Miracle-dz/UniFAR-FTUs huggingface dataset:
Employing Methodologies from the Field of Artificial Intelligence for Identifying Fake News

The constant development of social media platforms has made it possible for anyone to disseminate news immediately and to a large audience. Because of this, there has been a significant reduction in the amount of complexity everywhere. This indicates that it is becoming an increasingly difficult task to differentiate between authentic and fake news in the environment. In this research, we propose a realistic solution to the issues that are encountered while detecting false news by utilizing artificial intelligence (AI) and machine learning (ML) methodologies in conjunction with semantic features. This solution is aimed at identifying fake news. In order to assist us in making a decision, a number of research articles on machine learning were analyzed.
A Prospective Observational Study Analyzing the Analgesic Efficacy of Caudal Block and Nerve Stimulator-Guided Pudendal Nerve Block in Children Undergoing Hypospadias Repair

Introduction: Hypospadias repair is a common pediatric surgery. While caudal block (CB) is the most widely used procedure for analgesia, pudendal nerve block (PNB) can be utilized as an alternative. We evaluated the postoperative analgesic efficacy of CB and PNB in children undergoing hypospadias repair. Methods: In this prospective observational study, we evaluated 101 patients who received standard general anesthesia along with CB or PNB. Postoperative pain score (Face, Legs, Activity, Cry, and Consolability {FLACC} score) along with the total number of analgesic doses and the total amount of analgesic drugs consumed within 24 hours were noted. Time to first rescue analgesia, post-block penile length and midshaft circumference, surgeon satisfaction score, and postoperative complications were also evaluated. Results: Out of 101 patients observed, 50 received CB, and 51 received PNB. At 24-hour interval, the median FLACC score in CB was 6, while in the PNB, it was 4 (p<0.001). None of the patients who were given PNB received more than three doses (p<0.001), and the average consumption of analgesic drugs within 24 hours was significantly higher in CB (38.4±4.28) compared to PNB (21.7±6.33) (p<0.0001). The median time to first rescue analgesia in CB was four hours, while in patients receiving PNB, it was eight hours (p<0.001). The increase in penile volume was significantly higher with CB as compared to PNB (p<0.001). Surgeon satisfaction score was found to be better with PNB (p<0.002). Conclusion: Patients who received PNB had significantly reduced pain scores and analgesic consumption in the first 24 hours post surgery.
Effect of Glycolic Acid, Maleic Acid, and EDTA in the Removal of Smear Layer from Root Canal Dentin

Objective: To assess the efficacy of 5% Glycolic Acid (GA), 17% ethylenediaminetetraacetic acid (EDTA) and 7% maleic acid (MA), in removing the smear layer (SL). Material and Methods: For the experiment, forty single-rooted human teeth were selected. To perform the chemo ‑ mechanical preparation, the root canals were instrumented to an apical size of #30, along with simultaneous irrigation of 2.5% NaOCl. The samples were allotted to the experimental groups based on the final irrigating solution (n=10): (1) The GA group: 05%, (2) the EDTA group: 17%, (3) the MA group: 7%, and (4) the control group: Distilled water. The teeth were evaluated for the presence or absence of SL using SEM. Results: Comparing the 5% GA, 7% MA and 17% EDTA groups, no statistically significant differences were found at the coronal and middle thirds (p>0.05). However, in the apical third, MA had greater efficacy than EDTA (p=0.002) and GA (p=0.041), with a significant difference in the SL removal, while there was no significant difference between the latter (p=0.148). Conclusion: Thus, it was concluded that 7% maleic acid as a final irrigating solution is more efficacious than 17% EDTA and 5% glycolic acid in eliminating the smear layer from the apical portion of the root canal.
Long‐term nutrient load reductions and increasing lake TN : TP stoichiometry decrease phytoplankton biomass and diversity in a large shallow lake

