Miracle-dz/UniFAR-FTUs huggingface dataset:
Inclusión y barreras en la educación de universitarios con discapacidades

Introducción: Las personas con discapacidad son un grupo vulnerable y discriminado en su derecho a la educación. La falta de datos y evidencia es una barrera limitante para la inclusión educativa, ya que limita la creación de programas y medidas para apoyar a esta minoría. Objetivo: Determinar el número y tipo de discapacidades o restricciones funcionales en los estudiantes, así como su percepción de barreras que limiten su inclusión educativa. Método: Estudio descriptivo, prospectivo, de cortes transversales, exploratorio. La muestra (N=61) correspondió a estudiantes de reciente ingreso identificados con discapacidades y/o detectados con algún tipo de restricción funcional mediante el Cuestionario Washington (2001). Resultados: el 53.5% de los encuestados reportó alguna discapacidad o restricción de la funcionalidad. Las discapacidades visuales (42.86%) fueron las más frecuentes y las restricciones de la funcionalidad predominantes fueron la ansiedad (37.3%) y depresión (23.9%). La problemática principal percibida por los estudiantes fue la falta de infraestructura y transporte adecuados (26.76%). El 26.23% y 3.28% consideraron que estas barreras han afectado su desempeño académico de poco a mucho. 19.6% de los encuestados respondieron que debe restringirse el acceso a la licenciatura a personas con discapacidad (discapacidad emocional o mental predominantemente). Conclusiones: Se observa mayor atención a las discapacidades motoras tanto en las medidas de infraestructura tomadas por la institución como en las necesidades de asistencia técnica percibida por los estudiantes, así como tendencia a excluir a personas con discapacidades emocionales o mentales.
Energy storage for electrical systems in the USA

Energy storage is becoming increasingly important as renewable generation sources such as Wind Turbine and Photo Voltaic Solar are added to the mix in electrical power generation and distribution systems. The paper discusses the basic drivers for energy storage and provides brief descriptions of the various energy storage technologies available. The information summarizes current technical tradeoffs with different storage approaches and identifies issues surrounding deployment of large scale energy storage systems.
Numerical Studies of Hydrogen and LPG Turbulent Premixed Flames

- This paper presents numerical studies of turbulent premixed flames for lean hydrogen and stoichiometric LPG mixtures. The transient flames under investigation propagate past repeated solid baffle plate(s) and a square obstruction with varied area blockage ratios in a lab-scale combustion chamber. The chamber allows for up to three removable baffle plates to be equipped in addition to a square obstacle to increase turbulence intensity within the chamber. The hydrogen mixture is studied at an equivalence ratio of 0.7 and the LPG mixture is investigated at an equivalence ratio of 1.0. An in-house computational fluid dynamics (CFD) model is applied to numerically evaluate transient flame propagation. The large eddy simulation (LES) technique is applied for turbulence modelling. Reaction rate calculations are carried out using a dynamic flamelet model for turbulent premixed flames. Four flow configurations with different area blockage ratios (ABRs) are used to investigate combustion overpressure. Numerical results are compared against published experimental data to ascertain the ability of the numerical model in reproducing key combustion events for hydrogen and LPG. A conclusion is drawn that the increase in blockage ratio raises peak combustion overpressure and the maximum rate of pressure rise. Hydrogen combustion, albeit at a lower equivalence ratio, results in higher maximum overpressures and peak rate of pressure rise when compared with LPG.
Modeling and Predictive Control of Nonlinear Coupled and Underactuated Dynamics of a Hexacopter

This paper considers the application of Kalman Filtering (KF) for Unmanned Aerial Vehicle (UAV) identification. A 3 Degree-Of-Freedom (DOF), nonlinear, coupled, and underactuated dynamics were considered for formulating the mathematical model to be used in numerical simulation. This work presents a simulation study that investigates unknown dynamics model parameters estimation of a hexacopter UAV under the presence of noisy feedback signals. Subspace identification method via principle component analysis was used to identify a precise nonlinear model based on Kalman filter to estimate and predict the hexacopter’s altitude. The results are compared with those obtained without using the predictive technique.
The Effect of Switching From Warfarin to Novel Oral Anticoagulants on Patients’ Satisfaction and the Travel Burden in a Rural Setting

