Experimental Findings Research Articles

Finite Element Analysis of Reinforced Concrete Beams Strengthened with Hybrid Fiber Reinforced Polymer Systems using ANSYS

Background: Existing reinforced concrete (RC) structures can deteriorate over time due to aging, poor construction design, natural disasters, etc. In recent years, fiber-reinforced polymer (FRP) composite materials are becoming a preferred choice for concrete construction repair due to their durability, high strength, and corrosion resistance. This study aimed to study and analyze the properties of the constituent materials to identify any weaknesses and potential improvements. Methods: The present study investigated the effectiveness of flexural strengthening of RC beams using a hybrid grouping of glass-FRP (GFRP) and carbon-FRP (CFRP) unidirectional laminates. ANSYS finite element analysis (FE) software was used to investigate the failure modes of the beams and the stress-strain parameters. The impact of adopting two different grades of reinforcing bars in RC beam modeling was also contrasted in the study. Results: Comparisons between the finite element analysis and experimental literature results were made. Based on the test findings, it could be concluded that retrofitted beams perform better than non-retrofitted beams. According to experimental results, the HY14 sheet enhanced beam had a 188.46% higher ultimate load than the unenhanced beams. Conclusion: Comparing experimental findings to the conclusions of the numerical analysis, a maximum difference of ultimate load and deflection at mid-span of 3.40% and 4.91%, respectively, were used to assess the accuracy of the results.

The impact of object properties and scan geometry on the quality of TLS data

Object surface Properties, range, and measuring trip time are main variables affecting the positional accuracy of the computed point clouds by the terrestrial Laser Scanner (TLS). In this research, Practical experiments were carried out by Faro focus premium TLS in order to investigate how does the variation of surface roughness and its reflectivity affect the positional accuracy of the measured scanner data at different scan angles and ranges. For this purpose, different materials that have distinct surface properties were conducted (glass, steel, wood, ekoplast, and total station (TS) sheet targets). Also, to examine the impact of the surface color, three of those selected materials have been painted with RGB color and black and white colors as well. About 54 scans were recorded during the experiment as all materials were scanned at three different scanning angles of (0˚, 30˚, and 60˚) and at ranges of 5 and 20 meters. The experiment's findings reveals that, at various incident angles, smooth surfaces have a greater impact on the accuracy of the scanned objects to create 3D point clouds than do rough surfaces. Furthermore, the total RMSEs in the point clouds position that measured from surface painted with red and black colors is greeter and higher than those measured from blue, green, and white colors painted surfaces. Interestingly, the total station target had never reflects the laser beam at all incident angles and ranges for class-1 laser beams. Additionally, the intensity of various materials varies. For example, the smooth materials steel and glass have varying degrees of accuracy because of their respective characteristics of the surface.

Overview of physics results from the ADITYA-U tokamak and future experiments

The ADITYA upgrade (ADITYA-U), a medium-sized (R0=75 cm,a=25 cm) conventional tokamak facility in India, has been consistently producing experiments findings by using circular and shaped-plasmas. Recognizing the plasma parameters aligning closely with the design parameters of circular limited plasmas, ADITYA-U shifted its focus toward exploring the operational regime for experimentation on saw-tooth and MHD phenomena. Moreover, ADITYA-U has made consistent advancements toward conducting preliminary plasma shaping experiments through the activation of top and bottom divertor coils utilizing hydrogen as well as deuterium fuels. Confinement is improved by a factor of ∼1.5 in D2 plasmas when compared to H 2 plasmas of ADITYA-U. Further, ADITYA-U operations emphasize preventing disruptions and runaway electrons (REs) to ensure safe operations for future fusion devices. Significant suppression of REs has been achieved in ADITYA-U with the application of pulsed localized vertical magnetic field (LVF) perturbation, thereby establishing the technique’s independence from the tokamak device. The successful RE mitigation requires a critical threshold of LVF pulse magnitude, which is approximately 1% of the toroidal magnetic field, and a minimum duration of ∼5 ms. Apart from this, several novel findings have been achieved in the ADITYA-U experiments, including the modification of sawtooth duration through gas-puff, the emergence of MHD-induced geodesic acoustic mode-like oscillations, the propagation of fast heat pulses induced by MHD activity, the control of RE dynamics through Gas-puffs, the propagation of pinch-driven cold-pulses, the transport and core accumulations of argon impurities, the mass dependency of plasma toroidal rotation and the detection of ‘RICE’ scaling, as well as the characterization of edge plasma using wall conditioning methods, such as glow discharge cleaning using a combination of Ar-H 2 mixture, localized wall cleaning by electron cyclotron resonant plasma, and the development of machine learning-based disruption predictions, will be discussed in this paper.

Adaptive Bregman–Kaczmarz: an approach to solve linear inverse problems with independent noise exactly

We consider the block Bregman–Kaczmarz method for finite dimensional linear inverse problems. The block Bregman–Kaczmarz method uses blocks of the linear system and performs iterative steps with these blocks only. We assume a noise model that we call independent noise, i.e. each time the method performs a step for some block, one obtains a noisy sample of the respective part of the right-hand side which is contaminated with new noise that is independent of all previous steps of the method. One can view these noise models as making a fresh noisy measurement of the respective block each time it is used. In this framework, we are able to show that a well-chosen adaptive stepsize of the block Bregman–Kaczmarz method is able to converge to the exact solution of the linear inverse problem. The plain form of this adaptive stepsize relies on unknown quantities (like the Bregman distance to the solution), but we show a way how these quantities can be estimated purely from given data. We illustrate the finding in numerical experiments and confirm that these heuristic estimates lead to effective stepsizes.

Effectiveness of a suction device for containment of pathogenic aerosols and droplets.

As the global community begins recovering from the COVID-19 pandemic, the challenges due to its aftermath remain. This health crisis has highlighted challenges associated with airborne pathogens and their capacity for rapid transmission. While many solutions have emerged to tackle this challenge, very few devices exist that are inexpensive, easy to manufacture, and versatile enough for various settings. This paper presents a novel suction device designed to counteract the spread of aerosols and droplets and be cost-effective and adaptable to diverse environments. We also conducted an experimental study to evaluate the device's effectiveness using an artificial cough generator, a particle counter, and a mannequin in an isolated system. We measured droplet removal rates with simulated single and repeated cough incidents. Also, measurements were taken at four distinct areas to compare its effectiveness on direct plume versus indirect particle removal. The device reduced airborne disease transmission risk, as evidenced by its capacity to decrease the half-life of aerosol volume from 23.6 minutes to 15.6 minutes, effectively capturing aerosol-sized droplets known for their extended airborne persistence. The suction device lessened the peak total droplet volume from peak counts. At 22 minutes post peak droplet count, the count had dropped 24% without the suction device and 43% with the suction device. The experiment's findings confirm the suction device's capability to effectively remove droplets from the environment, making it a vital tool in enhancing indoor air quality. Given the sustained performance of the suction device irrespective of single or multiple cough events, this demonstrates its potential utility in reducing the risk of airborne disease transmission. 3D printing for fabrication opens the possibility of a rapid iterative design process, flexibility for different configurations, and rapid global deployment for future pandemics.

Impact of discrepant accumulations strategy on collective cooperation in multilayer networks

Understanding large-scale cooperation among related individuals has been one of the largest challenges. Since humans are in multiple social networks, the theoretical framework of multilayer networks is perfectly suited for studying this fascinating aspect of our biology. To that effect, we here study the cooperation in evolutionary game on interdependent networks. Importantly, a part of players are set to adopt Discrepant Accumulations Strategy. Players employing this mechanism not only use their payoffs in the multilayer network as the basis for the updating process as in previous experiments, but also take into account the similarities and differences in strategies across different layers. Monte Carlo simulations demonstrate that considering the similarities and differences in strategies across layers when calculating fitness can significantly enhance the cooperation level in the system. By examining the behavior of different pairing modes within cooperators and defectors, the equilibrium state can be attributed to the evolution of correlated pairing modes between interdependent networks. Our results provide a theoretical analysis of the group cooperation induced by the Discrepant Accumulations Strategy. And we also discuss potential implications of these findings for future human experiments concerning the cooperation on multilayer networks.

Wehnelt photoemission in an ultrafast electron microscope: Stability and usability

We tested and compared the stability and usability of three different cathode materials and configurations in a thermionic-based ultrafast electron microscope: (1) on-axis thermionic and photoemission from a custom 100 μm diameter LaB6 source with a graphite guard ring, (2) off-axis photoemission from the Ni aperture surface of the Wehnelt electrode, and (3) on-axis thermionic and photoemission from a custom 200 μm diameter polycrystalline Ta source. For each cathode type and configuration, including the Ni Wehnelt aperture, we illustrate how the photoelectron beam-current stability is deleteriously impacted by simultaneous cooling of the source following thermionic heating. Furthermore, we demonstrate usability via collection of parallel- and convergent-beam electron diffraction patterns and by formation of the optimum probe size. We find that usability of the off-axis Ni Wehnelt-aperture photoemission is at least comparable to on-axis LaB6 thermionic emission, as well as to on-axis photoemission [the heretofore conventional approach to ultrafast electron microscopy (UEM) in thermionic-based instruments]. However, the stability and achievable beam currents for off-axis photoemission from the Wehnelt aperture were superior to that of the other cathode types and configurations, regardless of the electron-emission mechanism. Beam-current stability for this configuration was found to be ±1% (one standard deviation from the mean) for 70 min (longest duration tested), and steady-state beam current was reached within the sampling-time resolution used here (∼1 s) for 15 pA beam currents (i.e., 460 electrons per packet for a 200 kHz repetition rate). Repeatability and robustness of the steady-state condition were also found to be within ±1% of the mean. We discuss the implications of these findings for UEM imaging and diffraction experiments, for pulsed-beam damage measurements, and for practical switching between optimum conventional TEM and UEM operation within the same instrument.

Intelligent Decision Support System in Healthcare using Machine Learning Models

Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems.

A motor task, not working memory, causes the revelation effect.

Performing a cognitive task prior to making a recognition judgment increases the probability of old responses, which is known as the revelation effect. The criterion shift account (Niewiadomski & Hockley, 2001) proposes that occupation of working memory causes the revelation effect. However, we proposed that working memory does not cause it. Two experiments were conducted to disconfirm the relationship between working memory and the revelation effect and to consider an alternative explanation that metacognition causes the effect. In Experiment 1, the revelation effect was caused by a finger movement task, which puts little or no load on working memory. In Experiment 2, a metacognitive instruction that a cognitive task would make subsequent recognition easier induced a conservative criterion shift. The finding that a simple motor task caused the revelation effect in Experiment 1 disconfirms the relationship between working memory and the revelation effect and extends the boundaries of the occurrence of the effect. The findings in Experiment 2 suggest that metacognition may be related to the occurrence of the revelation effect. This study implies a paradoxical aspect of human cognition in that metacognition, which usually makes cognition more effective and rational, may also cause an irrational phenomenon, the revelation effect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Yield Performance Study of Bina Developed Sesame Varieties under Agro-Ecological Conditions of Mymensingh District in Bangladesh

