state-of-the-art estimators through extensive simulation studies and a real data analysis. Next, in high-frequency setting with non-synchronous trading and contamination of microstructure noise, we propose a Nonparametrically Eigenvalue-Regularized Integrated coVariance matrix Estimator (NERIVE) which does not assume specific structures for the underlying integrated covariance matrix. We show that NERIVE is positive definite in probability, with extreme eigenvalues shrunk nonlinearly under the high dimensional framework p/n → c > 0. We also prove that in portfolio allocation, the minimum variance optimal weight vector constructed using NERIVE has maximum exposure and actual risk upper bounds of order p−1/2. The practical performance of NERIVE is illustrated by comparing to the usual two-scale realized covariance matrix as well as some other nonparametric alternatives using different simulation settings and a real data set. Last, another nonlinear shrinkage estimator of large integrated covariance matrix in high-frequency setting is explored, which shrinks the extreme eigenvalues of a realized covariance matrix back to an acceptable level, and enjoys a certain asymptotic efficiency when the number of assets is of the same order as the number of data points. Novel maximum exposure and actual risk bounds are derived when our estimator is used in constructing the minimum-variance portfolio. In simulations and a real data analysis, our estimator performs favourably in comparison with other methods.
Implementation of the Problem Based Learning Model in Science Education: Trend and Opportunity of Research Using Systematic Literature Network Analysis

The purpose of this study is to describe research trend data related to the implementation of PBL in science education. Article metadata was collected from the SCOPUS database, and 131 relevant articles were selected in 2013-2022. Data were analyzed using the Systematic Literature Network Analysis (SLNA) method with the VOSViewer application. Variables of bibliometric analysis, such as the number of articles and citations each year, the journals and authors with the most publications, countries, and networks of authors' cooperation, were determined based on metadata and analyzed descriptively. This research shows that the number of PBL research articles in science education during the 2013-2022 period fluctuated and tended to increase. The top keywords with the highest link strength are science, teacher, and technology. Recent research focuses on modules/e-modules, scientific literacy, natural science, and physics as important keywords. Future research related to the use of PBL learning has the potential to be developed in physics material and focuses on developing 21st-century skills such as creative thinking, critical thinking, communication, and collaboration. In addition, further research can focus on developing the knowledge and skills of prospective teachers in implementing PBL models that are integrated with 21st-century skills.
Amélioration de la représentation des aérosols dans un modèle de chimie-transport : modélisation et assimilation de données

L'objectif general de cette these est d'ameliorer la representation des aerosols dans le modele MOCAGE. Pour se faire, nous avons modifie directement la representation des aerosols en reexaminant et ameliorant les differents processus deja presents via la prise en compte de schemas et de parametrisations plus detailles et implemente l'assimilation de donnees des aerosols dans le modele. Les processus ayant subi les ameliorations les plus importantes sont les emissions des aerosols du type sel marin, poussiere desertique et cendre volcanique, le depot humide et la sedimentation. Nous avons evalue les impacts de ces changements et compare les champs modelises avec des observations. Les modifications implementees ont permis d'ameliorer significativement l'accord entre modele et observations. Comme approche complementaire pour repondre a l'objectif de cette these, nous avons egalement implemente dans le CTM MOCAGE l'assimilation de donnees des aerosols. Le systeme d'assimilation de donnees des aerosols est implemente et il est capable d'assimiler l'epaisseur optique des aerosols (AOD) et les mesures lidar. L'assimilation des observations d'AOD est un outil efficace pour ameliorer les performances du modele en terme d'AOD et de concentration. L'assimilation de mesures lidar sur le signal ainsi que les coefficients de retrodiffusion et d'extinction, ont montre un impact efficace sur les profils verticaux.