Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Haemolysin Production and Complement Levels in Indigenous Rabbits (Oryclolagus cunicali) and Other Animal Species

The complement system is an essential part of the immune response, defending against infections and contributing to inflammation. This studyfocuses on haemolysin production and complement levels in local rabbits, using subcutaneous and intravenous inoculation. It also evaluatedcomplement levels in various animal species and healthy humans for comparison. Serum samples were collected, and the complement fixationtest was performed using sheep red blood cells as markers. The findings revealed changes in red blood cell lysis with different haemolysin dilutions over time. The study also provides information on complement minimum haemolytic dose concentrations in various animal species. A graph illustrates the antibody levels in response to different inoculation routes. This research enhances our understanding of haemolysin and complement levels, emphasizing the need for standardized protocols and larger population studies to explore clinical implications to benefit immunological studies.
Efficacy of Different Root Canal Irrigating Solutions in Removing Smear Layer: A Scanning Electron Microscopic Study

Background The aim of the present investigation was to evaluate and compare the efficacy of frequently used chemical agents in terms of their capacity to eliminate the smear layer after instrumentation, as observed via scanning electron microscopy (SEM). Materials and methods Sixty extracted single-rooted mandibular premolar teeth, each with roots 15 mm in length, were used in this study. The teeth were divided into one control group and four study groups, each containing 12 teeth. In Control Group 0, teeth were irrigated with 3 ml of saline only. In Group 1, teeth were irrigated initially with 3% sodium hypochlorite (NaOCl) and then given a final rinse with 3 ml of 17% ethylenediaminetetraacetic acid (EDTA) for one minute. In Group 2, teeth were irrigated with 3% NaOCl and given a final rinse with 3 ml of a mixture of tetracycline, acid, and detergent (MTAD, BIOPURE) for one minute. In Group 3, teeth were irrigated with saline and given a final rinse with 3 ml of 17% EDTA for one minute. In Group 4, teeth were irrigated with saline and given a final rinse with 3 ml of MTAD for one minute. One-half of each tooth was chosen and prepared for scanning electron microscopic (SEM) examination at the cervical, middle, and apical thirds. These were observed at magnifications of up to 1,000 times to check for the presence or absence of a smear layer. The data were analyzed using the Kruskal-Wallis test and post-hoc Dunn's test. Results All of the root canal irrigation protocols exhibited superior efficacy compared to the control group in the elimination of the smear layer. Group 2 (3% NaOCl with MTAD) showed the lowest mean scores, compared to all the groups, followed by Group 1 (3% NaOCl with 17% EDTA). MTAD was more effective than EDTA. The smear layer was effectively removed from the apical third, followed by the middle and coronal thirds of the root. Conclusion Initial irrigation with 3% NaOCl and one-minute final irrigation with 3 ml MTAD was the most effective root irrigant, and particularly indicated in teeth with infection of the apical third.
Comparing the Accuracy of Classical and Machine Learning Methods in Time Series Forecasting: A Case Study of USA Inflation

This paper presents a comparison of statistical classical methods and machine learning algorithms for time series forecasting notably the Exponential Smoothing, hybrid ARIMA-GARCH model, K-Nearest Neighbors (KNN), Prophet, and Long-Short Term Memory (LSTM). The data set used in this study is related to US inflation and covers the period from 1965 to 2021. The performance of the models was evaluated using different metrics especially Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (Median AE), and Root Mean Squared Error (RMSE). The results of the numerical comparison show that the best performance was achieved by Exponential Smoothing, followed closely by KNN. The results indicate that these two models are well-suited for forecasting inflation in the US. ARIMA-GARCH, LSTM, and Prophet performed relatively poorly in comparison. Overall, the findings of this study can be useful for practitioners in choosing the most suitable method for forecasting inflation in the US in the short-term period.
An Assessment of Deep Learning Versus Massively Parallel, Non-Linear Methods for Highly-Efficient MIMO Detection