Nutrient loading of freshwater and marine habitats has increased during the last century as a result of anthropogenic activities. From the 1980s onwards, following implementation of new policy targeting eutrophication, total phosphorus (TP) and total nitrogen (TN) loads were reduced in many European waters. Often, however, decreases in TP were stronger as compared to TN, leading to increased TN : TP ratios. Our analysis shows that the large and shallow lake IJsselmeer (the Netherlands) experienced a similar trend, whereas TN was reduced by 50%, TP was reduced by 89% between 1975 and 2018. Most of this nutrient load reduction was achieved before the year 2000, changes in nutrient concentrations in the lake became smaller afterwards, especially for TN, leading to a further increase in stoichiometric imbalance up to a yearly averaged TN : TP (molar) of 296 in 2018. The observed changes in nutrients were accompanied by a decline in total phytoplankton biomass, and slight declines in phytoplankton genus evenness and diversity. Although biomass decreases likely resulted from the overall decrease in nutrient availabilities, the reduced diversity may have resulted from the shift toward very high TN : TP ratios that indicate relatively low TP levels and enhanced competition for phosphorus. Overall, our findings demonstrate long‐term trends with decreased phytoplankton biomass and diversity following reduced nutrient concentrations and enhanced stoichiometric imbalance. Ultimately, such changes at the food web base may alter the structure and functioning of the entire aquatic food‐ web in lake IJsselmeer.
ZnO Based, Piezotronic Optical Fiber Sensors for Tracing Volatile Organic Compounds

The detection of volatile organic compounds (VOCs) is a major market and research domain, where optical sensing technologies have shown promising advances [1]. Accordingly, ZnO has been extensively used as a transduction material in the sensing of VOCs in the vapour phase, while employing both resistive [2] and optical [3] detection methodologies. ZnO also exhibits significant piezoelectric properties, which in turn have been recently used in the development of piezotronic [4] self-powered, sensing and actuating devices.
Interactions entre composition chimique et populations microbiennes de la neige : quelles sont les conséquences sur le cycle du mercure en Arctique ?

L'objectif principal de cette these est de caracteriser les interactions entre la composition chimique et la structure des communautes microbiennes dans un manteau neigeux arctique. Une attention toute particuliere est portee au mercure, dont le702 cycle biogeochimique complexe est encore mal connu. Avant toute chose, les fractions biotiques et abiotiques d'un manteau neigeux saisonnier doivent etre caracterisees. A partir d'echantillons de neige et d'eau de fonte preleves au cours d'une etude de deux mois en 2007 a Ny-Alesund, nous avons montre que la diversite des sequences est elevee et nous avons egalement examine le devenir du mercure, depuis son depot au cours des phenomenes de depletions jusqu'a son transfert lors de la fonte des neiges. Les resultats de cette campagne ont souligne la necessite d'ameliorer nos connaissances sur la speciation du mercure et ont conduit a l'elaboration d'un biocapteur mer-lux pour mesurer la fraction biodisponible de mercure, deploye lors d'une seconde campagne de deux mois au printemps 2008. En parallele a l'analyse chimique, nous avons suivi les changements dans la structure des communautes microbiennes presentes dans les echantillons de neige et d'eau de fonte. Nous avons enfin explore l'effet de la contamination au mercure sur la fonction de la communaute et demontre que le methylmercure affecte la structure des communautes ainsi que sa fonction a des concentrations beaucoup plus faibles que precedemment rapportees. Nos resultats fournissent une base pour de nouvelles etudes sur l'interaction entre la composition chimique, la presence de contaminants anthropiques et la structure des communautes microbiennes.
Effects of Supplementation of a Mycotoxin Mitigation Feed Additive in Lactating Dairy Cows Fed Fusarium Mycotoxin-Contaminated Diet for an Extended Period

