Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sawtooh Chart for Compact Cumulative Data Visualization

We introduce the sawtooth chart, a compact representation of cumulative data that splits a continuous line chart into segments once a given value has been reached. All segments are then aligned over an horizontal layout and their slope indicates the time to reach the given value. This design originated as an alternative to the standard COVID-19 cumulative deaths charts to convey how much time it took for countries to reach 500 deaths. The main benefit of the sawtooth chart is that it provides a compact graphics and the slope encoding as segments provide an easier way to compare trends. We report on the technical details and feedback we collected during the design process of this technique which is applicable beyond COVID-19 data and available as an open-source project.
BACTERIAL DIVERSITY WITH PLANT GROWTH-PROMOTING POTENTIAL ISOLATED FROM Agave americana RHIZOSPHERE

ABSTRACT Objective: Study the diversity of cultivable rhizospheric bacteria associated to Agave americana, and select native strains with potential as plant growth-promoting bacteria (PGPB). Design/methodology/approach: The isolated bacteria were phenotypically characterized. The genetic diversity and identity of the strains were revealed by genomic fingerprints and by sequencing of 16S rRNA gene. Plant growth promoting ability and plant inoculation assays were evaluated to know the potential as PGPB. Results: A total of 235 strains were isolated from A. americana rhizosphere and were classified within of 10 different bacterial genera. Rhizobium, Pseudomonas, Acinetobacter had high potential as PGPB. Study limitations/implications Cultivable approach was used to study rhizobacteria. A metagenomic study could expand the knowledge about the structure and diversity of bacterial community associated to A. americana. Findings/conclusions Rhizosphere bacteria have potential use as biofertilizer for the cultivation and propagation of A. americana and other agave species.
CONCEPT AND RATIONALE OF EVOLUTIONARY BARLEY BREEDING UNDER CLIMATE CHANGE IN JORDAN

In this study, we reviewed the climate changing and the impact on crop production, and evolutionary breeding as adaptation key to crop resilience. The increasing climate change impact on the agriculture system has renewed interest to the broadest possible germplasm base for a resilient and sustainable food system. Heterogeneous populations developed through evolutionary plant breeding could be the ideal solution to reduce the effects of environment variability on cereal crop planted under low-input conditions.The study assessed the genetic basis of adaptation of a barley population which evolved in different rainfed locations and years in Jordan without any human selection as suggests model of plant breeding strategy to improve food security, nutrition, income and resilience of smallholder farmers in the dryland regions in the climate change scenarios. The study suggests that the breeder can shift the undesirable traits in evolutionary populations by practicing individual selection for specific adaptations, or individual selection from populations showing wide adaptations and high stability. On the other hand, the breeder can overcome the undesirable traits by keeping the highest variations within the population by seed sieving to remove small seed and plant mowing for tallest head.
Zooming into scientific illustrations with unlimited levels of details

For instance, if a biologist was to explain the function of an organ, she may like to draw its rough shape over the 3D body of an mannequin in order to indicate its location; then she would zoom in to show the detailed organization of tissues with their complex distributions of fibers, vessels, and folded membranes; and then zoom in again to reach the scale of cells; and finally she may zoom again into an individual cell to explain their internal complexity. Here, zooming in is to be interpreted as a combination of moving forwards across 3D matter, and progressively changing scale, to focus on smaller details.
GO-CRM.SB: A Goal-Oriented Methodology for CRM Projects in Small Businesses

The need to establish relations which lead to satisfaction for the customer and profitability for the company is felt not only in big companies, which have been conducting projects to this end for some time, but also in small businesses. Unlike a big company, a small business cannot undertake large-scale investment in projects of this kind. The main aim of this paper is to provide an overview of a methodology which, starting from the company’s objectives in the field of CRM (Customer Relations Management), facilitates a rapid and effective insertion of technology into small businesses. This methodology, called GO-CRM.SB, is part of a broader application framework for an innovative CRM tool, easy to implement and maintain in a small business.This article sets out the requirements and the fundamental steps in the application of the methodology, leaving the description of case studies on this methodology to further papers and the details of how it is used to the GO-CRM.SB model manual (forthcoming).
JPPE 24권4호_4차편집(0831).hwp