Multiple-user, multiple-input, multiple-output (MU-MIMO) systems supporting a large number of concurrent streams have the potential to substantially improve the connectivity and throughput of future wireless communication systems. Towards this goal, deep learning (DL)-based techniques have recently been proposed for MIMO signal detection. Good performance results have been reported when compared to conventional detection methods, but it is unclear how they measure against state-of-the-art detection techniques. In this work, for the first time, we perform a critical evaluation of DetNet, MMNet, GEPNet, and RE-MIMO, four prominent model-based DL techniques based on different working principles, and assess their reliability, complexity, and robustness against the practical Massively Parallel Non-Linear processing (MPNL) detection approach. The results show that the model-based DL approaches offer promising results but have difficulty adapting to channel models that differ from those on which they were trained. They also exhibit lower reliability and higher complexity than MPNL, even without considering the training stage. We find that, at present, the human-designed MPNL outperforms the DL-based detection methods in virtually all the metrics. Nevertheless, DL-based solutions are rapidly advancing, and further research intended to address their current shortcomings may one day offer advantages over human-designed detection methods.
The Health Impact of mHealth Interventions in India: Systematic Review and Meta-Analysis

Background Considerable use of mobile health (mHealth) interventions has been seen, and these interventions have beneficial effects on health and health service delivery processes, especially in resource-limited settings. Various functionalities of mobile phones offer a range of opportunities for mHealth interventions. Objective This review aims to assess the health impact of mHealth interventions in India. Methods This systematic review and meta-analysis was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies conducted in India, and published between April 1, 2011, and March 31, 2021, were considered. A literature search was conducted using a combination of MeSH (Medical Subject Headings) terms in different databases to identify peer-reviewed publications. Thirteen out of 1350 articles were included for the final review. Risk of bias was assessed using the Risk of Bias 2 tool for RCTs and Risk Of Bias In Non-randomised Studies - of Interventions tool (for nonrandomized trials), and a meta-analysis was performed using RevMan for 3 comparable studies on maternal, neonatal, and child health. Results The meta-analysis showed improved usage of maternal and child health services including iron–folic acid supplementation (odds ratio [OR] 14.30, 95% CI 6.65-30.75), administration of both doses of the tetanus toxoid (OR 2.47, 95% CI 0.22-27.37), and attending 4 or more antenatal check-ups (OR 1.82, 95% CI 0.65-5.09). Meta-analysis for studies concerning economic evaluation and chronic diseases could not be performed due to heterogeneity. However, a positive economic impact was observed from a societal perspective (ReMiND [reducing maternal and newborn deaths] and ImTeCHO [Innovative Mobile Technology for Community Health Operation] interventions), and chronic disease interventions showed a positive impact on clinical outcomes, patient and provider satisfaction, app usage, and improvement in health behaviors. Conclusions This review provides a comprehensive overview of mHealth technology in all health sectors in India, analyzing both health and health care usage indicators for interventions focused on maternal and child health and chronic diseases. Trial Registration PROSPERO 2021 CRD42021235315; https://tinyurl.com/yh4tp2j7
Vortical Structures Promote Atheroprotective Wall Shear Stress Distributions in a Carotid Artery Bifurcation Model

Carotid artery diseases, such as atherosclerosis, are a major cause of death in the United States. Wall shear stresses are known to prompt plaque formation, but there is limited understanding of the complex flow structures underlying these stresses and how they differ in a pre-disposed high-risk patient cohort. A ‘healthy’ and a novel ‘pre-disposed’ carotid artery bifurcation model was determined based on patient-averaged clinical data, where the ‘pre-disposed’ model represents a pathological anatomy. Computational fluid dynamic simulations were performed using a physiological flow based on healthy human subjects. A main hairpin vortical structure in the internal carotid artery sinus was observed, which locally increased instantaneous wall shear stress. In the pre-disposed geometry, this vortical structure starts at an earlier instance in the cardiac flow cycle and persists over a much shorter period, where the second half of the cardiac cycle is dominated by perturbed secondary flow structures and vortices. This coincides with weaker favorable axial pressure gradient peaks over the sinus for the ‘pre-disposed’ geometry. The findings reveal a strong correlation between vortical structures and wall shear stress and imply that an intact internal carotid artery sinus hairpin vortical structure has a physiologically beneficial role by increasing local wall shear stresses. The deterioration of this beneficial vortical structure is expected to play a significant role in atherosclerotic plaque formation.
The effects of corridor control systems upon traffic flow