Introduction: New oral anticoagulants (NOACs) have shown comparable efficacy to warfarin in the treatment of patients with venous thromboembolism (VTE), stroke and atrial fibrillation (AF). Various studies on quality-of-life improvement in rural patients following the switch from vitamin K antagonists (VKAs) to NOACs have produced inconclusive results. The aim of the study is to assess the impact of switching from warfarin to NOACs on remotely living patients’ quality of life and the burden of travel. Methods: A questionnaire was provided to the patient by their pharmacists. The questionnaire assessed their travel burden and their level of satisfaction with their treatment. Results: The switch from warfarin to NOACs reduced the burden of travel in 75% of patients. A total of 66% of patients were hesitant about the efficacy of their warfarin treatment. The inconvenience caused due to international normalized ratio (INR) monitoring was reduced in 83% of patients; 70% and 72% of patients strongly agreed that NOACs improved their adherence and treatment satisfaction, respectively. The average number of patients’ travels for INR testing for warfarin monitoring was 7.27 trips/year. The average number of trips made by the patient to obtain their NOACs and warfarin scripts was 2.1 and 4.81 trips/year, respectively. Conclusion: The switch from warfarin, a VKA, to NOACs in patients who live in remote areas without medical services improved their quality of life, decreased their travel burden, and increased their treatment satisfaction and adherence. Switching to NOACs reduced the number of trips travelled by patients to obtain their anticoagulation scripts and/or to adjust their doses.
Does Higher Parental Involvement Lead to Learning Gains? Experimental Evidence from Indonesia

We study how information delivered by teachers to parents on students’ learning progress and guidelines for active involvement in children's education can improve learning outcomes. We conducted a randomized control trial experiment in 130 primary schools in Kebumen District, Central Java, Indonesia. The implementation of the intervention collided with the school closures due to the COVID-19 pandemic, adding to the significance of this intervention to help parents in undertaking learning from home. We find that the intervention increased parental involvement at home and communication with teachers. The information also improved parental demand to teachers which increased teachers’ motivation and support in students’ learning. However, the positive impacts on parents and teachers did not translate into improved student numeracy test scores. Further investigation revealed that parents' low capability to teach their children and the lack of right support given by teachers to students during learning from home hindered the impact on learning outcomes.
Contextual Factors behind Audience Engagement Behaviours of YouTube Vloggers: A Case Study

YouTube as a social media site for online videos has become a major platform for the distribution and consumption of video blogs (vlogs). Famous YouTube video bloggers (vloggers) can obtain large audiences and become important for product marketing. The success of vloggers can be related to the achievement of audience engagement, manifested by viewers’ participation and consumption on YouTube. Existing studies have explored vloggers’ audience engagement behaviours (AEBs) in their videos. This work-in-process research shifts focus from content to the vlogging “context” - situational factors involved during the production of vlogs. Context has been studied in subjects including human-computer interactions (HCI), television and language use, but rarely in vlogging. Previous research unveiled that context could affect bloggers’ written content. Research in marketing suggests the effect of context on brands’ engagement strategies towards consumers. However, the relationships between vlogging context and vloggers’ AEBs in videos have rarely been explored. This study explores the question “How can vlogging context affect vloggers’ audience engagement behaviours in videos? ” This study implemented a qualitative analysis of videos from two famous UK YouTube vloggers. The analysis currently focuses on exploring how three key types of context (vlogger, audience and environmental context ) may affect the two AEBs – interaction and self-disclosure. The results propose that the three contexts affect vloggers’ AEBs through multiple contextual factors within each context. This highlights the importance of the vlogging context regarding its impact on vloggers’ implementation of AEBs. The study contributes to establishing a further understanding of AEBs of vloggers by taking context into account in addition to content. It provides another angle to evaluate vloggers and social media producers’ practices for building audiences.
Comparative Analysis of LLC Resonant and Quasi Resonant Converter for Photo Voltaic Emulator

This paper presents the modeling, analysis and control of different DC-DC converter topologies to emulate the photovoltaic (PV) system. A PV emulator is basically a DC-DC converter having same electrical characteristics that of solar PV panel. The emulator helps to achieve real characteristics of PV system in a better way in an environment where using actual PV systems can produce inconsistent results due to variation in weather conditions. The paper describes different types of DC-DC converters like buck, Resonant and Quasi Resonant Converter. The complete system is modelled in MATLAB ® Simulink SimPowerSystem software package. The Simulation results obtained from the MATLAB ® Simulink SimPowerSystem software package for different topologies under steady and dynamic conditions are analyzed and presented. An evaluation table is also presented at the end of the paper, presenting the effectiveness of each topology.
Design of a Low-Noise Low-Power Fourth Order Complementary Super Source Follower Filter for EEG Applications

Design of a filter for biomedical application is a challenging task owing to its low-power and low-noise values introduced at low frequencies. This paper describes the design of a Complementary Super Source Follower (CSSF-C) Low Pass Filter (LPF) circuit and the same has been compared with Complementary Source Follower (CSF-C) LPF targeted for the detection of EEG signals. Simulated results obtained in Cadence Analog Design Environment using CMOS 0.18 µm technology node shows the CSSF-C LPF circuit emulates gain of -528 mdB, bandwidth of 100 Hz, power consumption of 12.6 nW from 0.5 V supply voltage. Further, the proposed LPF circuit is capable of achieving Dynamic Range (DR) of 63 dB with an Input Referred Noise (IRN) of 36 µVrms. The proposed CSSF-C LPF is expected to improve the quality of acquired EEG signals.
A Digital Watermarking System for Video Authentication Using DMT

The paper presents multi-wavelet based on in-visible water marking. In the past, DMT technique has less copyright protection, content authentication and produce poor quality. The proposed method is solved referred problems. In this paper, firstly apply the multi wavelet to improve image resolution at LL sub band. Secondly, embedded the important data (watermark image) into host multimedia, and it can be used in digital right management, authentication and data hiding. The result shows that the watermark scheme has strong robustness, and can embed much more data. By using DMT to improve quality of watermarking system.
Artificial Neural Network (ANN) Modelling for Biogas Production in Pre-Commercialized Integrated Anaerobic-Aerobic Bioreactors (IAAB)

The use of integrated anaerobic-aerobic bioreactor (IAAB) to treat the Palm Oil Mill Effluent (POME) showed promising results, which successfully overcome the limitation of a large space that is needed in the conventional method. The understanding of synergism between anaerobic digestion and aerobic process is required to achieve maximum biogas production and COD removal. Hence, this work presents the use of artificial neural network (ANN) to predict the COD removal (%), purity of methane (%), and methane yield (LCH4/gCODremoved) of anaerobic digestion and COD removal (%), biochemical oxygen demand (BOD) removal (%), and total suspended solid (TSS) removal (%) of aerobic process in a pre-commercialized IAAB located at Negeri Sembilan, Malaysia. MATLAB R2019b was used to develop the two ANN models. Bayesian regularization backpropagation (BR) showed the best performance among the 12 training algorithms. The trained ANN models showed high accuracy (R2 > 0.997) and demonstrated good alignment with the industrial data obtained from the pre-commercialized IAAB over a 6-month period. The developed ANN model is subsequently used to create the optimal operating conditions which maximize the output parameters. The COD removal (%) was improved by 33.9% (from 68.7% to 92%), while the methane yield was improved by 13.4% (from 0.23 LCH4/gCODremoved to 0.26 LCH4/gCODremoved). Sensitivity analysis shows that COD inlet is the most influential input parameters that affect the methane yield, anaerobic COD, BOD and TSS removals, while for aerobic process, COD removal is most affected by mixed liquor suspended solids (MLSS). The trained ANN model can be utilized as a decision support system (DSS) for operators to predict the behavior of the IAAB system and solve the problems of instability and inconsistent biogas production in the anaerobic digestion process. This is of utmost importance for the successful commercialization of this IAAB technology. Additional input parameters such as the mixing time, reaction time, nutrients (ammonium nitrogen and total phosphorus) and concentration of microorganisms could be considered for the improvement of the ANN model.
AKER: An open-source security platform integrating IDS and SIEM functions with encrypted traffic analytic capability

ABSTRACT This research proposes Aker, an open-source security platform that integrates IDS and SIEM functions while supporting the automated investigation of threats hidden in encrypted traffic. Aker is based on open-source technologies, whose aim is to simplify and integrate network monitoring and security alerts management through coherent and interconnected dashboards. Aker enables the detection of malware concealed in encrypted traffic and uses a heuristic-based decision-tree model to classify these threats into different severity levels, based on seven specific criteria and indicators originating mainly from TLS handshake unencrypted metadata, and HTTP/ DNS contextual flows linked to the encrypted traffic. Our approach does not require deep packet inspection nor intensive traffic analysis, but rather relies on the analysis of a reduced set of network telemetry indicators and session descriptors. Aker permits to automate data collection and investigation while providing user-friendly dedicated dashboards to provide SOC analysts and threat intelligence experts with valuable decision support tools. We present the design and implementation aspects of the proposed platform along with some validation results.
Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

Simple Summary Aptamers represent an emerging technology that enables researchers to screen biological matrices such as blood and urine for thousands of different proteins at a rapid pace with high precision and accuracy. However, the sheer data volume generated by this high-capacity screening technique also creates a fundamental challenge towards efficiently analyzing these complex datasets and translating findings for the clinic. We address the new analytical considerations brought forth by aptamers, explore the necessary statistical analysis needed, and create a baseline to analyze these large-scale databases more comprehensively. In addition, we explore how aptamers can co-exist with current proteomic platforms to produce more robust findings in an evolving, multi-faceted approach towards the field. Unlocking the underlying signals masquerading behind these large datasets will ultimately empower clinicians and researchers to better understand diseases of interest and to curate more robust findings for patient care. Abstract The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
Design of a Multi-Epitope Vaccine against Tropheryma whipplei Using Immunoinformatics and Molecular Dynamics Simulation Techniques

