Miracle-dz/UniFAR-FTUs huggingface dataset:
FluSa-Tweet: A Benchmark Dataset for Influenza Detection in Saudi Arabia

Automatic flu detection is an important task in specialized natural language processing and its applications. Most existing studies emphasized on English language for influenza detection. However, Arabic language lacks significant resources, such as labeled datasets and NLP tools to cope with the current technologies. To this end, this paper presents the first dataset to detect influenza in Arabic tweets, particularly in Saudi Arabia. The dataset is manually annotated and contains 9145 tweets, which are categorized into three classes: (i) Awareness, (ii) Infection, and (iii) Unrelated. To mitigate the imbalance issue in the original dataset, three data resampling techniques are applied: (i) undersampling, (ii) oversampling, and (iii) oversampling using SMOTE. Five machine learning algorithms, such as Naive Bayes, Logistic Regression, Linear Support Vector Classifier, Random Forest, XGBoost, and three transformer-based models, are trained and tested to evaluate the dataset. Accuracy, precision, recall, and F1-Score are used to assess the models’ performance. Finally, transformer-based model achieves the highest results (97 % F1- score). This dataset is publically available for future research to automatically identify influenza in Arab countries.
Mixed-criticality communication scheme for networked mobile robots

We present an adaptive mixed-criticality based algorithm for weight-based task scheduling and communication resource allocation in the context of a Cyber-Physical System (CPS). The weight-based algorithm is motivated by the continuous computation of the task’s criticality and updates the weight of the CPS subsystem to be used in the task scheduler and the cost function of the optimal resource allocation problem. To demonstrate the algorithm performance, we consider a set of robots driving on a grid and performing a set of tasks with a different mixed-criticality profile, controlled and connected via a wireless channel with limited communication resources.
' My Story Ain ’ t Got Nothin To Do With You ' or Does It ? : Black Female Faculty ’ s Critical Considerations of Mentoring White Female Students

Previous literature on mentoring, specifically that of cross-cultural mentoring, has provided some insight into the intricacy of race in mentoring. However, much of this literature has focused on the mentoring relationship of a White individual mentoring a person of color. This qualitative inquiry critically explores the experiences of six Black female faculty who have mentored White female students in higher education graduate programs, focusing specifically on how they enter into these cross-cultural mentoring relationships. Using Black feminist thought, our findings suggest that while individual Black faculty may have unique experiences entering into mentoring relationships with White female students, a Black feminist standpoint does exist. These faculty members entered into the relationships cautiously and with thought, responding emotionally to the idea of mentoring White students, and screening the students, before formalizing the relationship via a student-centered approach. The findings from this study serve as a starting point in which to better understand faculty of color’s experiences mentoring White students as well as provide implications for both faculty and students who may enter into such a relationship.
Spectrophotometric Determination of Sun Screen Potential of Selected Medicinal Plants

Ultraviolet (UV) radiation emitted by sun has found to be a major cause to damage or alter structure and function of human skin. Various sunscreen cosmetics have been employed to protect the skin from these damaging effects. Many plant materials have shown the potential as sunscreen agent to protect the skin. Previous research revealed the potential of plant products such as fixed oil, volatile oil and plant extracts could be used for their sunscreen potential. In the present study some of selected medicinal plants Azadirachta indica, Ocimum sanctum, Centella asiatica and Hibiscus rosa sinensis have been evaluated for their sunscreen potential by spectrophotometric method. The In vitro method has been used for the study was rapid, convenient, safe and reliable and could be useful in preliminary evaluation of sun protective potential. The result of the study indicated all hydroalcoholic extract have sun protective potential and can be used in cosmetic as sunscreen agent. The comparative result indicated that Azadirachta indica hydroalcoholic extract has higher sun protection efficiency and Hibiscus rosa sinensis has lowest photo protection efficiency, The initial result may be useful in selection the plant for sunscreen effect while developing a cosmetic formulation.
Beware my spines: a new spiny fern (Dennstaedtia, Dennstaedtiaceae) from central and western Amazonia

ABSTRACT A new spiny fern belonging to the genus Dennstaedtia is described from Brazilian and Peruvian Amazonia. Dennstaedtia aculeata (sp. nov.) is the third spiny species known for the genus, and the first in South America. It is compared with another Neotropical spiny Dennstaedtia, D. spinosa. We also present images, illustrations and a distribution map of the specimens, and discuss the habitat preference of the species towards nutrient-richer soils and spinescence in the family.
Joint Autoencoder-Classifier Model for Malfunction Identification and Classification on Marine Diesel Engine Diagnostics Data

There has been an increasing demand on marine transportation and traveling, since the voyage of the ships are more economical and efficient than air or land-based alternatives. The propulsion of a ship is provided by a main engine system which includes the shaft, the propellers, and other auxiliary equipment. Marine diesel engine is a complex structure that the faults within these machines can cause malfunction of the whole system, which in turn inhibits the ship’s mission. It is crucial to monitor the engine and other auxiliary systems during the operation and infer their condition from their diagnostic data. In this study, we analyze monitoring data of a crude oil tanker for different ship loads and conditions. Our primary analysis includes main engine fault detection and classification for which we propose an end-to-end joint autoencoder-classifier model that contains a convolutional autoencoder, and a long-short term memory regressor connected to the latent space. Genetic algorithms optimized models gave us 93.61% accuracy for fault classification task. Further investigation on feature’s contributions to the model, we increased the accuracy up to 96%. One concern about marine transportation is the pollution of the air with greenhouse effect gases. In this study, we have developed NOx and SOx emission estimators for different faults and working conditions. Leveraging ship load, working conditions and engine faults in the models helped us to achieve 50% better estimation performance. Although there are other studies regarding gases emissions in the literature, this is the first study that took engine faults into account. We believe that the joint autoencoder-classifier model will be useful for other time series estimation task on other domains, especially where the operating condition plays a role in the process.
Comparative Performance Analysis of Scalp EEG and Ear EEG based P300 Ambulatory Brain-Computer Interfaces using Riemannian Geometry and Convolutional Neural Networks

Brain-Computer Interfaces (BCI) provide the users to communicate with computers via brain signals. Significant research within the BCI is devoted to ElectroEncephaloGraphy (EEG), which picks, on the scalp, immensely frail electrical currents delineating brain activity. This paper presents a new ambulatory classification method for EEG Event Related Poten-tials (ERP) for a Practical Brain Computer Interface (BCI). To be more specific, this paper focuses on enhancing the performance of the ERP classification using Ear EEG along with scalp EEG during walking at 1.6m/s. We demonstrate the signal quality of Ear EEG for targets and non-targets. Through a novel estimation of Covariance matrices, this work extends the use of Riemannian Geometry (RG). In addition, the utility of Ear EEG has been justified by the 5% improvement in ERP detection performance after a novel fusion of Riemannian Geometry attributes from Ear EEG and scalp EEG. Further, we also proposed a fusion of feature attributes of both scalp and Ear EEG obtained from the fully connected layer of trained EEGNet CNN model with autoencoders passed through XGBoost. This method improved the state of the art by 10%. The proposed methods serve as novel adaptations of RG and CNN methods for mobile EEG in a practical BCI setup. The proposed method was also validated on the track 5 of the International BCI competition and achieved third position in the challenge.
Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.
Deep-Sea Sediments from the Southern Gulf of Mexico Harbor a Wide Diversity of PKS I Genes

The excessive use of antibiotics has triggered the appearance of new resistant strains, which is why great interest has been taken in the search for new bioactive compounds capable of overcoming this emergency in recent years. Massive sequencing tools have enabled the detection of new microorganisms that cannot be cultured in a laboratory, thus opening the door to the search for new biosynthetic genes. The great variety in oceanic environments in terms of pressure, salinity, temperature, and nutrients enables marine microorganisms to develop unique biochemical and physiological properties for their survival, enhancing the production of secondary metabolites that can vary from those produced by terrestrial microorganisms. We performed a search for type I PKS genes in metagenomes obtained from the marine sediments of the deep waters of the Gulf of Mexico using Hidden Markov Models. More than 2000 candidate genes were detected in the metagenomes that code for type I PKS domains, while biosynthetic pathways that may code for other secondary metabolites were also detected. Our research demonstrates the great potential use of the marine sediments of the Gulf of Mexico for identifying genes that code for new secondary metabolites.
Performance Analysis of Vertical Underwater Visible Light Communication System with Cross-QAM

In this paper, we consider a vertical Underwater Wireless Visible Light Communication (UWVLC) system, where the source is located at the surface of the sea. The distance between both the source and the destination is divided in certain intervals or layers. We assume turbulence in the underwater channel and the intensity of the turbulence, referred in term of scintillation index, varies between consecutive layers. Further, the turbulence based fading channel is modelled by log-normal distribution and the statistics of the channel vary from one layer to another. Thus, we assume cascaded channel with independent but non-identically distributed log-normal distributions. The variance of the channel in each layer is a function of scintillation index of the layer. We assume 32-cross quadrature amplitude modulation (XQAM) scheme and derive closed form expression of Average Symbol Error Probability (ASEP). We analyze the ASEP performance of the system for different layers with different intensities of turbulence. As the number of layers or depth increases, the performance degrades. We also present simulation results of ASEP and compare them with their analytical counterparts. A close matching between both validates our analytical approach. Furthermore, we present the performance with 32-rectangular quadrature amplitude modulation (RQAM) scheme with 16 × 2 and 8 × 4 constellations. We observe that the XQAM outperforms the RQAM.
CNN Based Hybrid Precoding for MmWave MIMO Systems With Adaptive Switching Module and Phase Modulation Array

Hybrid precoding is considered as a promising candidate to balance between the sum rate and the power and cost in the mmWave MIMO systems. However, it still suffers from huge power consumption due to considerable radio frequency links and phase shifters. In this study, an energy-efficient adaptive switching module (ASM) and phase modulation array (PMA) assisted hybrid precoding scheme is proposed, in which ASM is introduced to enhance the energy efficiency while PMA is employed to implement analog precoding to reduce power consumption. The ASM-PMA hybrid precoder design problem related to three matrix variables is formulated. Besides, the joint ASM-PMA hybrid precoder/combiner design at both transmitter and receiver is also investigated. Owing to the tough task of joint optimization of matrix variables, we can divide it into two sub-problems. Correspondingly, alternating optimization hybrid precoding (AOHP) algorithm is suggested to solve the PMA. Then, matching theory and convolutional neural network (CNN) based algorithms are respectively proposed to seek the optimal ASM. For the CNN based scheme, relaxed adaptive switching CNN (RAS-CNN) is designed to solve the binary integer problem in the offline train stage, while in the online deployment stage, ideal adaptive switching CNN (IAS-CNN) is adopted. Furthermore, CNN accepts estimated channel as input to yield robust precoding performance and lower computational complexity. Simulation results verify that, the proposed hybrid precoding can outperform other schemes in energy efficiency with satisfactory spectral efficiency.
Prenatal Exposure to Organophosphorus Pesticides and Preschool ADHD in the Norwegian Mother, Father and Child Cohort Study

Prenatal organophosphorus pesticide (OPP) exposure has been associated with child attention-deficit/hyperactivity disorder (ADHD) in agricultural communities and those that are exposed to residentially applied insecticides. To examine this association in populations that are exposed primarily through diet, we estimate the associations between prenatal OPP exposure and preschool ADHD in the Norwegian Mother, Father and Child Cohort Study (MoBa), and describe modification by paraoxonase 1 (PON1) gene variants. We used participants from the MoBa Preschool ADHD Sub-study (n = 259 cases) and a random sample of MoBa sub-cohort participants (n = 547) with birth years from 2004 to 2008. Prenatal urinary dialkylphosphate (DAP) metabolites (total diethylphosphate [∑DEP] and total dimethylphosphate [∑DMP]) were measured by an ultra-performance liquid chromatography-time-of-flight system and summed by molar concentration. Maternal DNA was genotyped for coding variants of PON1 (Q192R and L55M). We used a multivariable logistic regression to calculate the odds ratios (OR) and 95% confidence intervals, adjusted for maternal education, parity, income dependency, age, marital status, ADHD-like symptoms, pesticide use, produce consumption, and season. We found no associations between DAP metabolite concentrations and preschool ADHD. The adjusted ORs for exposure quartiles 2–4 relative to 1 were slightly inverse. No monotonic trends were observed, and the estimates lacked precision, likely due to the small sample size and variation in the population. We found no evidence of modification by PON1 SNP variation or child sex. Maternal urinary DAP concentrations were not associated with preschool ADHD.
Comparative Analysis of TCP Congestion Control Methods

TCP Tahoe, TCP Reno, TCP New Reno, and TCP Vegas were among the congestion-control strategies tested in this paper. Several metrics, such as throughput, latency, and packet loss rate, are used to draw comparisons. Using the NS2 simulator, two alternative situations were created: one with a single TCP flow and the other with two. According to the performance investigation, TCP Vegas outperforms other protocols because it detects and monitors congestion early in the process. Furthermore, if there are two TCP flows, one of them will provide the other a greater chance of performing if packet transmission slows down. When Vegas senses congestion, for example, it decreases the quantity of outgoing packets, allowing Tahoe to take use of the network bandwidth. Tahoe will thus have a better throughput.
Modelling the Condensation Process of Low-Pressure Refrigerants in Mini-Channels

The measure of the energy efficiency of the non-adiabatic two-phase condensation process of refrigerants in mini-channels is both the value of the heat transfer coefficient α and the flow resistance expressing the external energy input required to realize the flow. The modelling of this very complex process is effective if the condensation mechanism in mini-channels is correctly identified. It has been proven that the effects of changes in the condensation mechanism are the different structures of the two-phase flow resulting from process interactions both in the channel cross-section and along the flow path. The research aimed to connect the value of the heat transfer coefficient with the flow structures occurring during condensation. Thermal and visualization studies of the condensation process of low-pressure refrigerants were carried out: Novec649, HFE7100 and HFE7000 in tubular mini-channels with diameters dh = 0.5; 0.8; 1.2; 2.0 mm. Based on visualization studies, flow structures were proposed to be divided into 3 main groups: dispersive, stratified and intermittent. Based on this, a computational correlation was derived for determining the heat transfer coefficient and frictional resistance depending on the type of flow structure. The research shows that the highest values of the heat transfer coefficient occur during the mist flow and the lowest during the bubble flow.
Biotechnological and Technical Challenges Related to Cultured Meat Production

The constant growth of the population has pushed researchers to find novel protein sources. A possible solution to this problem has been found in cellular agriculture, specifically in the production of cultured meat. In the following review, the key steps for the production of in vitro meat are identified, as well as the most important challenges. The main biological and technical approaches are taken into account and discussed, such as the choice of animal, animal-free alternatives to fetal bovine serum (FBS), cell biomaterial interactions, and the implementation of scalable and sustainable biofabrication and culturing systems. In the light of the findings, as promising as cultured meat production is, most of the discussed challenges are in an initial stage. Hence, research must overcome these challenges to ensure efficient large-scale production.
Metaphors in Jason Mraz's Song Lyrics (An Ecolinguistic Perspective)

This research was intended to analyze Metaphor in Jason Mraz’s song lyrics based on an Ecolinguistic perspective by Kovecess theory. The objective of this research was “To describe how metaphors delineation realized in Jason Mraz’s song lyrics based on the common source domain and the common target domain”. This research used a descriptive qualitative method. The researcher used the metaphor theory by Kovecess to analyze the data. The source of the data in this research was taken from Jason Mraz’s song lyrics such as The World as I See it, Back to the Earth, and Wise Woman. The data was collected through downloading, reading, classifying, coding, describing, and interpreting the data to get a complete conclusion of the Metaphor in Jason Mraz’s song lyrics based on an Ecolinguistic perspective. The researcher analyzed lyrics that show metaphor based on the common source domain and the common target domain in the lyrics. The result showed that the metaphor delineations were revealed from the analysis process by using the mapping common source domain and common target domain by Kovecess. The researcher found 19 Metaphors include the common source domain and the target domain. It showed the condition of the world that has a remarkable place, the beauty of ecology, our role as a part of nature. The three songs also convey an implicit message about how protecting and loving the environment create harmony and live peacefully because humans and nature have an interrelation. Keywords: metaphor, ecolinguistics, kovecses, sog lyrics, source domain and target domain
Construction of a Repeatable Framework for Prostate Cancer Lesion Binary Semantic Segmentation using Convolutional Neural Networks

—Prostate cancer is the 3 rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con- volutional neural network has been used in multiple studies to objectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and validate the results. In this study, we executed a repeatable framework for segmenting prostate cancer lesions using annotated apparent diffusion coefficient maps from the QIN-PROSTATE-Repeatability dataset — a publicly available dataset that includes multiparametric MRI images of 15 patients that are confirmed or suspected of prostate cancer with two studies each. We used a main architecture of U-Net with batch normalization tested with different encoders, varying data image augmentation combinations, and hyperparameters adopted from various published frameworks to validate which combination of parameters work best for this dataset. The best performing framework was able to achieve a Dice score of 0.47 (0.44-0.49) which is comparable to previously published studies. The results from this study can be objectively compared and improved with further studies whereas this was previously not possible.