Fusarium mycotoxins are inactivated by rumen flora; however, a certain amount can pass the rumen and reticulum or be converted into biological active metabolites. Limited scientific evidence is available on the impact and mitigation of Fusarium mycotoxins on dairy cows’ performance and health, particularly when cows are exposed for an extended period (more than 2 months). The available information related to these mycotoxin effects on milk cheese-making parameters is also very poor. The objective of this study was to evaluate a commercially available mycotoxin mitigation product (MMP, i.e., TOXO® HP-R, Selko, Tilburg, The Netherlands) in lactating dairy cows fed a Fusarium mycotoxin-contaminated diet, and the repercussions on the dry matter intake, milk yield, milk quality, cheese-making traits and health status of cows. The MMP contains smectite clays, yeast cell walls and antioxidants. In the study, 36 lactating Holstein cows were grouped based on the number of days of producing milk, milk yield, body condition score and those randomly assigned to specific treatments. The study ran over 2 periods (March/May–May/July 2022). In each period, six animals/treatment were considered. The experimental periods consisted of 9 days of adaptation and 54 days of exposure. The physical activity, rumination time, daily milk production and milk quality were measured. The cows were fed once daily with the same total mixed ration (TMR) composition. The experimental groups consisted of a control (CTR) diet, with a TMR with low contamination, high moisture corn (HMC), and beet pulp; a mycotoxins (MTX) diet, with a TMR with highly contaminated HMC, and beet pulp; and an MTX diet supplemented with 100 g/cow/day of the mycotoxin mitigation product (MMP). The trial has shown that the use of MMP reduced the mycotoxin’s negative effects on the milk yield and quality (protein, casein and lactose). The MTX diet had a lower milk yield and feed efficiency than the CTR and MMP HP-R diets. The MMP limited the negative effect of mycotoxin contamination on clotting parameters, mitigating the variations on some coagulation properties; however, the MMP inclusion tended to decrease the protein and apparent starch digestibility of the diet. These results provide a better understanding of mycotoxin risk on dairy cows’ performances and milk quality. The inclusion of an MMP product mitigated some negative effects of the Fusarium mycotoxin contamination during this trial. The major effects were on the milk yield and quality in both the experimental periods. These results provide better insight on the effects of mycotoxins on the performance and quality of milk, as well as the cheese-making traits. Further analyses should be carried out to evaluate MMP’s outcome on immune–metabolic responses and diet digestibility.
Syntropic counterpoints: art of AI sense or machine made context art

Project Syntropic Counterpoints has been conceptualized in the form of a series of discussions between artificial intelligence (historical persons) clones, related to topics we want to expose to AI interpretation. The project is an artist response to rising technology singularity and emerging Artificial Intelligence implementation in every aspect of everyday life which changes the social interaction landscape forever. With this project we intend to point to questions such as: Are we using AI to make humans smarter or to create a new living entity equal to us? How will this reflect on human society and its present planetary supremacy? Can we share the world and accept equality with a new AI living entity? What could be the consequences of that decision? We are also trying to point to AI limitations and to examine the cultural, creative, historical and social benefits we can gain by using AI.
The Nexus Among Mobile-App Quality (M-app-QUAL), Brand Relationship, Brand Advocacy, and Brand Equity in the Retail Industry

Abstract Consumers increasingly use mobile applications for purchasing decisions; uncovering the dimensions of service quality relevant to these apps is essential. Existing research has extensively studied online service quality in e-commerce settings, and some studies focused on mobile shopping. However, these scales do not capture all aspects relevant to mobile apps. By administering surveys, we propose and validate the dimensions of the M-app-QUAL scale using a nomological network linking the service quality dimensions to consumer outcomes. Our results demonstrate that proposed constructs have varying impacts on relational constructs- brand identification and brand attachment, which in turn, influence brand advocacy and brand equity. Moreover, theoretical and managerial contributions are detailed.
Angle Position Compensation Control Strategy for High-Speed Power Generation of Aerospace Integrated Switched Reluctance Starting/Generating System