Whipple’s disease is caused by T. whipplei, a Gram-positive pathogenic bacterium. It is considered a persistent infection affecting various organs, more likely to infect males. There is currently no licensed vaccination available for Whipple’s disease; thus, the development of a chimeric peptide-based vaccine against T. whipplei has the potential to be tremendously beneficial in preventing Whipple’s disease in the future. The present study aimed to apply modern computational approaches to generate a multi-epitope-based vaccine that expresses antigenic determinants prioritized from the core proteome of two T. whipplei whole proteomes. Using an integrated computational approach, four immunodominant epitopes were found from two extracellular proteins. Combined, these epitopes covered 89.03% of the global population. The shortlisted epitopes exhibited a strong binding affinity for the B- and T-cell reference set of alleles, high antigenicity score, nonallergenic nature, high solubility, nontoxicity, and excellent binders of DRB1*0101. Through the use of appropriate linkers and adjuvation with a suitable adjuvant molecule, the epitopes were designed into a chimeric vaccine. An adjuvant was linked to the connected epitopes to boost immunogenicity and efficiently engage both innate and adaptive immunity. The physiochemical properties of the vaccine were observed favorable, leading toward the 3D modeling of the construct. Furthermore, the vaccine’s binding confirmation to the TLR-4 critical innate immune receptor was also determined using molecular docking and molecular dynamics (MD) simulations, which shows that the vaccine has a strong binding affinity for TLR4 (−29.4452 kcal/mol in MM-GBSA and −42.3229 kcal/mol in MM-PBSA). Overall, the vaccine described here has a promising potential for eliciting protective and targeted immunogenicity, subject to further experimental testing.
Impact of Preterm Birth on Neurodevelopmental Disorders in South Korea: A Nationwide Population-Based Study

Neurodevelopmental disorder (NDD) in preterm infants has become of great interest. We aimed to investigate the impact of preterm birth on the proportion of NDD using nationwide data provided by the Korean National Health Insurance Service. We included 4894 extremely preterm or extremely low-birth-weight (EP/ELBW; <28 weeks of gestation or birth weight < 1000 g) infants, 70,583 other preterm or low-birth-weight (OP/LBW; 28–36 weeks of gestation or birth weight < 2500 g) infants, and 264,057 full-term infants born between 2008 and 2015. We observed their neurodevelopment until 6 years of age or until the year 2019, whichever occurred first. Diagnoses of NDDs were based on the World Health Organization’s International Classification of Diseases 10th revision. An association between preterm birth and NDD was assessed using a multivariable logistic regression model. There was a stepwise increase in the risk of overall NDD with increasing degree of prematurity, from OP/LBW (adjusted odds ratio 4.46; 95% confidence interval 4.34–4.58), to EP/ELBW (16.15; 15.21–17.15). The EP/ELBW group was strongly associated with developmental delay (21.47; 20.05–22.99), cerebral palsy (88.11; 79.89–97.19), and autism spectrum disorder (11.64; 10.37–13.06). Preterm birth considerably increased the risk of NDD by the degree of prematurity.
Field Testing Satellite-Derived Vegetation Health Indices for a Koala Habitat Managers Toolkit

A Central Queensland University (CQU) partnership with the Queensland Government National Park management agency has developed a koala (Phascolarctos cinereus) habitat managers’ toolkit for vegetation health assessment. Private and public landholders use the field-based toolkit to assess habitat suitability or monitor conservation outcomes for the koala—an iconic Australian arboreal herbivorous marsupial. The toolkit was upgraded recently with instructions to process European Space Agency (ESA) Sentinel-2 multispectral satellite-derived selected vegetation maps for areal vegetation health trend monitoring. A field campaign sought to validate the relatively coarse spatial resolution derived indices (photosynthetic health, leaf area index and leaf water content) to verify their suitability for the habitat management decision-support toolkit. Other user requirement-driven criteria for including remote sensing in the toolkit were imagery and associated processing software costs and ease of map production for habitat managers without cost-effective access to spatial science skills. Despite moderate-to-low field and image vegetation proxy correlations, discussing the results with stakeholders indicates that, at a landscape scale, the use of cost-free, suitable temporal resolution, 10-m spatial resolution imagery is satisfactory when aligned with the design outcomes of a habitat health toolkit.