Miracle-dz/UniFAR-FTUs huggingface dataset:
Sliding-Mode Control of Bidirectional Flyback Converters with Bus Voltage Regulation for Battery Interface

Energy storage systems are essential for multiple applications like renewable energy systems, electric vehicles, microgrids, among others. Those systems are responsible of regulating the dc bus voltage using charging-discharging systems which are mainly formed by a power converter and a control system. This work focuses on the control system of a flyback converter. A detailed design procedure of an adaptive sliding-mode controller (SMC) and its parameters is presented. The proposed procedure was validated through simulations which allow to confirm its good performance in terms of global stability providing the desired dynamic of the dc bus voltage regulation.
Comparative Evaluations to Enhance Chemical and Microbial Quality of Salted Grey Mullet Fish

Salted fish preparations are popular and widely eaten across the world. Salting is a preservation strategy to enhance shelf life by decreasing water activity in fish muscles. Salted fish demand increases with less regard for preservation characteristics, and novel strategies are required for healthy production. Lysozyme, EDTA, their mixture, and black cumin oil nanoemulsion (BCN) were used for dipping treatments to make preservative films before salting. A medium-sized mullet were classified into six groups: fresh, commercial salted mullet, and four treatments. Stored salted mullet groups were evaluated for chemical composition changes, lipid profile changes, nitrogen profile changes, and microbial contamination. The results were reflected by nonsignificant changes in protein content (21.08 ± 1.05%) of the BCN compared to the fresh (22.41 ± 1.41%), with the lowest salt penetration to the flesh (11.11 ± 0.74%). The lowest value recorded was the BCN for the pH (5.53 ± 0.06) and water activity (0.447 ± 0.019). The changes in lipid profile compared to the fresh were very low, which does not happen in a commercial salted sample. The changes in total volatile and non-protein nitrogen represented very low for the BCN compared to the commercial salted one. Coliform, E. coli, Staphylococcus aureus, Salmonella, and Clostridium were not detected in the BCN salted mullet. The overall acceptability for the BCN mullet was the best in the test panel list evaluation, followed by the mixture treatment. This research indicated that the BCN treatment before mullet fish salting was optimal, with greater food safety properties and no consumer acceptance alterations.
Identification of Critical miRNAs as Novel Diagnostic Markers for Laryngeal Squamous Cell Carcinoma

The prognosis of laryngeal squamous cell carcinoma (LSCC) patients remains poor, and early diagnosis can distinctly improve the long-term survival of LSCC patients. MicroRNAs (miRs) are a group of endogenous, noncoding, 18-24 nucleotide length single-strand RNAs and have been demonstrated to regulate the expression of many genes, thus modulating various cellular biological processes. In this study, we aimed to identify critical diagnostic miRNAs based on two machine learning algorithms. The GSE133632 dataset was acquired from the Gene Expression Omnibus (GEO) datasets, comprising LSCC tissular samples (57 specimens) and matched neighboring healthy mucosa tissular samples (57 specimens). Differentially expressed miRNAs (DEMs) were screened between 57 LSCC specimens and 57 normal specimens. The LASSO regression model and SVM-RFE analysis were carried out for the identification of critical miRNAs. ROC assays were applied to evaluate discriminatory ability. We identified 32 DEMs between LSCC specimens and normal specimens. Two machine learning algorithms confirmed that hsa-miR-615-3p, hsa-miR-4652-5p, hsa-miR-450a-5p, hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-139-5p, and hsa-miR-424-5p were critical diagnostic factors. According to the ROC assays, seven miRNAs had an AUC value of >0.85 for LSCC. Taken together, our findings identified seven critical miRNAs in LSCC patients which can be used to diagnose LSCC patients with high sensitivity and specificity. These results must be verified by large-scale prospective studies.
Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed.
Compact MR Rotary Brake for Prosthetic Applications

Controllable stiffness can greatly improve the usefulness of a prosthetic foot and its users comfort. In this thesis the feasibility of using a magnetorheological rotary brake in conjunction with carbon fiber blades to provide variable stiffness in a prosthetic foot is investigated. For the brake to be effective in a prosthesis it needs to be compact. A known reference design, currently used in a prosthetic knee, is scaled down and a finite element analysis applied to investigate the effect chosen parameters have on the torque output. The size is investigated with regard to the radius of the brake because of limited space as well as weight restrictions. The minimum torque output needs to be high enough to provide a range for the stiffness to be considered variable and have an effect on the prosthesis behavior. The results show that a magnetorheological brake retains enough braking torque after being scaled down to be considered an option in a foot prosthesis.
The Transformation of Adolescent and Young Adult Oncological and Supportive Care in Canada: A Mixed Methods Study

Background: Due to ongoing disparity in the specialized care available to adolescents and young adults (AYAs) with cancer, this study aimed to understand the gaps and barriers to accessing care and preferences on types of solutions at a national Canadian level. Methods: A mixed-methods study involving an online survey and focus groups (FGs) was conducted among AYAs residing in different regions of Canada. Results: There were a total of 174 survey respondents, of whom the majority were between 30–39 years of age (n = 125, 71.8%). Of the 174 respondents, 36 (20.7%) participated in one of seven FGs. Triangulation of the results illustrated that AYAs are not appropriately informed about the long-term health risks of being treated for cancer and where/how to seek support. These results culminated into three themes: (1) the need for AYA relevant and timely information about health risks; by (a) producing health risk-related content with the AYA life stage in mind; (b) providing a guided “map” to help AYAs anticipate what they may experience, and (c) providing checklists to help AYAs navigate their experience; (2) need for tailored and timely supportive care including (a) establishing ongoing check-ins and (b) receiving navigation support, and (3) need for enhanced connections by creating (a) a space to gather, connect and seek mentorship and (b) a hub to access information. Conclusion: AYAs continue to lack sufficient support both during and following cancer and mechanisms are required to ensure longitudinal support is provided across jurisdictions and in all stages of the cancer journey.
Validación de la punción aspiración con aguja fina guiada por ecografía en el diagnóstico de cáncer de tiroides

introducción: la punción aspiración con aguja fina ha desempeñado un papel esencial en la evaluación del paciente con nódulo tiroideo, siendo un método rápido, mínimamente invasivo, con reinserción rápida a sus actividades. Objetivo: determinar la validez de la Punción Aspiración con Aguja Fina (PAAF) guiada por ecografía en el diagnóstico de cáncer de tiroides en pacientes del Hospital José Carrasco Arteaga durante el año 2017. Metodología: estudio de validación de pruebas diagnósticas que comparó la punción aspiración con aguja fina guiada por ecografía con la histopatología (gold estándar). Participaron 236 pacientes que cumplieron los criterios de inclusión. Para el análisis se aplicaron frecuencias y porcentajes, desviación estándar y media, según variables cuantitativas y cualitativas respectivamente, para la validación de la PAAF se realizaron pruebas de sensibilidad, especificidad, valor predictivo positivo y negativo. Se aplicaron los procesos bioéticos respectivos. Resultados: el 73.78% de pacientes tuvieron más de 48 años (media de 56,52, DS +/- 13,51). Según la clasificación de Bethesda la mayoría de los pacientes presentaron atipía con 27,1%, seguido de 26,3% de neoplasia folicular. Por histopatología el 53,8% fueron benignos y el 43,2% corresponde a carcinomas papilares. La sensibilidad fue de 73,39%; especificidad del 57,48%, índice de validez 64,83%; valor predictivo positivo 59,7%; valor predictivo negativo 71,57%; razón de verosimilitud positivo 1,73, razón de verosimilitud negativo 0,46; índice de Youden 0,31. Conclusiones: la PAAF tuvo una mayor sensibilidad que especificidad, sin embargo, no podría ser determinante en el momento del diagnóstico y toma de decisiones.
A Simulator and First Reinforcement Learning Results for Underwater Mapping

Underwater mapping with mobile robots has a wide range of applications, and good models are lacking for key parts of the problem, such as sensor behavior. The specific focus here is the huge environmental problem of underwater litter, in the context of the Horizon 2020 SeaClear project, where a team of robots is being developed to map and collect such litter. No reinforcement-learning solution to underwater mapping has been proposed thus far, even though the framework is well suited for robot control in unknown settings. As a key contribution, this paper therefore makes a first attempt to apply deep reinforcement learning (DRL) to this problem by exploiting two state-of-the-art algorithms and making a number of mapping-specific improvements. Since DRL often requires millions of samples to work, a fast simulator is required, and another key contribution is to develop such a simulator from scratch for mapping seafloor objects with an underwater vehicle possessing a sonar-like sensor. Extensive numerical experiments on a range of algorithm variants show that the best DRL method collects litter significantly faster than a baseline lawn mower trajectory.
The Design of Predictive Model for the Academic Performance of Students at University Based on Machine Learning

Students in South African Universities come from different socio-cultural backgrounds, countries and high schools. This suggests that these students have different experiences which impacts on their levels of grasping information in class as they potentially use different lenses on tuition. The current practice in Universities in contributing to the academic performance of students include the use of tutors, the use of mobile devices for first year students, use of student assistants and the use of different feedback measures. What is problematic about the current practice is that students are quitting university in high numbers. In this study, knowledge has been drawn from data through the use of machine learning algorithms. Bayesian networks, support vector machines (SVM) and decision trees algorithms were used individually in this work to construct predictive models for the academic performance of students. The best model was constructed using SVM and it gave a prediction of 72.87% and a prediction cost of 139. The model does predict the performance of students in advance of the year-end examinations outcome. The results suggest that South African Universities must recognize the diversity in student population and thus provide students with better support and equip them with the necessary knowledge that will enable them to tap into their full potential and thus enhance their skills.
Reproductive sterility in aquaculture: A review of induction methods and an emerging approach with application to Pacific Northwest finfish species

Aquaculture is the fastest-growing food-production sector and is striving to become a long-term sustainable approach to meet the rising global demand for seafood. During the expansion and advancement of aquaculture, minimizing ecological impacts should occur concomitantly with maximizing production. Farmed fish, often genetically distinct from their natural conspecifics, may pose significant risks of genetic contamination and ecological imbalance to wild populations if they escape from aquaculture confinement. Growing reproductively sterile fish is the most effective way to genetically contain farmed fish. Atlantic salmon ( Salmo salar ) escape events in the ‘ Pacific Northwest ’ region of the United States and Canada have raised alarms over potential ecological impacts and led to legislation in Washington State phasing out the culture of non-native finfish species. Farming sterile native species such as coho salmon ( Oncor-hynchus kisutch ) and sablefish ( Anoplopoma fimbria ) in the Pacific Northwest would ease public concerns and promote environmentally and economically sustainable aqua-culture. Sterile fish also can mitigate the challenge of precocious maturation, a prominent issue associated with culture of salmonids and many other species, to improve somatic growth, flesh quality and fish health and welfare. Here, we review methods having potential applications for producing sterile fish and introduce our novel immersion-based technology that temporarily silences the dead end ( dnd ) gene using Morpholino oligonucleotides to produce sterile coho salmon and sablefish for the first time. The successful induction of sterility in these two iconic Pacific Northwest species without introducing genetic modifications would promote the use of this immersion-based sterilization technology for more aquaculture finfish worldwide
Zero-augmented models for exploring the factors affecting the pass rate of 2016 grade 10 learners in Khomas region, Namibia

The poor performance of grade 10 learners has been a big concern over the last few years and in the effort to understand this phenomenon there has been efforts to present models that explain it. This study aimed at exploring the factors which influence Khomas Region grade 10 learners' pass rate using Generalized Linear Models (GLMs). The data used for this study was obtained from the Directorate of National Examination and Assessment for the year 2016, with permission from the Permanent Secretary of the Ministry of Education (DNEA). With the presence of excess zeros in the study data, six GLMs were explored (Poisson, Negative Binomial, Hurdle Poisson, Hurdle Negative Binomial, Zero Inflated Poisson and Zero- Inflated Negative Binomial) to assess their goodness of fit on modelling the zero-inflated DNEA count data. Afterwards, the better performing GLM was used in achieving the study aim. The Zero- Inflated Negative Binomial performed better based on its lowest Akaike Information Criterion (AIC) values among the six fitted GLMs. Results from the fitted Zero- Inflated Negative Binomial model revealed that the age of the learner, school location and the type of school (private/state) had significant differential in the pass rate of grade 10 learners, with p-values < 0.05 in the Zero- Inflated Negative Binomial model. Thus, it is recommended that for densely populated areas, emphasis should be put on building more schools in these areas so that classrooms are not overcrowded per subject. In addition, overaged learners should also be given extra assistance such as extra classes and extra motivation.
ATTITUDES AND DRIVING BEHAVIOR FACTORS ON THE TRAFFIC ACCIDENTS AMOUNG YOUNG MOTORCYCLIST IN SURABAYA CITY

: The majority of traffic accidents in Surabaya are dominated by motorcycles and drivers who had accidents mostly were teenagers. This study aims to examine the relationship between attitudes, driving behavior, and traffic accidents among young motorcyclist in Surabaya. The study used a primary dataset of 435 motorcyclists from 17 to 25 years of age. This study uses a Structural Equation Modeling (SEM) approach to determine the relationship between attitudes towards risky driving behavior, driving behavior, and traffic accidents. The driving behavior parameters used are divided into six parameters: traffic errors, speed violations, control errors, traffic violations, safety violations, and stunts. The results of this study indicate that all driving behavior parameters are significantly influenced by the driver's attitude towards risky driving behavior. Meanwhile, attitudes towards risky driving behavior have no direct or indirect influence on accidents. Then, the driving behavior parameters that have a significant effect on traffic accidents are traffic errors and traffic violations.
Mining communities in social network based on information diffusion

Based on the locality of information diffusion in social network, a new community detection algorithm DC_ID is proposed in this paper. Different from that of the traditional community discovery algorithms, the goal of the DC_ID algorithm is that the difference between the node's influence degree in its community and its influence degree in the whole network is small, which paves the way for further research on the influence maximization problem based on the detected community structure. Moreover, the algorithm can tackle the community detecting program in directed and weighted networks, which makes it different from other existing algorithms. The algorithm consists of two stages: partition and combination. During the first stage, the algorithm first estimates all nodes' influence in the network, then chooses the most influential non‐community node as the seed, and expands its influence along diffusion paths layer by layer. Finally, the local community with the node as its core is found. During the second stage, the closeness of two local communities will be evaluated by their ‘combination entropy’. When the combination entropy of any two communities is higher than some predefined threshold, they will be combined into one. To evaluate the quality of the detected community structure, two new measures, LEW and CRC, are introduced. Empirical studies on three real‐world social networks show that the algorithm outperforms the benchmark algorithm in runtime, LEW, and CRC. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Preventing Special Education Assignment for Students with Learning or Behavioral Difficulties: A Review of Programs

Abstract The number of students assigned to special education has increased in the past decades, in spite of efforts for more inclusion. For students with mild learning or behavioral difficulties, special education assignment might be prevented if appropriate support is provided in general education. In this study, research on programs that could reduce the number of students assigned to special education is reviewed systematically. The review focuses on students in elementary schools. In total, 12 experimental or quasi-experimental studies of nine programs were reviewed. Programs were categorized based on what they were designed to improve: academic achievement, behavior, or both, and the multi-tiered Response to Intervention (RTI) framework was used to describe the intensity of the programs. It was found that several programs did reduce the number of students assigned to special education, while others did not or yielded mixed results. Three common elements of programs deemed effective were identified: an emphasis on tutoring, professional development and parental involvement. Implications for research and practice are discussed.
Network intrusion detection system: A survey on artificial intelligence‐based techniques

High data rate requirements in recent years have resulted in the massive expansion of communication systems, network size and the amount of data generated and processed. This has eventually caused many threats to the communication networks as well due to a more frequent generation of security attacks that are either novel or the mutation of the existing attacks. To secure the networks against such threats, an intrusion detection system (IDS) is considered as one of the promising solutions. The main problem with the IDS is its increased false alarm rate (FAR) in detecting the zero‐day attacks. To improve the detection accuracy and minimizing the FAR, the researchers proposed IDS solutions using artificial intelligence (AI) approaches. In this research, we have systematically reviewed the recent AI‐based network IDS (NIDS) solutions proposed during the period 2016–2021 by the research community. We systematically analysed the proposed NIDS solutions based on their strengths, shortcomings, AI methodology adopted, datasets, and the evaluation metrics used for evaluation purposes. From the review, we observed that the hybrid approach is mostly adopted by the researchers to propose AI‐based NIDS solutions, with a trend shifting to deep learning‐based approaches over the last 2 years. Also, most of the proposed solutions are evaluated using a very old dataset with only a few studies opting for the latest datasets. Finally based on our observations, we highlighted the research challenges and the future research directions to help young researchers to contribute to this field.
The Study of Coherence-Induced Phenomena Using Double-Sided Feynman Diagrams

We present a method that utilizes double-sided Feynman diagrams and show it to be particularly useful for the study of coherence-induced phenomena, because the photon processes form an infinite power series. We systematically formulate the diagrammatic method and employ it to analyze electromagnetically induced transparency, interacting dark resonances, and a general N-level chain system. The method not only provides an alternate way to predict these phenomena, but also reveals the contributions from individual transition pathways.