Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrazine Degradation in Pesticide-Contaminated Soils: Phytoremediation Potential

Studies were conducted in the laboratory to determine the fate of atrazine in pesticide-contaminated soils from agrochemical dealer sites. No significant differences in atrazine concentrations occurred in soils treated with atrazine individually or combinations with metolachlor and trifluralin. In a screening study carried out in soils from four agrochemical dealer sites, rapid mineralization of atrazine occurred in three out of eight soils tested, with the greatest amount occurring in Bravo rhizosphere soil (35% of the applied atrazine after 9 weeks). Suppression of atrazine mineralization in the Bravo rhizosphere soil did not occur with the addition of high concentrations of herbicide mixtures, but instead was increased. Plants had a positive impact on dissipation of aged atrazine in soil, with significantly less atrazine extractable from Kochia-vegetated soils than from nonvegetated soils. Disciplines Entomology | Environmental Health | Environmental Microbiology and Microbial Ecology | Weed Science Comments Reprinted (adapted) with permission from Phytoremediation of Soil and Water Contaminants, 664(4); 54-64. Doi: 10.1021/bk-1997-0664.ch004. 1997 American Chemical Society. Authors Ellen L. Kruger, Jennifer C. Anhalt, Diana Sorenson, Brett Nelson, Ana L. Chouhy, Todd A. Anderson, and Joel R. Coats This book chapter is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/ent_pubs/374
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Third-Generation Data Mining: Towards Service-Oriented Knowledge Discovery SoKD’10

Knowledge Discovery in Databases (KDD) has grown a lot during the last years. But providing user support for constructing workflows is still problematic. The large number of operators available in current KDD systems makes it difficult for a user to successfully solve her task. Also, workflows can easily reach a huge number of operators (hundreds) and parts of the workflows are applied several times. Therefore, it becomes hard for the user to construct them manually. In addition, workflows are not checked for correctness before execution. Hence, it frequently happens that the execution of the workflow stops with an error after several hours runtime. In this paper we present a solution to these problems. We introduce a knowledge-based representation of Data Mining (DM) workflows as a basis for cooperative-interactive planning. Moreover, we discuss workflow templates, i.e. abstract workflows that can mix executable operators and tasks to be refined later into sub-workflows. This new representation helps users to structure and handle workflows, as it constrains the number of operators that need to be considered. Finally, workflows can be grouped in templates which foster re-use further simplifying DM workflow construction.
An Energy-Efficient Spiking Neural Network Accelerator Based on Spatio-Temporal Redundancy Reduction

The neurons of spiking neural networks (SNNs) carry sparsity to the activation from temporal and spatial. To achieve high energy efficiency, this work proposes layer-wise configurable timesteps (LCTs) and channel-regrouped sparse convolution (CRSC) to explore and exploit the redundancy of temporal and spatial dimensions. In the temporal dimension, principal component analysis (PCA) is utilized to analyze redundant information in each layer, and LCT is adopted to balance and eliminate variable redundancies between layers. In the spatial dimension, channels are regrouped by spike activation frequencies to ensure workload balance, and sparse convolution is implemented to further accelerate SNN’s computation flow. A layer-fuse method that embeds the parameter of batch normalization in leaky integrate-and-fire (BLIF) is also proposed to reduce data movement and processing time. An energy-efficient SNN accelerator integrating the above methods is designed. Compared with the baseline, the hardware equipped with the proposed LCT can achieve $3.22\times $ acceleration by redundancy elimination. The CRSC and BLIF can also reduce the hardware computation and memory access by $3.24\times $ during inference. Implemented in TSMC 28 nm technology, this accelerator can achieve an energy efficiency of 36.89 TOPS/W at 650 MHz.
Comprehensive Evaluation of Quality of Life in Penile Cancer Patients following Surgical Treatment

Background: Penile cancer (PC) is a highly aggressive disease, with a significant tendency for lymphatic spreading and subsequent development of distant metastases. The mutilating nature of PC surgical treatment has profound implications on the patient’s body integrity and self-image, sexual life and intimacy, voiding and mental health. The aim of our study was to comprehensively evaluate PC patients’ post-treatment quality of life (QoL), sexual activity, self-esteem, fatigue and fear of disease recurrence. (2) Methods: A cross-sectional study was conducted at the Clinic of Urology, University Clinical Centre of Serbia, and included 31 PC patients. Data were collected by means of a questionnaire. (3) Results: The average score on the Global health status scale was 67.2 out of 100 (ranging from 16.7 to 100), and the SD was 22.5. Hierarchical linear regression analysis showed that demographic characteristics, Hospital Anxiety and Depression scale (HADS) anxiety and depression scores, total Multidimensional Fatigue Inventory, Fear of cancer recurrence and Rosenberg scores and erectile function score explained a total of 78.2% of the variance in the global health status/QoL scale of PC patients. (4) Conclusions: Efforts should be made not only to increase the survival of PC patients after surgical treatment but also to enable the best possible level of QoL in the post-operative period.
TEM search engine for fast near-surface imaging

We present a search engine method for finding approximate 1D solutions to circular central-loop configuration Transient Electromagnetic (TEM) data. The search engine method is a concept used for query search from a large database on the internet. By extension, several approximate solutions to an input TEM data can be searched rapidly from the pre-established database. While the database size is determined by the survey target and the sensitivity analysis of layers, the fast search speed is ensured by using the Multiple Randomized K-Dimensional (MRKD)-tree method in computer search technology. In addition to its high speed in finding solution, the search engine method gives a solution space, which quantifies resolution and uncertainty of the results. Combining both search and inversion methods can help to interpret TEM data reliably for near surface imaging.
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN

Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z>1) galaxies but their formation and role in galaxy evolution remain unclear. High-resolution observations of low-redshift clumpy galaxy analogues are rare and restricted to a limited set of galaxies but the increasing availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples increasingly feasible. Deep Learning, and in particular CNNs, have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localising specific objects or features in astrophysical imaging data. In this paper we demonstrate the feasibility of using Deep learning-based object detection models to localise GSFCs in astrophysical imaging data. We apply the Faster R-CNN object detection framework (FRCNN) to identify GSFCs in low redshift (z<0.3) galaxies. Unlike other studies, we train different FRCNN models not on simulated images with known labels but on real observational data that was collected by the Sloan Digital Sky Survey Legacy Survey and labelled by volunteers from the citizen science project `Galaxy Zoo: Clump Scout'. The FRCNN model relies on a CNN component as a `backbone' feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN -`Zoobot' - with a generic classification backbone and find that Zoobot achieves higher detection performance and also requires smaller training data sets to do so. Our final model is capable of producing GSFC detections with a completeness and purity of>=0.8 while only being trained on ~5,000 galaxy images.
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights

This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.
Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data

Perceptual metrics are traditionally used to evaluate the quality of natural signals, such as images and audio. They are designed to mimic the perceptual behaviour of human observers and usually reflect structures found in natural signals. This motivates their use as loss functions for training generative models such that models will learn to capture the structure held in the metric. We take this idea to the extreme in the audio domain by training a compressive autoencoder to reconstruct uniform noise, in lieu of natural data. We show that training with perceptual losses improves the reconstruction of spectrograms and re-synthesized audio at test time over models trained with a standard Euclidean loss. This demonstrates better generalisation to unseen natural signals when using perceptual metrics.
Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study

Objectives Computer vision (CV) is a field of artificial intelligence that enables machines to interpret and understand images and videos. CV has the potential to be of assistance in the operating room (OR) to track surgical instruments. We built a CV algorithm for identifying surgical instruments in the neurosurgical operating room as a potential solution for surgical instrument tracking and management to decrease surgical waste and opening of unnecessary tools. Methods We collected 1660 images of 27 commonly used neurosurgical instruments. Images were labeled using the VGG Image Annotator and split into 80% training and 20% testing sets in order to train a U-Net Convolutional Neural Network using 5-fold cross validation. Results Our U-Net achieved a tool identification accuracy of 80-100% when distinguishing 25 classes of instruments, with 19/25 classes having accuracy over 90%. The model performance was not adequate for sub classifying Adson, Gerald, and Debakey forceps, which had accuracies of 60-80%. Conclusions We demonstrated the viability of using machine learning to accurately identify surgical instruments. Instrument identification could help optimize surgical tray packing, decrease tool usage and waste, decrease incidence of instrument misplacement events, and assist in timing of routine instrument maintenance. More training data will be needed to increase accuracy across all surgical instruments that would appear in a neurosurgical operating room. Such technology has the potential to be used as a method to be used for proving what tools are truly needed in each type of operation allowing surgeons across the world to do more with less.
State of Health Estimation for Battery Modules with Parallel-Connected Cells Under Cell-to-Cell Variations

State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but they cannot be directly applied to module-level SOH estimation, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. Experimental datasets are used to illustrate and evaluate the proposed method. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. This trade-off is explored through a case study. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with 0.5% root-mean-square errors and 1.5% average three-sigma values. With all the optimization and training processes completed offboard, the proposed method has low computational complexity for onboard implementations.
Considering the Effects of a Catastrophic Terrorist Attack

This report investigates the short term effects of a catastrophic terrorist attack on deaths, destruction of infrastructure, and property damage. In addition, it explores the longer range economic impacts in the weeks and months after the attack. A scenario analysis was conducted to analyze the short term effects. In the scenario, the Port of long Beach was chosen as the site of the terrorist attack. The tool of strategic gaming was used to provide information about long range economic impacts of the attack.
Comparison of the therapeutic effects of 15 mg and 30 mg initial daily prednisolone doses in patients with subacute thyroiditis: a multicenter, randomized, open-label, parallel-controlled trial

Abstract Introduction Current guidelines recommendations for the initial dose of prednisolone (PSL) in the treatment of subacute thyroiditis (SAT) are based on low-quality studies. We designed a randomized controlled trial (RCT) to compare the efficacy and safety of using a low initial dose of PSL with a standard initial dose of PSL in SAT patients. Patients and methods This open-label RCT was conducted at five hospitals in China from June 2019 to January 2022. SAT patients with moderate-to-severe pain or a poor response to non-steroidal anti-inflammatory drugs (NSAIDs) were randomly assigned in a 1:1 ratio to the experimental and control groups. The initial dose of PSL was 15 mg/d in the experimental group and 30 mg/d in the control group. The primary outcome was the total duration of PSL treatment, with non-inferiority prespecified with a margin of 7 days. Clinical trial registration number: ChiCTR1900023884. Results The full analysis set included 60 patients (30 in each group). The mean duration of PSL treatment in the experimental and control group was 34.62 ± 14.12 and 41.18 ± 16.89 days, respectively, meeting the non-inferiority criterion (pnon-inferiority = 0.0006). The total dose of PSL used in the experimental group was lower than in the control groups (330 vs 595 mg, p < 0.0001). There were no differences in the mean time to pain relief and complete resolution, the occurrence of recurrence, hypothyroidism, or adverse events between the groups. Conclusions The initial dose of 15 mg/d of PSL was not inferior to the dose of 30 mg/d in terms of efficacy and showed a similar safety profile. A low initial dose of PSL could be recommended for Chinese adult SAT patients who have a suboptimal response using NSAIDs or experience moderate-to-severe pain. KEY MESSAGES Low initial dose (15 mg/d) of prednisolone was non-inferior to the standard initial dose of prednisolone (30 mg/d) in treatment duration, time to pain relief, or the prevalence of hypothyroidism, recurrence, and adverse reactions in the treatment of subacute thyroiditis. Patients with subacute thyroiditis administered a low initial dose of prednisolone had a lower total dose of prednisolone compared to those receiving the standard dose of prednisolone.
Biological Oxidation of Manganese Mediated by the Fungus Neoroussoella solani MnF107

Manganese oxides are highly reactive minerals and influence the geochemical cycling of carbon, nutrients, and numerous metals in natural environments. Natural Mn oxides are believed to be dominantly formed by biotic processes. A marine Mn-oxidizing fungus Neoroussoella solani MnF107 was isolated and characterized in this study. SEM observations show that the Mn oxides are formed on the fungal hyphal surfaces and parts of the hypha are enveloped by Mn oxides. TEM observations show that the Mn oxides have a filamentous morphology and are formed in a matrix of EPS enveloping the fungal cell wall. Mineral phase analysis of the fungal Mn oxides by XRD indicates that it is poorly crystalline. Chemical oxidation state analysis of the fungal Mn oxides confirms that it is predominantly composed of Mn(IV), indicating that Mn(II) has been oxidized to Mn (IV) by the fungus.
Participatory Protection in Theory and Application: Paper Tigers, Fences & Fines, or Negotiated Co-Management?

Forest protection can imply binding constraints on communities (‘fences & fines’) yet some attempts to protect forest by blocking local land use are fruitless (‘paper tigers’). Participatory protection, i.e. involving a local community in forest ‘co-management’, is a relatively recent forest policy innovation that falls between these two endpoints. We model the emergence of negotiated agreements that can share management of and benefits from forest between actors with different objectives, a state and forest users. Going beyond models of user-user interactions involving common-property resources, our state-user model addresses the question of when rights are devolved to such users. Next we compare our derived hypotheses about de facto rights, negotiated agreement, and within-agreement transfers – all endogenous – with unique community-level data describing a large protected area in Indonesia. The results broadly support this model.
Seasonal and Interannual Variations of Irminger Ring Formation and Boundary-Interior Heat Exchange in FLAME

AbstractThe contribution of warm-core anticyclones shed by the Irminger Current off West Greenland, known as Irminger rings, to the restratification of the upper layers of the Labrador Sea is investigated in the 1/12° Family of Linked Atlantic Models Experiment (FLAME) model. The model output, covering the 1990–2004 period, shows strong similarities to observations of the Irminger Current as well as ring observations at a mooring located offshore of the eddy formation region in 2007–09. An analysis of fluxes in the model shows that while the majority of heat exchange with the interior indeed occurs at the site of the Irminger Current instability, the contribution of the coherent Irminger rings is modest (18%). Heat is provided to the convective region mainly through noncoherent anomalies and enhanced local mixing by the rings facilitating further exchange between the boundary and interior. The time variability of the eddy kinetic energy and the boundary to interior heat flux in the model are strongly corr.
Comparison of Stereoscopic Fusional Area between People with Good and Poor Stereo Acuity

Purpose: This study investigated differences in stereoscopic fusional area between those with good and poor stereoacuity in viewing stereoscopic displays. Methods: Stereo acuity of 39 participants (18 males and 21 females, 23.6±3.15years) was measured with the random dot stereo butterfly method. Participants with stereo-blindness were not included.Stereoscopic fusional area was measured using stereoscopic stimulus by varying the amount of horizontal disparity in astereoscopic 3D TV. Participants were divided into two groups of good and poor stereo acuity. Criterion for good stereoacuity was determined as less than 60 arc seconds. Measurements arising from the participants were statisticallyanalyzed. Results: 26 participants were measured to have good stereo acuity and 13 participants poor stereo acuity. Incase of the stereoscopic stimulus farther than the fixation point, threshold of horizontal disparity for those with poorstereo acuity were measured to be smaller than the threshold for those with good stereo acuity, with a statisticallysignificant difference. On the other hand, there was no statistically significant difference between the two groups, in caseof the stereoscopic stimulus nearer to the fixation point. Conclusions: In viewing stereoscopic displays, the boundary ofstereoscopic fusional area for the poor stereo acuity group was smaller than the boundary of good stereo acuity grouponly for the range behind the display. Hence, in viewing stereoscopic displays, participants with poor stereo acuity wouldhave more difficulty perceiving the fused image at farther distances compared to participants with good stereo acuity.Key words: Stereo acuity, Stereoscopic fusional area, Stereoscopic display
Speciation analysis of lead, zinc and cadmium in river bottom deposits in Swietokrzyskie Region

Bottom deposits of selected rivers in the Świetokrzyskie Region of Poland flowing through areas of various geological characteristics and land development were analyzed for the presence of cadmium, zinc and lead. Microwave-assisted 3-stage sequential extraction recommended by the European Union (EU) was applied for that purpose as well as a 5-stage extraction proposed by Tessier et al. Residues after sequential extraction were mineralized in concentrated nitric acid in order to check if applied sequential extraction procedures allowed extraction of total amounts of metals from bottom deposits. Metal contents were determined with F-AAS technique in particular extracts.
Revisiting Small-Signal Dynamic Stability Assessment of Multi-Inverter Interfaced Grids

Procedures for developing small-signal dynamic models of multiple inverter-interfaced power grids are well-established in the literature. For grids consisting of multiple buses in such models, the definition of node voltages is however dealt arbitrarily by use of high virtual resistance (<inline-formula><tex-math notation="LaTeX">$R_{v}$</tex-math></inline-formula>) between the corresponding node and the ground. The selection of <inline-formula><tex-math notation="LaTeX">$R_{v}$</tex-math></inline-formula> obviously has an impact on the resultant eigenvalues of the system under consideration. This article proves that even the selection of arbitrarily higher <inline-formula><tex-math notation="LaTeX">$R_{v}$</tex-math></inline-formula> leaves the scope of unacceptable errors in obtaining true system modes. The stability conclusion obtained thereby is also questionable. To address this fundamental gap, a novel approach to redefine node voltages as the functions of system states and known parameters is proposed. The resultant system matrix is free from any random characterization in terms of <inline-formula><tex-math notation="LaTeX">$R_{v}$</tex-math></inline-formula>. The improved and detailed small-signal model of multiple grid-feeding inverters is used to validate the consistency and superiority of the proposed method.