The paper analyzes the effects of corridor control systems on traffic flow by recording and evaluating traffic data along a series of typical corridor-controlled segments. Based upon a comparison with segments without corridor control systems and with the help of before and after statements with respect to traffic flow, the effects on freeway performance can be provided. A case study of traffic analysis of a freeway-segment in the vicinity of Munich, Germany, is reported. Along this corridor there have been detected remarkable effects which can be generalized forming a promising basis for comprehensive investigations
Eco-Friendly Coal Gangue and/or Metakaolin-Based Lightweight Geopolymer with the Addition of Waste Glass

Massive amounts of deposited coal gangue derived from the mining industry constitute a crucial problem that must be solved. On the other hand, common knowledge about the recycling of glass products and the reuse of waste glass is still insufficient, which in turn causes economic and environmental problems. Therefore, this work investigated lightweight geopolymer foams manufactured based on coal gangue, metakaolin, and a mix of them to evaluate the influence of such waste on the geopolymer matrix. In addition, the effect of 20% (wt.) of waste glass on the foams was determined. Mineralogical and chemical composition, thermal behaviour, thermal conductivity, compressive strength, morphology, and density of foams were investigated. Furthermore, the structure of the geopolymers was examined in detail, including pore and structure thickness, homogeneity, degree of anisotropy, porosity with division for closed and open pores, as well as distribution of additives and pores using micro-computed tomography (microCT). The results show that the incorporation of waste glass increased compressive strength by approximately 54% and 9% in the case of coal-gangue-based and metakaolin-based samples, respectively. The porosity of samples ranged from 67.3% to 58.7%, in which closed pores constituted 0.3–1.8%. Samples had homogeneous distributions of pores and additions. Furthermore, the thermal conductivity ranged from 0.080 W/(m·K) to 0.117 W/(m·K), whereas the degree of anisotropy was 0.126–0.187, indicating that the structure of foams was approximate to isotropic.
Shaping responsible consumption patterns

The article provides analysis of sustainable consumption concepts in order to identify the existing and emerging patterns of responsible consumption within the framework of the Sustainable Development Goals. Based on the analysis of scientific publications, patterns of responsible consumption were listed and then were used for an online survey of 600 young active consumers about their understanding of and attitude to responsible consumption. The main driver of responsible consumption is saving, limiting unnecessary spending. Among the forms of responsible consumption, those associated with saving of resources and the limitation of the number of purchased goods also prevail. Consumers are not ready to pay more for responsible consumption ‒ the increase in costs is the main barrier. Consumers are willing to exercise responsible consumption if it helps reduce costs by consuming fewer resources and goods and make purchases at an affordable price.
Initial operation results of the HellENIc GeoMagnetic Array (ENIGMA), a new magnetometer array in South-Eastern Europe

National Observatory of Athens (NOA) currently operates ENIGMA (HellENIc GeoMagnetic Array), an array of 4 ground-based magnetometer stations in the area of south-eastern Europe (central and southern Greece). The current stations are latitudinally equispaced between 30◦ and 33◦ corrected geomagnetic latitude. In the near future another station will be installed in Macedonia or Thrace, and there are plans for the installation of an additional station in Crete by the end of 2010. Magnetic field measurements are essential for correlative satellite and ground-based studies of various magnetospheric phenomena (Schwingenschuh et al., 1996). The ENIGMA data combined with the magnetometer measurements performed onboard the Cluster satellites provide the unique opportunity to study the following scientific objectives (Schwingenschuh et al., 2000): • Determine how Pi 2s are produced by substorms • Determine how PC 3-4 magnetic pulsation energy enters the magnetosphere and propagates along magnetic field lines to low latitudes
Human Error Verification during ELF stimuli: Retinal Receptivity of ELF-Inducing Phosphenes

Needless to say, considerations for details in the retinal receptivity of the extremely low frequency (ELF)-inducing blindsight-like phosphenes are of great importance. In this paper, a further detailed validation of human reliabilities during the ELF stimuli was carried out. In the results of our analysis using signal detection theory, we found that the supraliminal effect had a certain amount of persistence for the flicker in the central part of the visual fields. Our results definitely revealed precious findings about an actual control of retinal activities with blindsight-like phosphenes induced by ELF stimuli, which may be able to provide preventive solutions for serious artificial mistakes and/or human errors.
Using Bull and Bear Index of Deep Learning to Improve the Indicator Model on Extremely 
Short-term Futures Trading

Abstract Day traders, who trade over time horizons of one day or less, account for around 30% of the total transaction volume at present. Most day traders’ trading strategies are based on their own experiences or news headlines. They may also rely on technical indicators, such as the Relative Strength Index (RSI), to predict short-term trading opportunities and stock index turning points for making selling and buying decisions with respect to stock index futures. This study determined exact RSI indicators, which enhanced the accuracy of short-term stock index prediction. We then tested the proposed model’s performance during an unprecedented crisis such as COVID-19. We used artificial intelligence techniques, such as the SMO algorithm, to evaluate the performance of the proposed model and apply empirical methods on short-term stock index futures datasets to explore the impact of different RSI indicators on the turning points of the stock index futures. The results show that RSI 20 based on regular and COVID-19 periods can enable day traders to achieve higher profits compared to the RSI 30 index. Keywords: Day traders, Relative strength index, Short-term investment, Artificial intelligence, COVID-19.
PREDICTION OF AUTHORS OF THE TEXTS WRITTEN IN AZERBAIJANI BY THE NEAREST NEIGHBOR-BASED ALGORITHM

The article analyzes the use of the nearest neighbor algorithm among the formal methods to identify particular authors of texts written in the Azerbaijani language and considers its application to the issue of recognizing specific authors. In the work under review, the roots of the words included in the texts of different authors are found, and the vectors according to the frequency of processing of these words are constructed according to a certain rule, and the nearest neighbor method is applied to them by treating these vectors as points in the Euclidean space.
A 144-Element Beam-Steerable Source Array With 9.1-dBm Radiated Power and 30.8-dBm Lensless EIPR at 675 GHz

This article presents a large-scale coherent terahertz (THz) source array with 144 coupled oscillator elements. A novel 2-D scalable coupling topology suitable for large array formation is proposed to enable the desired coherent oscillation mode, in which a differential patch antenna can be incorporated, radiating the third harmonic for high output frequency beyond 600 GHz without a lens. High-efficiency harmonic oscillator design and optimization method are proposed for the third harmonic generation and extraction. This design is also capable of beam steering by changing the bias voltage to establish different phase gradients across the array. Finally, the fabricated chip can radiate 9.1-dBm power and 30.8-dBm lensless effective isotropic-radiated power (EIRP) at 675 GHz. The beam is steerable from −45° to 45° in the <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane, making it the highest power silicon-based THz source and highest frequency beam-steerable chip. This chip was fabricated in a 65-nm CMOS process, occupying a <inline-formula> <tex-math notation="LaTeX">$2\times1.7$ </tex-math></inline-formula> mm area. The output frequency is tunable from 643 to 689 GHz. At 675 GHz, the measured phase noise at 1-MHz offset is −90.9 dBc/Hz, and the dc-to-THz efficiency is 0.245%.
An approach to the diagnosis of lumbar disc herniation using deep learning models

Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc. Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist’s labeling and annotation, and then the deep learning models were trained using the training/validation dataset. Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH. Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.
Carnosic Acid and Carnosol: Analytical Methods for Their Determination in Plants, Foods and Biological Samples

Among the various phytochemicals, which are present in Lamiaceae plants, carnosic acid and carnosol stand out. Carnosic acid is a phenolic diterpene carrying two phenolic hydroxyl groups and a carboxyl group, while carnosol carries a lactone moiety in addition to phenolic hydroxyls. Both these phenolic diterpenes exhibit interesting biological properties, such as antioxidant, anticancer, anti-inflammatory and neuroprotective activities. In this review, we summarize the existing analytical methods for the determination of carnosic acid and carnosol, primarily in plants, but also in foods and biological samples. Due to the biological importance of carnosic acid and carnosol, a variety of analytical methods, including high-performance liquid chromatography–ultra violet (HPLC–UV), liquid chromatography–mass spectrometry (LC–MS) and capillary electrophoresis (CE), were developed for their determination. In addition, we discuss the extraction methods applied for their isolation from plants and in brief the bioactivities of these phytochemicals.
Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as “healthy” (T-score > −0.9) and “unhealthy” based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA.