Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of complementary and alternative health care practices among children.

INTRODUCTION The purposes of this descriptive study were to: (a) describe the use of complementary and alternative medicine (CAM) practices for children as reported by their parents, and (b) to develop a CAM screening tool. METHOD A convenience sample of 191 parents was obtained from 3 primary care settings within a midwestern, metropolitan area. The questionnaire consisted of: (a) seven screening questions, (b) a list of specific CAM therapies used by children and by parents, (c) six open-ended questions about sick child care, and (d) a demographic section. RESULTS Thirty-three percent of parents reported using CAM for their child within the past year, most commonly citing infant massage, massage therapy, vitamin therapy, and botanical products. Analysis revealed that White parents who used CAM for themselves and had children school-age and older were significantly more likely to provide CAM for their child. Chi-square analyses between the responses to selected screening questions and use of CAM were statistically (P =.001) significant but identified only 24% of the parents who used CAM for their children. DISCUSSION Pediatric nurses should ask parents and children about CAM use in an open manner that promotes discussion. Further refinement of the screening tool is needed.
Lung Nodule Classification Using MobileNet Transfer Learning

Lung cancer is one of the deadliest forms of cancer, responsible for millions of deaths worldwide each year. The detection and classification of lung nodules can be challenging due to the small size and obscured locations of nodules, especially in early-stage cancers. To address this challenge, this study proposes an automatic lung nodule classification method that utilizes deep learning (DL) techniques to improve accuracy and diagnosis time. The study’s primary goal is to outperform current methodologies in detecting malignant lung nodules and predicting lung cancer. To achieve this objective, different DL models, including CNN, VGG16, and MobileNet, were evaluated for their efficacy in a classification task. The results showed that using transfer learning with MobileNet achieved the highest accuracy rate of 99.11%. The proposed technology can help medical personnel locate and diagnose lung nodules early, allowing for faster intervention and potentially better patient outcomes. Overall, this research emphasizes the potential of MobileNet transfer learning in facilitating the diagnostic process of lung nodules, which can be critical for accurate diagnosis and treatment planning.
Contexte et atouts du poste

The Delys group (previously Regal) <http://team.inria. fr/regal/> is a joint team of Inria and Sorborne Universty whose research topics cover the whole spectrum of distributed systems and multicore systems. Our research topics span the whole spectrum between theoretical aspects of distributed systems, such as possibility and impossibility results, consensus or fault detectors, to the design and implementation of highly efficient algorithms and systems, such as an extreme-scale geo-replicated database minimising synchronisation. We publish in the best venues across the spectrum, such as PODC, DISC, SSS, OPODIS, JPDC, OSDI, EuroSys, Middleware, SRDS, DSN, IPDPS, EDCC, OSR, Systor, ASPLOS, POPL, etc.
Contexte et atouts du poste

This PhD thesis is proposed in the context of the PIMPE project: Physical complex Interactions and Multi-person Pose Estimation. The project is funded by the IDEX of the Communauté Université Grenoble Alpes, under the International Strategic Partnerships program. Therefore, the PhD funding is granted and the student will spend half of the PhD in Grenoble and half of the PhD in Barcelona. In Grenoble, the PhD student will be hosted at the Perception Team of Inria/LJK (https://team.inria.fr/perception/). In Barcelona, the PhD student will be hosted at IRI-UPC (https://www.iri.upc.edu/).
DeepImputeIDS: Enhancing Intrusion Detection Systems with Deep Learning-Based Missing Data Imputation

This paper introduces a novel approach for handling missing data in Intrusion Detection System (IDS) datasets, named DeepImputeIDS. This approach incorporates a multistage process involving several data handling, preprocessing, and imputation methods to improve the input quality for subsequent deep learning models. Furthermore, we propose the Random Missing value (RMV) algorithm, a technique to introduce missing values into a dataset randomly. This serves as a valuable tool for testing and comparing different methods of handling missing data. The effectiveness of DeepImputeIDS is evaluated using the NSL-KDD and UNSW-NB15 datasets, with results indicating considerable improvement in handling missing values. We also present an analysis of the impact of missing data imputation on five common machine learning classifiers, revealing that the imputation strategy should be tailored to the specific characteristics of the dataset and the models in use. The findings underscore the significance of thoughtful missing data imputation in machine learning tasks, particularly in the context of IDS.
Analysis of Power System Frequency with Combined Contribution of Synchronous and Converter-Based Sources

With the rapid development of renewable energy sources, the structure of sources has changed, which has modified the frequency characteristics of the power system. In this paper, the combined contribution of synchronous sources and converter-based sources was quantitatively analyzed. The weight of sources on system frequency was defined. The influence of electrical distance, power capacity, and other factors on the weight was analyzed. Based on a power system example with converter-based sources, the accuracy of this study was verified.
Pengaruh Pengenaan Pajak Pertambahan Nilai (PPN) dan Pajak Penjualan atas Barang Mewah (PPnBM) terhadap Daya Beli Konsumen Barang Elektronika (Studi Empiris pada Konsumen Barang Elektronika di Wilayah Jalan ABC Kota Bandung)

Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pengenaan PPN dan PPnBM terhadap daya beli konsumen barang elektronika. Populasi dari penelitian ini adalah konsumen barang elektronika yang berada di wilayah Jalan ABC Kota Bandung dengan menggunakan metode purposive sampling untuk penentuan sampel. Sampel yang diuji adalah konsumen pada toko elektronik di wilayah Jalan ABC Kota Bandung. Uji statistik yang digunakan adalah model regresi berganda. Hasil penelitian menunjukkan bahwa secara parsial Pajak Pertambahan Nilai (PPN) tidak memiliki pengaruh yang signifikan terhadap variabel daya beli konsumen, sedangkan variabel Pajak Penjualan atas Barang Mewah memiliki pengaruh positif yang signifikan terhadap variabel daya beli konsumen. Secara simultan pengenaan Pajak Pertambahan Nilai (PPN) dan Pajak Penjualan atas Barang Mewah (PPnBM) mempunyai pengaruh yang signifikan terhadap daya beli konsumen.
Factor Graph Optimization-based Indoor Pedestrian SLAM with Probabilistic Exact Activity Loop Closures using Smartphone

Indoor localization by smartphones has indicated its promising application prospect in daily life. Smartphone-based pedestrian dead reckoning (PDR) is a common method to obtain the locations. However, PDR suffers from position error accumulation. Although radio frequency (RF) and indoor map can be utilized to restrain the error drift, it requires the prior deployment of facilities or information, which is unsuitable for unknown environments. This paper proposes a factor graph optimization (FGO)-based indoor pedestrian simultaneous localization and mapping (SLAM) with probabilistic exact activity loop closures using a smartphone. In this paper, the smartphone built-in inertial measurement unit (IMU) is solely used to achieve SLAM, in which the human turning activity is regarded as the landmark. Repeatedly observed activities are then used to form loop closures to restrain the drift. FGO is first utilized to formulate pedestrian IMU-only SLAM, which achieves better estimation accuracy than the filter-based method. Moreover, multi-hypothesis tracking is employed to deal with ambiguous data association. During the turning, key points are defined and mutually matched to form exact loop closures to improve estimation accuracy. Simulations and experimental tests are both done to evaluate the performance of the proposed method.
Accuracy Of Cone Beam Computed Tomography In Horizontal Linear Skull Measurements

Introduction: Over the past century, craniometric measurements have been considered of great significance in the field of dentistry, especially in the field of orthodontics and orthognathic surgery. Over the last decade, the usage of Cone Beam Computed Tomography (CBCT) has tremendously escalated to overcome the drawbacks of 2D imaging techniques. Accordingly, it has been the focus of many studies to evaluate the accuracy of this radiographic technique in obtaining linear skull measurements. Aim : The aim of this study was to determine the reliability of linear measurements obtained from CBCT in comparison to direct skull measurements. Materials and Methods: This study was conducted on twelve dry human skulls. Three direct horizontal linear parameters were obtained between certain identified landmarks on each skull using a digital caliper. The skulls were subjected to CBCT examination. The measurements were obtained from CBCT 3D Volume Rendering (3D VR) images, then compared to the real skull measurements and statistically assessed for accuracy. Results: The results of the current study revealed very good intra and inter-observer agreement regarding all the assessed linear measurements obtained from gold standard and CBCT scans. In addition, CBCT showed non-significant underestimated values with difference ranging from (-0.13 mm, -0.10 %) to (-0.32 mm, -0.34 %) in comparison to direct skull measurements (gold standard). Conclusion: Linear measurements derived from CBCT are accurate and reliable and can be confidently used for different maxillofacial clinical applications especially in orthodontics.
Hide and seek: hidden genetic variance contributing to the adaptive potential of selfing populations

Standing genetic variation is considered a major contributor to the adaptive potential of species. The low heritable genetic variation observed in self-fertilising populations has led to the hypothesis that species with this particular mating system would be less likely to adapt. However, a non-negligible amount of cryptic genetic variation for polygenic traits, accumulated through negative linkage disequilibrium, could prove to be an important source of standing variation in self-fertilising species. Using a classical quantitative genetics model, we demonstrate that selfing populations are better able to store cryptic genetic variance than outcrossing populations, notably due to their lower recombination rate. Following a shift in the environment, this hidden diversity can be partially released, increasing the additive variance and adaptive potential of selfing populations. In such conditions, even though the process of adaptation itself is mating system dependant, selfers reach levels of fitness that are equal to or higher than outcrossing populations within a few generations. Outcrossing populations respond better to selection for the new optimum, but they maintain more genetic diversity resulting in a higher genetic load. In selfing populations, genetic diversity is remobilised, and new close-to-optimum genotypes are generated and quickly increase in frequency, leading to more homogenous populations. Our results bring new insights into the role of standing genetic variation for adaptation in selfing populations.
Vegetable Oil to Biolubricants: Review on Advanced Porous Catalysts

Vegetable oil is one of the most potential sustainable feedstocks to produce fuels and chemicals. The review emphasizes isomerization of fatty acid as an important path for biolubricant production. The role of solid acid catalysts, including zeolites, was highlighted to design better isomerization catalysts. The isomerization is a favored mesoporous site with intermediate Bronsted acid strength, which is also enhanced after metal doping on a porous surface. The hierarchical ferrierite (FER) catalyst showed the best selective isomerization with the 10-membered ring cavities, which can be regenerated easily. FER can be produced using a low-cost organic structure-directing agent. The possibility to design two-dimensional pore zeolites with pore mouth selectivity was also discussed. Moreover, the challenges for biolubricant formulation, with focus on palm oil, were also discussed with a detailed comparison to other vegetable oils. The highest palm oil conversion was achieved over the base catalyst, namely, Sr.
Research on the Method of Identifying the Operating State of Converter Transformers Based on WOA-DBN

In recent years, the global interconnection of UHVDC and EHV systems has made the operating environment of transformers increasingly harsh. As the core equipment of the Electric power transmission, the safe and stable operation of converter transformers is crucial. At present, domestic and foreign scholars have conducted extensive research on the vibration characteristics and status monitoring of ordinary power transformers based on vibration methods, but the research on converter transformers is not perfect. Conduct research on the state recognition method of converter transformers, introduce deep confidence networks as the basic algorithm for converter transformer operation state recognition, and use the WOA algorithm to optimize the selection of DBN hyperparameters, forming a classification recognition algorithm based on WOA-DBN. State recognition was conducted to verify the feasibility of the algorithm, and the classification performance of the algorithm was verified with the help of classification accuracy and recognition efficiency. The experiment shows that the proposed method for identifying the status of converter transformers has good engineering practicality and can be further applied to fault identification and operational status monitoring of converter transformers.
PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI

Wound is a condition where the tissue was damaged. The body will repair this damage by wound healing mechanism which often leads to the formation of scar. The scar is caused by the inflammation phase of wound healing mechanism. Ibuprofen is one of the antiinflammatory agent that can inhibit or shorten the inflammatory phase by inhibit cyclooxygenase
Department of Civil and Environmental Engineering Phytoremediation and Bioremediation of Perchlorate at the Longhorn Army Ammunition Plant Final Report Section 1 Executive Summary

Phytoremediation is the use of vegetation for in-situ treatment of hazardous wastes. It is an emerging technology that has proven effective and relatively inexpensive at many pilot and full-scale sites. Plants have shown the ability to withstand high concentrations of some toxic chemicals and take-up contaminants quickly, converting them to innocuous end-products. In addition, plants can stimulate the degradation of chemicals in the rhizosphere by the release of root exudates and enzymes, and the build-up of organic carbon in the soil. Preliminary results have demonstrated hybrid poplars can remove ammonium perchlorate (NH 4 ClO 4) from hydroponic solution or simulated groundwater. In addition, it has been demonstrated in the laboratory that facultative bacteria are capable of reducing perchlorate and chlorate under reducing conditions to chloride ions and dissolved oxygen, innocuous end-products. Work done for this project has addressed objectives specific to the perchlorate concerns at the Longhorn Army Ammunition Plant (LHAAP) at Marshall, Texas. This work consisted of laboratory studies conducted to determine the uptake and transformation of perchlorate by plants and bacteria; also seven novel bacteria capable of perchlorate degradation were isolated from soil samples collected at the LHAAP. The potential for stimulation of bacteria utilizing poplar roots was also investigated. This is the final report in a series describing our efforts to determine the fate of perchlorate in hybrid poplar trees and microorganisms. This fundamental research was conducted to investigate the possibility of treating the perchlorate issues present at the LHAAP by bioremediation, and to discover the primary design criteria. The work done on this project has shown the potential for success of phytoremediation/rhizoremediation system at the Longhorn Army Ammunition Plant.
A Comparative Analysis of Machine Learning Methods for Algal Bloom Detection Using Remote Sensing Images

Algal blooms are a major environmental challenge for lakes and reservoirs and pose severe threats to water on both aquatic and human health. Conventional algorithms used for algal bloom detection based on remote sensing reflectance proved to be effective in some lakes. However, it is still difficult to obtain high accuracy for multiple lakes using single-threshold-based detection. Currently, machine learning (ML) algorithms have been applied to pinpoint algal bloom locations with excellent results, but the ability of different ML models to be applied in different lakes is still unknown. This article presents the performance of algal bloom detection with commonly used ML algorithms in Chinese eutrophic inland lakes based on Sentinel-2 images. A series of comprehensive tests for accuracy, stability, and robustness was designed for four ML models, including random forest (RF), extreme gradient boosting, artificial neural network, and support vector machine, which were tested in Lake Taihu, Lake Chaohu, and Lake Dianchi. In addition, the index-based methods, including floating algae index and adjusted floating algae index, were also calculated for comparison with ML methods. The results showed that the RF model outperformed other ML models. The comparison results between the RF model and algal indexes revealed that the overall accuracy of RF remained above 0.90. Even with a single lake dataset used as training samples, the RF still maintained a fairly high accuracy of 0.88 for other lakes. To summarize, the four ML models demonstrate promising potential for algal bloom detection across different lakes and provide a practical reference for further applications.
A Comparative Analysis of Machine Learning Approaches for Fake News Detection

Considering it has an opportunity to harm society and the nation, the proliferation of fake news via social media platforms is a major cause for worry. There have been several searches for it. This research study assesses the investigation and legalization of fake news identification and reveals some of the most popular machine learning regulations to end up generating an accurate representation of a good or service using methods from machine learning that can categorize fake news as either true or false using instruments like Python Scikit-Learn and the use of natural language processing to earn writing criticism. This study recommends utilizing the nltk package of the functional computer programming language to handle crucial communication components and different pictures of text data, as it includes helpful resources like Indicators Measurement and Tiff Vectorizer. As a result of this procedure, features were retrieved and vectorized. Finally, they will use confusion matrix data to test out the feature selection method and select the most accurate features.
A Comparative Analysis of Machine Learning Models for Early Detection of Hospital-Acquired Infections

As more and more infection-specific machine learning models are developed and planned for clinical deployment, simultaneously running predictions from different models may provide overlapping or even conflicting information. It is important to understand the concordance and behavior of parallel models in deployment. In this study, we focus on two models for the early detection of hospital-acquired infections (HAIs): 1) the Infection Risk Index (IRI) and 2) the Ventilator-Associated Pneumonia (VAP) prediction model. The IRI model was built to predict all HAIs, whereas the VAP model identifies patients at risk of developing ventilator-associated pneumonia. These models could make important improvements in patient outcomes and hospital management of infections through early detection of infections and in turn, enable early interventions. The two models vary in terms of infection label definition, cohort selection, and prediction schema. In this work, we present a comparative analysis between the two models to characterize concordances and confusions in predicting HAIs by these models. The learnings from this study will provide important findings for how to deploy multiple concurrent disease-specific models in the future.
Efforts to Increase the Effectiveness of Learning the Qur'an By Using the Group Tutor Method

The purpose of this study is to find out the method of group tutors can improve children's ability to read the Qur'an according to tajwid in Sidoharjo village to find out the extent to which the group tutor method can improve the ability of children to read the Qur'an according to tajwid in Sidoharjo village. This type of research uses quantitative methods. Meanwhile, data analysis uses cumulative frequency analysis. The subjects in the study were children in Sidoharjo village based the results of the study showed that the group tutor method in the learning process that by using the group tutor method to increased the effectiveness of learning the Qu'ran. The increase in effectiveness occurs due to the management of the learning process in groups with heterogeneous backgrounds of children with abilities Learning using the group tutor method can increase the effectiveness of children, the average effectiveness time reaches 71.9%.
MPC-Based Control Strategy for Air-Conditioning Loads Participating in Frequency Regulation

Air-Conditioning Loads (ACLs) can participate in real time active power control and thus support power system Frequency Regulation (FR). However, the constraint by the thermal dynamics limits the frequency control performance. To fill this gap, a Model Predictive Control (MPC)-based control strategy is proposed in this paper to deal with thermodynamical restriction. By predicting the future state of the system and updates the control coefficients of ACLs control system accordingly, out of limit of the ACLs' control capacity can be avoided, and the frequency control performance can be therefore improved. Simulation results verify the effectiveness of the proposed method.
Mini-Symposium : Infant upper airway and ventilatory control Upper airway reflexes in response to gastric reflux

Gastric reflux, and especially laryngopharyngeal reflux, occur several times a day in every infant. Most often, this does not pose any problem. However, in certain conditions, the contact between the refluxate and the upper airway mucosa can trigger several reflexes leading to cardiorespiratory inhibition. This is especially true for the laryngeal chemoreflexes, which are triggered by laryngeal penetration of gastric refluxate. The laryngeal chemoreflexes are held responsible for a subset of apnoeas of prematurity, many apparent life-threatening events, and probably some cases of sudden infant death syndrome. Although a number of experiments in newborn animals, as well as a few clinical studies in human infants, have been performed in the last 40 years to evaluate laryngeal chemoreflexes, their true role in neonatal cardiorespiratory events is still highly debated. In addition, many uncertainties persist with regard to treatment and prevention of their potentially dramatic consequences. 2010 Elsevier Ltd. All rights reserved.