Background and objective: The purpose of study is to analyze the three-dimensional (3D) structure by creating a 3D model for green spaces in a park using unmanned aerial vehicle (UAV) images. Methods: After producing a digital surface model (DSM) and a digital terrain model (DTM) using UAV images taken in Mureung Park in Chuncheon-si, we generated a digital tree height model (DHM). In addition, we used the mean shift algorithm to test the classification accuracy, and obtain accurate tree height and volume measures through field survey. Results: Most of the tree species planted in Mureung Park were Pinus koraiensis, followed by Pinus densiflora, and Zelkova serrata, and most of the shrubs planted were Rhododendron yedoense, followed by Buxus microphylla, and Spiraea prunifolia. The average height of trees measured at the site was 7.8 m, and the average height estimated by the model was 7.5 m, showing a difference of about 0.3 m. As a result of the t-test, there was no significant difference between height values of the field survey data and the model. The estimated green coverage and volume of the study site using the UAV were 5,019 m2 and 14,897 m3, respectively, and the green coverage and volume measured through the field survey were 6,339 m2 and 17,167 m3. It was analyzed that the green coverage showed a difference of about 21% and the volume showed a difference of about 13%. Conclusion: The UAV equipped with RTK (Real-Time Kinematic) and GNSS (Global Navigation Satellite System) modules used in this study could collect information on tree height, green coverage, and volume with relatively high accuracy within a short period of time. This could serve as an alternative to overcome the limitations of time and cost in previous field surveys using remote sensing techniques.
The Effect of Adding Antimony Trioxide (Sb2O3) On A.C Electrical Properties of (PVA-PEG) Films

في هذا العمل، تم تحضير العديد من العينات عن طريق إضافة أكسيد الأنتيمون إلى بولي فينيل الكحول و بولي إيثلين كلايكول و دراسة تأثير إضافة أكسيد الأنتيمون كمادة مالئة مع نسب وزنية مختلفة على الخواص الكهربائية المتناوبة حيث تم تحضير العينات بشكل اغشية بواسطة تقنية الصب. أظهرت النتائج التجريبية للخصائص الكهربائية المتناوبة أن ثابت العزل الكهربائي يزداد مع زيادة تردد المجال الكهربائي المسلط وزيادة تركيز ثلاثي اوكسيد الأنتيمون, وايضا ان الفقدان العزلي يقل مع زيادة التردد ، في حين أنه يزداد مع زيادة تركيز ثلاثي اوكسيد الأنتيمون . وان الموصلية الكهربائي المتناوبة تزداد مع زيادة محتوى ثلاثي الأكسيد الأنتيمون والتردد للمركب
Barriers and Facilitators to Older Adults Participating in Fall-Prevention Strategies After Transitioning Home from Acute Hospitalization: A Scoping Review Clinical Interventions in Aging

Purpose: Approximately, 14% of older adults aged 65 years and over experience a fall within 1 month post-hospital discharge. Adequate self-management may minimize the impact of these falls; however, research is lacking on why some older adults engage in self-management to prevent falls while others do not. Methods: We conducted a scoping review to identify barriers and facilitators to older adults participating in fall-prevention strategies after transitioning home from acute hospitalization. Eligibility criteria were peer-reviewed journal articles published during 2009 – 2019 which were written in English and contained any of the following keywords or their synonyms: “ fall-prevention, ” “ older adults, ” “ post-discharge ” and “ transition care. ” We systematically and selectively summarized the ﬁ ndings of these articles using the Joanna Briggs Institute guidelines and the PRISMA-ScR reporting guidelines. Seven bibliographic databases were searched: PubMed/MEDLINE, ERIC, CINAHL, Cochrane Library, Scopus, PsycINFO, and Web of Science. We used the Capability-Opportunity-Motivation-Behavior (COM-B) model of health behavior change as a framework to guide the content, thematic analysis, and descriptive results. Results: Seventeen articles were ﬁ nally selected. The most frequently mentioned barriers and facilitators for each COM-B dimension differed. Motivation factors include such as older adults lacking inner drive and self-denial of being at risk for falls (barriers) and following-up with older adults and correcting inaccurate perceptions of falls and fall-prevention strategies (facilitators). Conclusion: This scoping review revealed gaps and future research areas in fall prevention relative to behavioral changes. These ﬁ ndings may enable tailoring feasible fall-prevention interventions for older adults after transitioning home from acute hospitalization.
Hypertension Risk Assessment from Photoplethysmographic Recordings Using Deep Learning Classiﬁcators

Background . Regular monitoring of blood pressure (BP) is essential to make an early detection of cardiovascular diseases caused by hypertension, a potentially deadly condition that do not present symptoms in its ﬁrst stages. This study aims to investigate whether deep learning techniques can assess risk levels of BP using only photoplethysmographic (PPG) recordings transformed into images by continuous wavelet transforms (CWT), without the need of electrocardiographic (ECG) recordings, as in many previous studies. Methods . A total of 508 recordings from 38 different patients were analyzed. Each recording contained simultaneous PPG and arterial blood pressure (ABP) signals. ABP recordings were used to deﬁne the category label of patients as normotensive (NT), prehipertensive (PH) or hipertnesive (HT). Each recording lasted 120 seconds and were cut into 5 seconds signal segments, thus providing 12.192 segments for analysis. GoogleNet and ResNet pretrained convolutional neural networks (CNN) were used for classiﬁcation. PPG signals were converted to RGB images using a scalogram, consisting of a time-frequency representation obtained by CWT that later was used as input images for the CNN. Results . Classiﬁcation using GoogleNet and ResNet networks was performed dividing the dataset in 80% for training and 20% for validation. The highest F1 score was achieved by discriminating NT patients from PH and HT, being 92 . 10% for GoogleNet and 93 . 91% for ResNet, respectively. Discrimination between NT and PH patients from HT patients achieved F1 scores of 84 . 16% for GoogleNet and 88 . 23% for ResNet, respectively. In addition, intra-patient classiﬁcation using different data segments for
An Electrophysiologic Computational Model of the Zebrafish Heart

In recent years there has been a growing interest in the zebrafish thanks to its physiological characteristics similar to humans ‘. The following work aims to create a full electrophysiological computational model of the zebrafish heart with the ultimate purpose of assessing the influence of pathologies and drug administration. The model considers a full body and the two-chambers of a 3 days post fertilization zebrafish. A four-variable phenomenological Action Potential model is used to describe the action potential of different regions of the heart. Tissue conductivity has been calibrated in order to reproduce the activation sequence described in literature. This model allows the evaluation of the main electrophysiological parameters in terms of activation sequence and timing, AP morphology (i.e., $APD_{90}$, AP amplitude, maximum and minimum AP derivatives), and ECG morphology (i.e., P-wave, T-wave, and QRS-complex amplitudes and durations).
Lantana camara: Phyto-constituents and Antimicrobial Activity Study

Background: The development of resistance by pathogenic organisms has caused renewed interest in medicinal plants as alternative antimicrobial agents in treating diseases and infections. Lantana camara is an evergreen shrub commonly found in the tropical and sub-tropical regions, different parts of the plant have been used traditionally in the treatment of fever, diarrheal, scabies, cold, skin infections etc. This study therefore, seeks to investigate the phytochemical composition and potential of Lantana camara extracts as an alternative antimicrobial agents. Methods: According to standard methods in this study, qualitative and quantitative phytochemical screening of Lantana camara leaves extracts was done. In vitro antimicrobial activity of the extract were evaluated against gram-positive and negative bacteria (Escherichia coli, Pseudomonas aeruginosa, Klebsiella oxytoca, Proteus vulgaris, Staphylococcus aureus, and Salmonella typhi) and fungal strains (Aspergillus flavus, Aspergillus niger, Aspegillus fumigatus, Fusarium poae, and Fusarium solani) using well-diffusion and mycelia inhibition method, respectively. Results: Phytochemical screening of the extracts revealed saponins, phenols, tannins, flavonoids, steroids, cardiac glycosides, and alkaloids, with tannins (70.454 ± 0.002 mg/g) found to be abundant in ethanolic extract. The extracts displayed moderate to high inhibitory activities on the tested strains with E.coli and A. flavus highly inhibited by the extracts. Conclusion: The present study clearly showed that the crude extracts are rich in diverse phytochemicals and are potential sources of antimicrobial principles that could be used in the management of diseases and infections.
Lattice Boltzmann simulation of particles agglomeration and rheology in a particulate flow