Research on high-speed switched reluctance starting/generating (SRS/G) systems is an important direction to enhance the power generation of aircraft electric power systems. In practical engineering applications, due to the limitation of digital processor performance, the low-frequency discrete control has a great influence on the control accuracy of the commutation process at high speed, which leads to the instability of power generation voltage. In this article, an angle position compensation algorithm is presented to solve the delay of commutation control in high-speed SRS/G systems under low control frequency. By analyzing the high-speed power generation operating characteristics of the switched reluctance motor (SRM), the optimal turn-on and turn-off angles are given for the whole high-speed power generation range of 8000–20000 r/min. The simulation and experimental results show that the angle position compensation algorithm presented in this article can improve the accuracy of commutation control and the voltage quality of high-speed power generation. The pulsation amplitude of the generation voltage can be suppressed below 6 V, and the dc distortion factor can be less than 0.015, which can meet the specifications of the aircraft electric power system for the 270 V dc power supply characteristics.
Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.
SP731-C Grazing Native Warm-Season Grasses in the Mid-South

Introduction What Are Native Warm-Season Grasses? Native warm-season grasses (NWSG) are those that have grown in an area prior to human settlement and were not brought in more recently from other parts of the world. Such grasses are naturally well-adapted to the region’s soils, climate and the insects and diseases that may also occur naturally in the area. While there are many species of grasses native to the Mid-South, this publication will focus on five species that are important for forage production: big and little bluestem, indiangrass, switchgrass and eastern gamagrass. These are tall, deep-rooted perennials with excellent drought tolerance and high yields. University of Tennessee, Center for Native Grasslands Management Grazing Native Warm-Season Grasses in the Mid-South
Interaction of Val66Met BDNF and 5-HTTLPR polymorphisms with prevalence of post-earthquake 27-F PTSD in Chilean population

Post-traumatic stress (PTSD) disorder is a mental health condition that can occur after experiencing or witnessing a traumatic event. The 27-F earthquake that struck Chile in 2010 was one such event that had a significant impact on the mental health of the population. A study was conducted to investigate the prevalence of PTSD and its associated factors among survivors of this earthquake. The study was a longitudinal design, involving a sample of 913 patients aged 18 to 75 years who attended 10 Primary Care Centers in Concepción, Chile. The Composite International Diagnostic Interview (CIDI) was used to assess both depressive episodes (DE) and PTSD before and after the earthquake. The study also involved genotyping studies using saliva samples from the participants, specifically focusing on the Val66Met and 5-HTTLPR polymorphisms. Statistical analysis was performed to examine the association between different variables and the presence of PTSD. These variables included demographic factors, family history of psychiatric disorders, DE, childhood maltreatment experiences, and critical traumatic events related to the earthquake. The results showed that the incidence of post-earthquake PTSD was 11.06%. No significant differences were found between the groups of participants who developed post-earthquake PTSD regarding the Val66Met or 5-HTTLPR polymorphisms. However, a significant association was found between the concomitant diagnosis of DE and the development of post-earthquake PTSD. The presence of DE doubled the risk of developing post-earthquake PTSD. The number of traumatic events experienced also had a statistically significant association with an increased risk of developing post-earthquake PTSD. The study’s limitations include the potential interference of different DE subtypes, the complexity of quantifying the degree of earthquake exposure experienced by each individual, and events entailing social disruption, such as looting, that can profoundly influence distress. In conclusion, the study found that PTSD following the 27-F earthquake in Chile was associated with a concomitant diagnosis of DE and the number of traumatic events experienced. The study did not find a significant association between PTSD and the Val66Met or 5-HTTLPR polymorphisms. The researchers recommend that mental health professionals should prioritize the detection and treatment of concomitant depressive episodes and exposure to critical traumatic events in survivors of disasters. They also suggest that further research is needed to better understand the relationship between genetic factors and post-disaster PTSD.
Identifying Topology of Photonic Lattices with Machine and Deep Learning

Topological photonics paves the way towards efficient optical processing of information due to the particular robustness of topological states of light against various forms of disorder [1]. This robustness can be characterized by the quantized topological invariants. Thus, extracting topological invariants constitutes an important task in diagnostics of experimental samples from both the fundamental and applied perspectives. Here, we propose an alternative to traditional methods of probing the topological invariant (such as band tomography) by using instead machine learning techniques [2].
Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas.