Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Operations-Based Application of Natural Language Processing to Enhance Aircraft System Troubleshooting

Troubleshooting an aircraft system is difficult. With flights often logging hundreds, or even thousands, of codes, the task of isolating the root cause of an issue is a complex undertaking. By leveraging Natural Language Processing techniques such as Word2Vec, artificial intelligence can be used to extract patterns from the context of these faults. Treating the fault codes issued by the on-board system in an aircraft as the “words” which make up a body of text, a model can be trained to understand the patterns of this language in a similar approach to how natural language is processed by computers to discretize the order and structure of human language. By assessing the cosine similarity of vectorized fault sequences used to train the model, faults occurring in similar sequences can be extracted, resulting in improved troubleshooting. The result of this effort is a tool to aid maintainers in isolating faults by quantifying the relations between the different codes and analyzing the patterns in which they occur. The benefits of such a tool include significant reduction in time and cost in aircraft maintenance by avoiding unnecessary exploratory maintenance.
Validity evidence based on content: Controversies and quantification

Validity is a crucial issue in test development because it represents the accuracy of test score interpretation in describing the measured attribute or construct. Among validity evidence that can be collected to support test score interpretation, the one based on test content incited controversies. The content-based validity evidence includes analyses of the relationship between test content and the construct the test purported to measure. Some authors opposed the use of such evidence in the validation process, arguing that the evidence can only provide hypotheses to be tested in the subsequent processes. Other authors argued that the content validation process provided meaningful information regarding test validity, particularly tests based on content such as achievement tests. Unfortunately, literature regarding such issues hitherto has not addressed the controversies adequately. Furthermore, several techniques for quantifying the content-based validity evidence have been proposed without any other article reviewing and evaluating them in comparison with the others. The current study was conducted to examine propositions made by prominent authors regarding validity based on test content and to compare proposed quantification techniques.
Salus Journal

Prison radicalisation in Australia has become a key focal point for the subject literature on radicalisation. However, while attention has been directed at identifying and apprehending violent extremists, less consideration has been given to what could be done with these people when convicted. This paper applies the root cause model to prisoner radicalisation to investigate the environmental, social and individual influences that contribute to radicalisation in prisons. This examination took a holistic view of the prison radicalisation process that is based on causal factors rather than the traditional phase model approach. It is argued that this is an important step in gaining an understanding of the interplay of influences in the prison radicalisation process. It is posited that once such an understanding is gained, it is more likely that effective disruption of the radicalisation process can occur.
Analysis of Adverse Drug Reactions in Pediatric Patients with Epilepsy: An Intensive Pharmacovigilance Study

Epilepsy is a chronic neurological disease characterized by the presence of spontaneous seizures, with a higher incidence in the pediatric population. Anti-seizure medication (ASM) may produce adverse drug reactions (ADRs) with an elevated frequency and a high severity. Thus, the objective of the present study was to analyze, through intensive pharmacovigilance over 112 months, the ADRs produced by valproic acid (VPA), oxcarbazepine (OXC), phenytoin (PHT), and levetiracetam (LEV), among others, administered to monotherapy or polytherapy for Mexican hospitalized pediatric epilepsy patients. A total of 1034 patients were interviewed; 315 met the inclusion criteria, 211 patients presented ADRs, and 104 did not. A total of 548 ASM-ADRs were identified, and VPA, LEV, and PHT were the main culprit drugs. The most frequent ADRs were drowsiness, irritability, and thrombocytopenia, and the main systems affected were hematologic, nervous, and dermatologic. LEV and OXC caused more nonsevere ADRs, and PHT caused more severe ADRs. The risk analysis showed an association between belonging to the younger groups and polytherapy with ADR presence and between polytherapy and malnutrition with severe ADRs. In addition, most of the severe ADRs were preventable, and most of the nonsevere ADRs were nonpreventable.
A PPLICATION OF O RANGE P EEL B IO - N ANOCOMPOSITES AS E NCAPSULANT IN M ICROENCAPSULATION

Paprika Oleoresin (PO) is a widely used natural colour in the food industry and microencapsulation by spray drying has served to be a successful option to prevent colour degradation of these compounds. This research was focused on evaluating the enhancement of physicochemical properties by using a blend of Gum Arabic (GA) and Orangepeel nanofibers (OPNF) as encapsulants. The addition of nanofibers to polymers resulted in superior properties, characterised by mechanical properties and SEM analyses. The wall material (GA:OPNF) was tested for different proportions (50:50, 60:40, 70:30, 80:20) by comparing encapsulation efficiency and moisture content. The best encapsulation efficiency was exhibited at 80:20 but moisture content was lowest at 70:30. Hence the latter was chosen for the evaluation of physicochemical properties such as water activity, hygroscopicity, caking, solubility, density, flowability and colour. The outcomes suggest that the proposed nanomaterial from orangepeel could serve as a potential supplement to the conventional encapsulant.
Interpretable image-based deep learning for price trend prediction in ETF markets

Image-based deep learning models excel at extracting spatial information from images but their potential in financial applications has not been fully explored. Here we propose the channel and spatial attention convolutional neural network (CS-ACNN) for price trend prediction. It utilizes the attention mechanisms to focus on specific areas of input images that are the most relevant for prices. Using exchange-traded funds (ETF) data from three different markets, we show that CS-ACNN—using images constructed from financial time series—achieves on-par and, in some cases, superior performances compared to models that use time series data only. This holds true for both model classification metrics and investment profitability, and the out-of-sample Sharpe ratios range from 1.57 to 3.03 after accounting for transaction costs. The model learns visual patterns that are consistent with traditional technical analysis, providing an economic rationale for learned patterns and allowing investors to interpret the model.
Vegetation survey and mapping of the McArthur River catchment, Northern Territory : March 2008 to February 2009

The Vegetation Survey and Mapping of the McArthur River Catchment, which includes portions of the Gulf Fall and Uplands, Gulf Coastal and Sturt Plateau Bioregions, Northern Territory was instigated as a pilot study for the NT RIS 2007/167 project titled “ A Strategy to refine and regionalise Northern Territory vegetation mapping. ” The improvement from the previous available 1:1m scale mapping is of significant benefit for a range of purposes relating to land resource planning, assessment and management.
Carbon Nanotubes in Nucleic Acids Delivery

Carbon nanotubes (CNTs) are type of nano-materials which have interesting physicaland chemical properties such as thermal, magnetic, surface and chemical properties. These properties make CNTs as promising opportunities for biomedical research and applications. Because of these interesting properties, CNTs were investigated for their drug delivery characteristics to deliver many therapeutic agents such as anticancer, peptides, vaccines, and nucleic acids into specific target cells. Results obtained gave the CNTs great interest in pharmaceutical development field as carriers with promising development future. CNTs were found to have large surface area which gives them the ability to traverse amphipathic cell membranes and flexible interactions with their cargo. Moreover, these interesting features give CNTs the ability to deliver many types of nucleic acids effectively into their targets. In this review, we discuss the different aspects and properties of CNTs and the use of different types of CNTs as drug delivery system for the delivery of many nucleic acids such as siRNA and miRNA.
Molecular Characterization and Management of Aspergillus flavus Link Ex Fries inGroundnut

Molecular Characterization and Management of Aspergillus flavus Link Ex Fries in Groundnut Thirty one isolates collected from different parts of major groundnut growing areas of Karnataka and one from ICRISAT were identified as Aspergillus flavus by molecular technique using species specific primers. Later a field experiment was conducted at two locations under artificial inoculation and natural infection conditions for the pre-harvest management of A. flavus incidence and thereby aflatoxin. The results indicated that all the treatments are effective in reducing the A. flavus incidence and there by aflatoxin level in the produce. Among all the treatments, T-7 was most effective under both the situations. Under artificial inoculation conditions T-7 recorded minimum incidence of A. flavus (1.00 per cent) and aflatoxin content (2.40 µg/kg) with higher benefit to cost ratio (2.86 ) as compared to control (25.90%, 19.90 µg/kg). In natural conditions also T-7 recorded lowest A. flavus incidence (1.40 per cent) and aflatoxin content (0.0 µg/kg) with highest benefit to cost ratio (2.77). Among all the treatments, T-5 (foliar spray with neem oil @ 5 ml/l) was the least effective under both the conditions. The integrated approach was found best under both the situations.
The structure of the Loch Creran Dalradian and a new model for the SW Highlands.

Synopsis The structure of the Dalradian rocks around Loch Creran is described in terms of NE-trending upright D1 folds becoming recumbent to the east, accompanied by a primary D1 swing to the SE at the head of Loch Creran. The rocks are cut by a major slide, the Benderloch Slide, which formed prior to the first cleavage, S1. D2, D3 and D4 deformation-events are also described but these have little effect on the D1 outcrop pattern. The major fault system has been revised. The mapping reveals various objections to the regional structural model proposed by Roberts and Treagus (1977), and a new, simple, ‘mushroom’ model is proposed for the SW Highlands. This involves the presence of a deep-seated massif within the fold belt which controlled the regional fold pattern. The writer concludes by emphasising that the structure of the SW Highlands of Scotland is by no means established.
Imaging features of breast cancer subtypes on contrast enhanced ultrasound: a feasibility study

The objective of this research was to study the contrast enhancement patterns of the different molecular subtypes of breast cancer on contrast-enhanced ultrasound (CEUS) using both qualitative and quantitative parameters. This prospective study included females with a single breast mass which was histopathologically proven carcinoma. B mode ultrasound (USG) and CEUS were performed in all patients during baseline assessment. Qualitative CEUS assessment encompassed enhancement pattern, presence of fill-in and washout. Quantitative assessment included measurement of peak enhancement, time to peak; area under the curve and mean transit time. A p-value < 0.05 was considered statistically significant for differentiating the subtypes. The included thirty masses were categorised into two subtypes—triple negative breast cancer (TNBC) (36.7%) and non-TNBC (63.3%) subtypes. With B-mode USG, a statistically significant difference was observed between the two groups with respect to their shape and margins. TNBC lesions showed an oval shape, circumscribed margins and peripheral nodular enhancement on CEUS with the absence of fill-in even in the delayed phase (p-value – 0.04). The two subtypes did not significantly differ in terms of quantitative perfusion parameters. The various subtypes of breast cancer therefore possess distinct contrast enhancement patterns. CEUS potentially allows differentiation amongst these molecular subtypes that may aid in radiology-pathology (rad-path) correlation and follow up of the patients.
STACKING ENSEMBLE CLASSIFICATION APPLIED TO US FLIGHT DELAY PREDICTION DURING THE COVID-19 PANDEMIC

This thesis aims to show that a Stacking Ensemble of multiple base-learners can provide a more accurate prediction of commercial ﬂight delays between the ten largest US airports than the individual prediction models. Three types of machine learning models, namely LASSO, Random Forests and Neural Networks are used as base-learners with diﬀerent hyper-parameters. A Stacking Ensemble is created by using LASSO as meta-learner. The Stacking Ensemble and the base-learners that performed best on the training data are then evaluated on a test data set. The results are compared by the metrics accuracy, ROC AUC, MCC and F1 Score. It is shown that the Stacking Ensemble is able to provide superior predictions for ﬂight delays in comparison to the best individual models.
A Market Crash or Tail Risk? Heavy Tails and Asymmetry of Returns in the Chinese Stock Market ∗

Rapid stock market growth without real economic back-up has led to the 2015 Chinese Stock Market Crash with thousands of stocks hitting the down limit simultaneously multiple times. We provide a detailed analysis of structural breaks in heavy-tailedness and asymmetry properties of returns in Chinese A-share markets due to the crash using recently proposed robust approaches to tail index inference. The empirical analysis points out to heavy-tailedness properties often implying possibly inﬁnite second moments and gain/loss asymmetry for daily returns on individual stocks. We further present an analysis of the main determinants of heavy-tailedness in Chinese ﬁnancial markets. It points out to liquidity and company size as being the most important factors aﬀecting the returns’ heavy-tailedness properties. At the same time, we do not observe statistically signiﬁcant diﬀerences in tail indices of the returns on A-shares and the coeﬃcients on factors aﬀecting them in the pre-crisis and post-crisis periods.
VU Research Portal Rigorously computing symmetric stationary states of the Ohta-Kawasaki problem in three dimensions

In this paper we develop a symmetry preserving method for the rigorous computation of stationary states of the Ohta–Kawasaki partial diﬀerential equation in three space dimensions. By preserving the relevant symmetries we achieve an enormous reduction in computational cost. This makes it feasible to construct computer-assisted proofs of complex three-dimensional structures. In particular, we provide the ﬁrst existence proofs for both the double gyroid and body centered cubic packed sphere solutions to this problem.
Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms

The planetary boundary layer height (PBLH) has a significant impact on the energy and material exchange in the atmosphere. The traditional gradient method (GM) determines the PBLH based on the gradient change of the aerosol profile. It is susceptible to the effect of complex atmospheric conditions, which leads to uncertainties in the estimation of PBLH. Here, a random forest (RF) algorithm that considers the vertical distribution of aerosols is proposed to find the PBLH under complex atmospheric conditions. The height of the three minimum local peaks of the range correction signal (RCS) profile and seven other variables, such as aerosol layer number (ALN), relative humidity (RH), solar radiation, and other meteorological parameters, from January 2017 to December 2021 is used as an RF model input. The radiosonde estimated PBLH (PBLHRS) is used as a reference value. The sensitivity analysis indicates that the relative error of RF-estimated PBLH (PBLHRF) is smaller than that of GM-estimated PBLH (PBLHGM), and it decreases with an increase in aerosol optical depth (AOD). Moreover, RF achieves good performance under different atmospheric conditions. It can effectively overcome the effects of complex atmospheric conditions in PBLH estimation. Based on the correlation analysis, it is found that the estimation accuracy of the RF algorithm is greatly improved compared with the GM. The correlation coefficient between the PBLHRF and the PBLHRS reaches 0.8, which is much larger than that of the PBLHGM (0.47). Finally, long-term PBLHRF analysis shows that there are obvious diurnal and seasonal variations of PBLH. It increases and then decreases from early morning to late evening. It is highest in summer and lowest in winter. Overall, RF can effectively overcome the shortcomings of traditional GM and has high accuracy and robustness for various atmospheric conditions. The findings obtained here have great potential for lidar application in obtaining reliable PBLH estimations.
Resistance of African tropical forests to an extreme climate anomaly

The responses of tropical forests to environmental change are critical uncertainties in predicting the future impacts of climate change. The positive phase of the 2015 – 2016 El Niño Southern Oscillation resulted in unprecedented heat and low precipitation in the tropics with sub- stantial impacts on the global carbon cycle. The role of African tropical forests is uncertain as their responses to short-term drought and temperature anomalies have yet to be determined using on-the-ground measurements. African tropical forests may be partic- ularly sensitive because they exist in relatively dry conditions compared with Amazonian or Asian forests, or they may be more resistant because of an abundance of drought-adapted species. Here, we report responses of structurally intact old-growth lowland trop- ical forests inventoried within the African Tropical Rainforest Observatory Network (AfriTRON). We use 100 long-term inventory plots from six countries each measured at least twice prior to and once following the 2015 – 2016 El Niño event. These plots experienced the highest temperatures and driest conditions on record. The record temperature did not significantly reduce carbon gains uncertainties in predicting the future impacts of climate change. The 2015 – 2016 El Niño Southern Oscillation (ENSO) resulted in unprecedented heat and low precipitation across the tropics, including in the very poorly studied African tropical forest region. We assess African forest ENSO responses using on-the-ground measurements. Across 100 long-term plots, record high temperatures did not significantly reduce carbon gains from tree growth or significantly increase carbon losses from tree mortality. Overall, despite the climate anomaly, for- ests continued to gain live biomass over the ENSO period. Our analyses, while limited to African tropical forests, suggest that they may be more resistant to climate extremes than Amazonian and Asian forests.
Thyroid Hurthle Cell Neoplasms: Review Article

Hurthle cell carcinoma is a rare type of Differentiated Thyroid Cancer (DTC), accounting for only 5% of all cases of DTC [1]. In 2017, the World Health Organization reclassified the Hurthle cell variant of follicular thyroid carcinoma as a distinct type, due to its different histopathological characteristics from follicular thyroid carcinoma. This reclassification was supported by recent discoveries in molecular characteristics through genetic sequencing tests. Despite these advances, the behavior of Hurthle cell carcinoma and adenoma remains unpredictable. In this paper, we present a short literature review of Hurthle cell neoplasms
HXRF Analysis of Yugüe Obsidian HXRF Analysis of Yugüe Obsidian

Analysis was performed on a 31-artifact sample of Late Terminal Formative obsidian excavated in 2003 from the archaeological site of Yugüe in the Lower Verde Valley of Oaxaca. This analysis was performed to determine the geochemical sources of the individual obsidian artifacts and replicate a prior study of Yugüe obsidian performed by David T. Williams for his thesis at the University of Colorado. This earlier analysis determined that five obsidian sources were present. Sourcing was accomplished using a handheld X-Ray fluorescence instrument and bivariate plotting of relevant trace elements. Five sources of obsidian were found during analysis: Pachuca, Otumba, Paredon, Guadalupe Victoria, and Zaragoza. Williams identified additional sources that were not identified in this study, but he also may have sampled artifacts from the site from other excavations. This previous analysis by Williams also did not attribute sources to individual artifacts, making it impossible for archaeological conclusions to be drawn about the life histories of particular artifacts. By attributing sources to individual artifacts during analysis, this project provides valuable context about both the site of Yugüe during the Terminal Formative period and the lower Rio Verde Valley.
A High Dynamic Range Envelop Detector for Heterodyne Receiver Architecture

Energy detection (ED) has been of interest for various applications ranging from very low frequency biomedical signal read-out to high frequency wireless communication. The analog implementation of the ED requires circuits with a non-linear transfer characteristics which can be obtained through squaring or rectification. Comparison of these two methods is presented and implementation limitations are detailed. Interesting characteristics of rectifiers, derived from ideal transfer characteristics and from non-ideal circuit implementations, encourage rectifier’s use in the ED rather than the squarer. A high speed and high precision current mode full-wave rectifier was implemented in GF 22FDX technology. Our circuit achieves a 60 dB dynamic range and 100 MHz bandwidth with an ultra-low power consumption of 4.3 $\mu \text{W}$ from 0.8V voltage supply.