An investigation was done during the Kharif-I (Summer) season 2022 at Sadar Upazilla of Mymensingh district in Bangladesh to explore the yield and factors affecting the yield of four sesame varieties developed by the Bangladesh Institute of Nuclear Agriculture (BINA). Three replications of a Randomized Complete Block Design (RCBD) were used, where Binatil-1, Binatil-2, Binatil-3, and Binatil-4 were the varieties. The experiment's findings showed that Binatil-1 contained the tallest plant (107.00 cm), whereas Binatil-3 contained the shortest plant (79.55 cm). It was noted that Binatil-2 had the greatest number of plant-1 branches (4.95), whereas Binatil-1 had the least amount (2.14). Additionally, Binatil-3 displayed the highest number of plant-1 pods (95.65), whereas Binatil-1 provided the lowest amount (51.27). Binatil-1 had the largest pod (3.56 cm), whereas Binatil-4 had the smallest (2.57 cm). It was noted that Binatil-1 displayed the greatest count of seeds in pod-1 (78.15), while Binatil-4 displayed the least amount (61.12). The highest 1000 seed weight was obtained from Binatil-2 (3.15 g), whereas the lowest 1000 seed weight was recorded in Binatil-1 (2.95 g). The established genotype Binatil-2 had the maximum seed yield (1.42 t ha-1), whereas Binatil-1 had the least (1.27 t ha-1). Among the cultivars, Binatil-3 matured in the fewest days (87), whereas Binatil-2 required the most days (95). At Binatil-2, significant yield performance was found from the Kharif-I (summer) season experiment. A simple correlation was done where branches per plant per thousand seed weight had a significant positive correlation and pod breadth had a negative impact on seed yield for all genotypes. This experiment will be helpful for oil crop breeders and the farming community to choose promising sesame genotypes with high yield potential and future breeding stock. Jagannath University Journal of Life and Earth Sciences, 9(2): 125–132, 2023 (December)

Exploring Bimanual Haptic Feedback for Spatial Search in Virtual Reality.

Spatial search tasks are common and crucial in many Virtual Reality (VR) applications. Traditional methods to enhance the performance of spatial search often employ sensory cues such as visual, auditory, or haptic feedback. However, the design and use of bimanual haptic feedback with two VR controllers for spatial search in VR remains largely unexplored. In this work, we explored bimanual haptic feedback with various combinations of haptic properties, where four types of bimanual haptic feedback were designed, for spatial search tasks in VR. Two experiments were designed to evaluate the effectiveness of bimanual haptic feedback on spatial direction guidance and search in VR. The results from the first experiment reveal that our proposed bimanual haptic schemes significantly enhanced the recognition of spatial directions in terms of accuracy and speed compared to spatial audio feedback. The second experiment's findings suggest that the performance of bimanual haptic feedback was comparable to or even better than the visual arrow, especially in reducing the angle of head movement and enhancing searching targets behind the participants, which was supported by subjective feedback as well. Based on these findings, we have derived a set of design recommendations for spatial search using bimanual haptic feedback in VR.

Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models

Through smart cities, Intelligent Transportation Systems (ITS), the agricultural sector, and wearable devices, the Internet of Things (IoT) has revolutionized several human interests. Through the development of new cluster tasks, the Decision-Making System (DMS) of Cluster Heads (CHs), and improving the accuracy of traffic prediction and reliability of transportation, the present study intends to improve the energy depletion of IoT devices. The paper explores the subject of data flow optimization using Fuzzy Assisted Cuckoo Search Optimization (FACSO), traffic flow using Gaussian Process Regression (GPR), and CH prediction using the Stochastic Optimization Algorithm (SOA). Optimizing network lifetime while minimizing Energy Consumption (EC) is feasible through the practical application of the SOA, GPR, and FACSO models. Increasing End-to-End Delay (EED), Network Throughput (NT), and energy efficiency can be rendered feasible through a real-time DMS regarding routing employing a novel approach referred to as FACSO. This approach has enhanced the efficacy and reliability of Wireless Sensor Networks (WSN). With up to 500 nodes and an EC of 0.3451 J, the experiment's findings demonstrate that a proposed SOA-FACSO model achieves superior EED.

A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.

Mechanism of lightning damage to glass fiber composite wind turbine blades

Wind turbine blades (WTBs) are susceptible to lightning damage, resulting in significant costs for repair and replacement, which poses a considerable economic burden on wind farms. Therefore, this study investigates the mechanism of lightning damage of glass fiber composite (GFC)-WTBs to reduce the risk of such damage. The damage of GFC-WTBs caused by lightning strikes was analyzed using a numerical simulation method. The lightning pilot was simulated using high-voltage rod electrodes, and subsequently, the electric field strength and damage area on the blade surface under different conditions were measured. Additionally, a simulation model based on finite element analysis was developed to further predict and validate the experimental findings. The results reveal that the maximum electric field intensity was observed in the blade tip. Notably, the electric field intensity was found to be over 798,000 V/m when the rod electrode was positioned at a 0-degree angle with the blade tip. Further investigation revealed a non-linear and positive correlation between the damage area of GFC and lightning current amplitude, charge, and specific energy. In conclusion, the study provides a comprehensive examination of the relationship between lightning current and the damage to GFC-WTBs, as well as elucidates the mechanism of lightning damage to GFC-WTBs.

The effects of identity salience on product judgment in a domain of trade-offs

PurposeThis paper aims to investigate a condition under which identity salience effects are weakened. By examining how identity salience influences individuals’ product judgment in a domain of trade-offs, the current research demonstrates that the utilitarian value of a product is an important determinant of the effectiveness of identity salience on product judgment.Design/methodology/approachThis research consists of two experiments. In Experiment 1, the authors examined whether identity salience effects were mitigated when the level of the perceived utilitarian value of an identity-incongruent product was greater than that of an identity-congruent product. In Experiment 2, the authors examined the effectiveness of internal attribution as a moderator that strengthens identity salience effects when the perceived utilitarian value of an identity-incongruent (vs. identity-congruent) product is higher.FindingsIn Experiment 1, the authors show that when the utilitarian value of a product with an attribute congruent (vs. incongruent) with one’s salient identity is lower, individuals do not show a greater preference for the identity-congruent (vs. identity-incongruent) product, mitigating the identity salience effects. Experiment 2 demonstrates that when individuals with a salient identity attribute a decision outcome to the self, they display a greater preference for the identity-congruent product even when its utilitarian value is lower compared to that of the identity-incongruent product.Research limitations/implicationsThe research contributes to previous research examining conditions under which identity salience effects are weakened [e.g. social influence by others (Bolton and Reed, 2004); self-affirmation (Cohen et al., 2007)] by exploring the role of the utilitarian value of a product, which has not been examined yet in prior research. Also, by doing so, the current research adds to the literature on identity salience in a domain of trade-offs (Benjamin et al., 2010; Shaddy et al., 2020, 2021). Finally, this research reveals that when a decision outcome is attributed to the self, identity salience effects become greater. By finding a novel determinant of identity salience effects (i.e. internal attribution), the present research contributes to the literature that has examined factors that amplify identity salience effects [e.g. cultural relevance (Chattaraman et al., 2009); social distinctiveness (Forehand et al., 2002); different types of groups (White and Dahl, 2007)].Practical implicationsThe findings provide managerial insights on identity-based marketing by showing a condition under which identity-based marketing does not work [i.e. when the utilitarian value of an identity-congruent (vs. identity-incongruent) product is lower] and how to enhance the effectiveness of identity-based marketing by using internal attribution.Originality/valueBy exploring the role of utilitarian value, not yet examined in prior research, the present research adds to the knowledge of the conditions under which identity salience effects are weakened. Furthermore, by finding a novel determinant of identity salience effects (i.e. internal attribution), the research contributes to the literature on factors that amplify identity salience effects.

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

Sol-Gel technique, characterization and photocatalytic degradation activity of Manganese doped ZnO nanoparticles

Photocatalysis using semiconductor metal oxide stands out as a highly effective and efficient method for eliminating organic pollutants from wastewater. This study aims to assess the photocatalytic capabilities of Mn doped ZnO nanocomposites in degrading methylene blue (MB) dye under ultra-violet light exposure. This study details the synthesis of ZnO photocatalysts through a straightforward one-step sol-gel method, incorporating varying levels of Mn-doping (0%, 2% & 4%). The structural and optical attributes were examined using techniques such as powder X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS) and UV-Vis diffuse reflectance spectra. X-ray diffraction analyses verified the presence of a hexagonal wurtzite crystal structure in all synthesized samples, exhibiting a high degree of crystallinity. In addition, an investigation into the impact of Mn impurities on the photocatalytic performance of ZnO catalysts was conducted in the context of methylene blue (MB) degradation. The experimental findings revealed that the Mn doped ZnO nanoparticles produced exhibited significantly superior photocatalytic performance compared to pure ZnO when used in breaking down methylene blue under UV-light exposure. This study proposes that these Mn doped ZnO could serve as a highly effective photocatalyst for treating water contaminated with certain chemically persistent synthetic organic dyes. The improved photocatalytic capabilities of ZnO nanostructures doped with Mn were ascribed to the synergistic impact of increased surface area in ZnO nanosphere and enhanced efficiency in charge separation resulting from optimized Mn doping. A potential explanation for the heightened photocatalytic performance of Mn-doped ZnO nanostructures is proposed tentatively.

Guava fruit disease identification based on improved convolutional neural network

Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.

RDGT-GAN: Robust Distribution Generalization of Transformers for COVID-19 Fake News Detection

Social media platforms have become a vital source of information during the outbreak of the pandemic (COVID-19). The phenomena of fake information or news spread through social media have become increasingly prevalent and a powerful tool for information proliferation. Detecting fake news is crucial for the betterment of society. Existing fake news detection models focus on increasing the performance which leads to overfitting and lag generalizability. Hence, these models require training for various datasets of the same domain with significant variations in the distribution. In our work, we have addressed this overfitting issue by designing a robust distribution generalization of transformers-based generative adversarial network (RDGT-GAN) architecture, which can generalize the model for COVID-19 fake news datasets with different distributions without retraining. Based on our experimental findings, it is evident that the proposed model outperforms the current state-of-the-art (SOTA) models in terms of performance.

How Reproducible Are Manual Therapy Interventions in Trials for Low Back Pain? A Scoping Review.

OBJECTIVES: To assess the reproducibility of manual therapy interventions used in clinical trials for low back pain (LBP), and summarize knowledge gaps in assessing the reproducibility of manual therapy interventions for LBP. DESIGN: Scoping review. LITERATURE SEARCH: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CENTRAL), and Embase were searched for trials from inception through April 2023. STUDY SELECTION CRITERIA: Randomized controlled trials were included if they described the use of manual therapy to treat LBP in adults 18 to 65 years old and were accessible in English. DATA SYNTHESIS: The Consensus on Exercise Reporting Template (CERT) checklist, used for exercise reporting, was previously modified for manual therapy reporting. This 11-item modified CERT was used to extract details of manual therapy reporting in the included trials. Frequency counts were calculated to identify items most and least commonly reported. RESULTS: Of 128 trials, none reported all 11 items of the modified CERT. The most commonly reported items were the description of how the application of manual therapy was decided (n = 113, 88.3%) and a description of adjunct interventions provided (n = 82, 64.1%). The least reported items were the description of an associated home program (n = 27, 21.1%) and a detailed description of the application of manual therapy (n = 22, 17.2%). CONCLUSION: Reporting of manual therapy interventions in trials investigating LBP was poor overall, limiting the reproducibility of these treatments. Using a checklist designed explicitly for manual therapy intervention reporting may improve reproducibility of these interventions and help align clinical outcomes with experimental findings. J Orthop Sports Phys Ther 2024;54(4):1-10. Epub 29 January 2024. doi:10.2519/jospt.2024.12201.