ABSTRACT The dynamics and rheology of particles in a Newtonian fluid subjected to shear are simulated using Lattice Boltzmann Method. A computationally-efficient Smoothed Profile Method is used to resolve fluid-solid interactions, and the Lennard-Jones inter-particle potential is implemented to account for inter-particle forces. The use of a bi-periodic computational domain with Lees-Edward boundary conditions allows simulation for systems consisting of a large number of particles under shear. The method is validated for single and dual particle problems and an analysis is performed for multi-particle problems under a range of shear rates and particle fractions. The introduction of particle-particle interactions, which are physically important in many engineering processes, is found to have a considerable impact on the dynamics, agglomeration and rheology. The total stress exhibits high unsteadiness primarily due to the solid component contribution, at higher particle fractions. The simulations underscore the complex interplay between shear, interparticle forces and agglomeration and the complex dependencies of the rheological properties. GRAPHICAL ABSTRACT
Improving Textual Emotion Recognition Based on Intra- and Inter-Class Variations

Textual Emotion Recognition (TER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications. Prior research has tackled the automatic classification of emotion expressions in text by maximising the probability of the correct emotion class using cross-entropy loss. However, this approach does not account for intra- and inter-class variations within and between emotion classes. To overcome this problem, we introduce a variant of triplet centre loss as an auxiliary task to emotion classification. This allows TER models to learn compact and discriminative features. Furthermore, we introduce a method for evaluating the impact of intra- and inter-class variations on each emotion class. Experiments performed on three datasets demonstrate the effectiveness of our method when applied to each emotion class in comparison to previous approaches. Finally, we present analyses that illustrate the benefits of our method in terms of improving the prediction scores as well as producing discriminative features.
Automated Detection of Pulse Using Continuous Invasive Arterial Blood Pressure in Patients During Cardiopulmonary Resuscitation

Continuous invasive arterial blood pressure (ABP) and its characteristic waveform features are widely used to monitor cardiovascular health. The invasive ABP signal has been proven useful to guide therapy during cardiopulmonary resuscitation (CPR) of patients in cardiac arrest. Automated algorithms to compute ABP parameters were not designed to work during CPR, so their performance in this scenario is unknown. The aim of this study was to develop automated algorithms to detect pulse and measure physiological ABP variables during CPR. A dataset of 122 segments of invasive ABP were extracted during chest compression pauses from 26 patients with regular ECG during and a total duration of 262 min. The ABP was denoised using a stationary wavelet decomposition and pulse peaks were detected in the first difference of the ABP by applying adaptive thresholding. The following parameters were computed: systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP) and heart rate (HR). The algorithm presented a median (IQR) Se/PPV/F1 of 97.6(17.5)/99.3(10.0)/97.2(10.1)% for diastolic peak detection, 4-points above the F1 obtained with Physionet's wabp algorithm. The absolute and relative errors were 0.62(1.40)mmHg and 1.22(1.62)%, 0.74(1.43)mmHg and 1.81(2.76)%, 1.13(1.67)mmHg and 4.68(4.86)%, 0.50(1.42)min-1 and 0.58(1.31)% for SBP, DBP, PP and HR, respectively.
Interactive Simulation of the ECG: Effects of Cell Types, Distributions, Shapes and Duration

The shape of the ECG depends on the lead positions but also on the distribution and dispersion of different cell types and their action potential (AP) durations and shapes. We present an interactive JavaScript program that allows fast simulations of the ECG by solving and displaying the dynamics of cardiac cells in tissue using a web browser. We use physiologically accurate ODE models of cardiac cells of different types including SA node, right and left atria, AV node, Purkinje, and right and left ventricular cells with dispersion that accounts for apex-to-base and epi-to-endo variations. The software allows for real-time variations for each cell type and their spatial range so as to identify how the shape of the ECG varies as a function of cell type, distribution, excitation duration and AP shape. The propagation of the wave is visualized in real time through all the regions as parameters are kept fixed or varied to modify ECG morphology. The code solves thousands of simulated cells in real time and is independent of operating system, so it can run on PCs, laptops, tablets and cell phones. This program can be used to teach students, fellows and the general public how and why lead positions and the different cell physiology in the heart affect the various features of the ECG.
Review of Methods to Reduce Energy Consumption in A Smart City Based On IoT and 5G Technology

The evolution of the technology 5G has influenced the Internet of the Things (IoT) according to the wireless communications of minor latency, and major speed compared to the existing technologies. Consequently, from the 21st century, 5G will act as the spinal column of the IoT and will level the way for the design and deployment of a Smart City, nevertheless, this progress has a negative point which is demonstrated in the energy consumption. The target of this investigation is to announce methods to reduce the consumption of energy in one Smart City based on the IoT and the technology 5G. The results obtained in this investigation allow determining that 5G and the IoT will have a major energy consumption with about 80 % of the entire potency, due to the transmission and reception of a big quantity of information in a Smart City. Likewise, this research, contributes to the knowledge, presenting a review of methods that allow minimizing energy consumption and projects that improve energy efficiency.
Aging Wireless Bandits: Regret Analysis and Order-Optimal Learning Algorithm

We consider a single-hop wireless network with sources transmitting time-sensitive information to the destination over multiple unreliable channels. Packets from each source are generated according to a stochastic process with known statistics and the state of each wireless channel (ON/OFF) varies according to a stochastic process with unknown statistics. The reliability of the wireless channels is to be learned through observation. At every time-slot, the learning algorithm selects a single pair (source, channel) and the selected source attempts to transmit its packet via the selected channel. The probability of a successful transmission to the destination depends on the reliability of the selected channel. The goal of the learning algorithm is to minimize the Age-of-Information (AoI) in the network over T time-slots. To analyze its performance, we introduce the notion of AoI-regret, which is the difference between the expected cumulative AoI of the learning algorithm under consideration and the expected cumulative AoI of a genie algorithm that knows the reliability of the channels a priori. The AoI-regret captures the penalty incurred by having to learn the statistics of the channels over the T time-slots. The results are two-fold: first, we consider learning algorithms that employ well-known solutions to the stochastic multi-armed bandit problem (such as ϵ-Greedy, Upper Confidence Bound, and Thompson Sampling) and show that their AoI-regret scales as Θ(log T); second, we develop a novel learning algorithm and show that it has O(1) regret. To the best of our knowledge, this is the first learning algorithm with bounded AoI-regret.
Implant-related artifacts around metallic and bio-integrative screws: a CT scan 3D Hounsfield unit assessment

Objective: To assess the degree of implant-related artifacts (IRA) around metallic and bio-integrative (BI) cannulated screws using Hounsfield units (HU) on computed tomography (CT). Our hypothesis was that BI implants would demonstrate significantly decreased IRA around the inserted screws. Methods: In this cadaveric CT imaging study, we used 2 below-knee specimens. Medial displacement calcaneal osteotomy was performed, and the specimens were fixed with either metallic or BI screws. HU values were measured over 4 different lines that crossed the osteotomy position. Results: The mean HU value was decreased in the BI implants compared to the metallic ones in 3 different positions: near the screw, directly over the screw, and inside the screw cannula. At the line placed 1 cm dorsal to the screw, the HU value for the metallic screw was lower than that for the BI screw. Conclusions: We found metallic implants to demonstrate significantly increased HU values in regions close to the implant and significantly decreased values 1 cm away from the implant, when compared to the BI screw. The decreased HU values 1 cm away from the implant could be due to a shielding effect of the surrounding bone, hindering the assessment of union and healing. BI implants represent an alternative to decrease these IRA effects. Level of Evidence III; Case-Control Study.