Broad Range Research Articles

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides.

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

An Exceptional Valorization of CuO Nanoparticles in Ionic Liquids as an Efficient Medium for the Electrophilic Substitution of Indole Towards the Formation of Bis(indolyl)methanes

Aim: Ionic liquids are promising green solvents with simple but unique structure-related physical properties such as negligible vapour pressure, exceptional thermal conductivity, remarkable thermal stability and their suitability and inertness towards a broad range of catalytic applications. CuO NPs have been addressed as a cost-effective and a reagent of a choice that necessitates only mild reaction conditions to offer a high yield of the desired products with exceptional selectivity in a short duration of time. Therefore, in the present work, attempts have been made to explore the catalytic potentials of CuO NPs in an ionic liquid medium to synthesize biologically important bis(indolyl)methanes. Background: Catalytic explorations of metal oxide nanoparticles in ionic liquids offers a cooperative effect that has a significant impact on the kinetics as well as on the outcome of the reaction. Therefore, such catalytic systems in the present times have seized the scientific community's interest from the perspectives of sustainable development in synthetic organic chemistry. The combination of metal oxide nanoparticles with highly tunable ionic liquids is not only used to synthesize simple organic molecules but also explored in the synthesis of complex organic molecules of high commercial and biological relevance. Objectives: The current work offers a rapid and robust protocol for synthesizing bis(indolyl)methanes via electrophilic substitution reaction between indole and various aldehydes in the presence of a CuO nanoparticles-ionic liquid system. The discussion focuses on the high tolerance of different functionalities by the catalytic system leading to the synthesis of bis(indolyl)methanes. Methods: CuO NPs have been synthesized via the co-precipitation method using ionic liquid. The applicability of metal oxide nanoparticles-IL matrix was further investigated in synthesizing bis(indolyl)methanes. Results: The FT-IR absorption below 600 cm-1 and the XRD pattern showing all the peaks in the diffraction diagram revealed the formation of CuO NPs. FESEM images show the flake-shaped morphology of CuO NPs and are found to be separated from the agglomerated clusters Conclusion: Ionic liquid-CuO NPs matrix reveals good to exceptional catalytic properties, and their advancements as a catalytic system at room temperature open new avenues for synthetic organic chemists.

Egg-free mayonnaise-type emulsions stabilized with proteins derived from the larvae of Tenebrio molitor

In view of demanding alternative protein sources to replace the conventional ones, egg-yolk free mayonnaise-type emulsions were made from protein preparations extracted from Tenebrio molitor larvae by applying alkaline extraction (ASP), alkaline extraction followed by isoelectric precipitation (AIP) and salt-assisted treatment followed by dialysis (SSP). Their droplet size, microstructure and rheological properties were compared with egg-yolk mayonnaise (CEY). All emulsions exhibited similar droplet size distribution, with the CEY-mayonnaise having a broader range. SSP showed the smallest mean droplet size acting as a more effective emulsifier. Steady-state flow properties and dynamic oscillatory experiments within the linear viscoelastic region of mayonnaise-type emulsions were performed. The higher value of G′ compared to the value of G″ indicated the elastic character of all samples. Both moduli were no-highly frequency dependent, which was in line with their solid-like nature. The thixotropic behaviour of the emulsions was evaluated by three-interval thixotropy tests revealing higher recovery rate in the case of CEY and ASP. Temperature sweeps were also performed and revealed that AIP and SSP showed less temperature dependence, indicating their lower sensitivity to temperature variations. Overall, the results of this study suggest that the protein preparations extracted from Tenebrio molitor larvae have the potential for producing egg yolk-free mayonnaise-type emulsions.

Testing the human superorganism approach tomorality.

Current theories in moral psychology do not agree about the kinds and range of offenses that people should moralize. In this study, a new approach to defining the moral domain, Human Superorganism Theory (HSoT), is presented and tested. HSoT proposes that the primary function of moral action is the suppression of cheaters in the unusually large societies recently established by our species (i.e., human 'superorganisms'). It suggests that a broad range of moral concerns exist beyond traditional notions of harm and fairness, including actions that inhibit functions such as group-level social control, physical and social structuring, reproduction, communication, signaling and memory. Roughly 80,000 respondents completed a web-based experiment hosted by the British Broadcasting Corporation, which elicited a suite of responses to characteristics of a set of 33 short scenarios representing the areas identified by the HSoT perspective. Results indicate that all 13 superorganism functions are moralized, while violations of scenarios falling outside this area (social customs and individual decisions) are not. Several hypotheses derived specifically from HSoT were also supported. Given this evidence, we believe this new approach to defining a broader moral domain has implications for fields ranging from psychology to legal theory.

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

Asymmetry and intermittency in the rheo-inertial transition to turbulence in pipe flow

Transition to turbulence in pipe has been extensively studied but is still not completely understood and even more for non-Newtonian fluids. We focus here on yield stress shear-thinning fluids and the mechanism leading to the transition in pipe, the so-called rheo-inertial transition to turbulence. An experimental setup has enabled us to identify flow regimes in a cylindrical pipe, using both flow visualizations and pressure drops measurements for a range of Reynolds numbers. We delimited the non-Newtonian specific regime in the laminar-turbulent transition triggered at a critical Reynolds number below the turbulent puffs onset. This pre-transition regime is associated with a velocity profile asymmetry in which its degree and position evolve as the Reynolds number increases. The origin for the stability of this rheo-inertial regime is discussed, as it could be due to a competition between the nonlinear contributions of rheological behavior and flow inertia. Beyond this regime, we quantified the intermittence of puff transit, revealing the delay to turbulence. We spotted for the first time a different rheo-inertial transitional behavior in the intermittency evolution vs Reynolds number, displaying a smoother transition on a broader range. Finally, the critical Reynolds numbers for different yield stresses are compared with previous works, and the novelty is the linear increase in the delay to turbulent puffs with the yield stress.

Enhanced Pinning in a Broad Angular Range by 1D-APCs With a Coherent Interface

Enhanced Pinning in a Broad Angular Range by 1D-APCs With a Coherent Interface

Statistical investigation of decayless oscillations in small-scale coronal loops observed by Solar Orbiter/EUI

Decayless kink oscillations are omnipresent in the solar atmosphere, and they are a viable candidate for coronal heating. Although there have been extensive studies of decayless oscillations in coronal loops with lengths of a few hundred megameters, the properties of these oscillations in small-scale (∼10 mm) loops are yet to be explored. In this study, we present the properties of decayless oscillations in small loops embedded in the quiet corona and coronal holes. We use high-resolution observations from the Extreme Ultraviolet Imager on board Solar Orbiter with pixel scales of 210 km and a cadence of 5 s or better. We analysed 42 oscillations in coronal loops with loop lengths varying between 3 to 23 mm. The average displacement amplitude is found to be 134 km. The oscillations period has a range of 28 to 272 s, and the velocity amplitudes range from 2.1 to 16.4 km s−1. The variation in the loop length with the period does not indicate a significant correlation. The wave mode of these waves is uncertain, and standing waves are one possibility. Our results for the coronal seismology and energy flux estimates were obtained considering standing modes. The observed kink speeds are lower than those observed in active region coronal loops. We obtain an average magnetic field value of 2.1 G. We estimated the energy flux with a broad range of 0.6–313 W m−2. Moreover, we note that short-period decayless oscillations are not prevalent in the quiet Sun and coronal holes. Our study suggests that decayless oscillations in small-scale coronal loops are unlikely to provide enough energy to heat the quiet Sun and accelerate solar wind in coronal holes.

Ten tips for effective use and quality assurance of multiple-choice questions in knowledge-based assessments.

Multiple-choice questions (MCQs) are the most popular type of items used in knowledge-based assessments in undergraduate and postgraduate healthcare education. MCQs allow assessment of candidates' knowledge on a broad range of knowledge-based learning outcomes in a single assessment. Single-best-answer (SBA) MCQs are the most versatile and commonly used format. Although writing MCQs may seem straight-forward, producing decent-quality MCQs is challenging and warrants a range of quality checks before an item is deemed suitable for inclusion in an assessment. Like all assessments, MCQ-based examinations must be aligned with the learning outcomes and learning opportunities provided to the students. This paper provides evidence-based guidance on the effective use of MCQs in student assessments, not only to make decisions regarding student progression but also to build an academic environment that promotes assessment as a driver for learning. Practical tips are provided to the readers to produce authentic MCQ items, along with appropriate pre- and post-assessment reviews, the use of standard setting and psychometric evaluation of assessments based on MCQs. Institutions need to develop an academic culture that fosters transparency, openness, equality and inclusivity. In line with contemporary educational principles, teamwork amongst teaching faculty, administrators and students is essential to establish effective learning and assessment practices.

Interacting supernovae from wide massive binary systems

Context. The features in the light curves and spectra of many Type I and Type II supernovae (SNe) can be understood by assuming an interaction of the SN ejecta with circumstellar matter (CSM) surrounding the progenitor star. This suggests that many massive stars may undergo various degrees of envelope stripping shortly before exploding, and may therefore produce a considerable diversity in their pre-explosion CSM properties. Aims. We explore a generic set of about 100 detailed massive binary evolution models in order to characterize the amount of envelope stripping and the expected CSM configurations. Methods. Our binary models were computed with the MESA stellar evolution code, considering an initial primary star mass of 12.6 M⊙ and secondaries with initial masses of between ∼12 M⊙ and ∼1.3 M⊙, and focus on initial orbital periods above ∼500 d. We compute these models up to the time of iron core collapse in the primary. Results. Our models exhibit varying degrees of stripping due to mass transfer, resulting in SN progenitor models ranging from fully stripped helium stars to stars that have not been stripped at all. We find that Roche lobe overflow often leads to incomplete stripping of the mass donor, resulting in a large variety of pre-SN envelope masses. In many of our models, the red supergiant (RSG) donor stars undergo core collapse during Roche lobe overflow, with mass transfer and therefore system mass-loss rates of up to 0.01 M⊙ yr−1 at that time. The corresponding CSM densities are similar to those inferred for Type IIn SNe, such as SN 1998S. In other cases, the mass transfer becomes unstable, leading to a common-envelope phase at such late time that the mass donor explodes before the common envelope is fully ejected or the system has merged. We argue that this may cause significant pre-SN variability, as witnessed for example in SN 2020tlf. Other models suggest a common-envelope ejection just centuries before core collapse, which may lead to the strongest interactions, as observed in superluminous Type IIn SNe, such as SN 1994W and SN 2006gy. Conclusions. Wide massive binaries exhibit properties that may not only explain the diverse envelope stripping inferred in Type Ib, IIb, IIL, and IIP SNe, but also offer a natural framework to understand a broad range of hydrogen-rich interacting SNe. On the other hand, the flash features observed in many Type IIP SNe, such as SN 2013fs, may indicate that RSG atmospheres are more extended than currently assumed; this could enhance the parameter space for wide binary interaction.

Heavy element abundances in galactic globular clusters

Context. Globular clusters are considered key objects for understanding the formation and evolution of the Milky Way. In this sense, the characterisation of their chemical and orbital parameters can provide constraints on chemical evolution models of the Galaxy. Aims. We use the heavy element abundances of globular clusters to trace their overall behaviour in the Galaxy, with the aim to analyse potential relations between the hot H-burning and s-process elements. Methods. We measured the content of Cu I and s- and r-process elements (Y II, Ba II, La II, and Eu II) in a sample of 210 giant stars in 18 galactic globular clusters from high-quality UVES spectra. These clusters span a broad metallicity range and the sample is the largest that has been uniformly analysed to date, with respect to heavy elements in globular clusters. Results. The Cu abundances did not show a considerable spread in the sample, nor any correlation with Na, indicating that the Na nucleosynthesis process does not affect the Cu abundance. Most GCs closely follow the Cu, Y, Ba, La, and Eu field stars’ distribution, revealing a similar chemical evolution. The Y abundances in mid-metallicity regime GCs (−1.10 dex < [Fe/H] < −1.80 dex) display a mildly significant correlation with the Na abundance, which ought to be further investigated. Finally, we do not find any significant difference between the n-capture abundances among GCs with either Galactic and extragalactic origins.

Flavor profile analysis of grilled lamb seasoned with classic salt, chili pepper, and cumin (Cuminum cyminum) through HS-SPME-GC-MS, HS-GC-IMS, E-nose techniques, and sensory evaluation on Sonit sheep

In this study, the volatile flavor profiles of grilled lamb seasoned with salt, chili pepper, and cumin were analyzed employing HS-SPME-GC–MS, HS-GC-IMS, E-nose, and sensory evaluation techniques. The E-nose was found effective in differentiating the samples seasoned variously. A total of 67 volatile compounds were identified by HS-SPME-GC–MS, and 59 by HS-GC-IMS. The PCA demonstrated a correlation between the seasonings and the volatile compounds, with five principal components accounting for 99.54% of the total variance. 1-octen-3-ol, 3-furanmethanol, acetic acid, and heptanal were introduced by salt; compounds like propyl acetate were correlated with chili pepper; a broader range, including ethyl 3-methylbutanoate and high concentrations of alpha-pinene, was associated with cumin. Samples seasoned with all three ingredients showed similarities to those associated with cumin, alongside unique compounds such as gamma-octalactone and alpha-pinene. Sensory evaluations by consumers indicated that the combination of these seasonings significantly enhanced the overall acceptability of the grilled lamb. Practical applicationUtilizing modern analytical techniques, this study has successfully revealed the distinct impacts of seasonings—salt, chili pepper, and cumin—on the flavor profile of grilled lamb. By providing experimental data on how each seasonings influence the flavor profile of grilled lamb prepared with Sonit sheep. The research offers theoretical foundation for the development of grilled lamb products. By conducting a thorough comparison between GC-MS and GC-IMS, this study has expanded the understanding of the distinct characteristics of these two technologies. It has also provided a clearer analysis of some flavor compounds dimers produced in GC-IMS system.

Secure Multiple-Image Transfer by Hybrid Chaos System: Encryption and Visually Meaningful Images

The secure transmission of information is one of the most important topics in the field of information technology. Considering that images contain important visual information, it is crucial to create a safe platform for image transfer. One commonly employed tool to enhance the complexity and randomness in image encryption methods is the chaos system. The logistic and sine maps are utilized in encryption algorithms but these systems have some weaknesses, notably chaotic behavior in a confined area. In this study, to address these weaknesses, a hybrid system based on the Atangana–Baleanu fractional derivative is proposed. The various tests employed to evaluate the behavior of the new system, including the NIST test, histogram analysis, Lyapunov exponent calculation, and bifurcation diagram, demonstrate the efficiency of the proposed system. Furthermore, in comparison to the logistic and sine maps, the proposed hybrid exhibits chaotic behavior over a broader range. This system is utilized to establish a secure environment for the transmission of multiple images within an encryption algorithm, subsequently concealing them within a meaningful image. Various tools employed to assess the security of the proposed algorithm, including histogram analysis, NPCR, UACI, and correlation values, indicate that the proposed hybrid system has application value in encryption.

Numerical study of TRISO particles with random size and location distribution in cuboid and cylindrical matrix: A validation for two-regime heat conduction model

During reactor operation, the precise size values or specific size distribution of tri-structural isotropic (TRISO) fuel particles randomly dispersed within the fully ceramic microencapsulated (FCM) fuel elements are often unknown. To investigate the impact of the random distribution of positions and sizes of TRISO particles within a cuboid or cylindrical matrix on the temperature distribution of fuel elements and to establish a practical heat conduction model, TRISO fuel particles with randomly distributed positions, diameters following Gaussian or uniform distributions are generated using relevant algorithms. A cuboid and a cylindrical TRISO fuel element are modeled in ANSYS, and a three-dimensional temperature field distribution of the fuel element is obtained through numerical simulation. The probability density of the maximum temperature of the fuel element is calculated and fitted. Finally, the applicability of the two-regime heat conduction model over a broader range is validated based on the simulated data. The conclusions of this study provide reference information for predicting the temperature distribution of FCM fuel elements containing TRISO particles with random size and location distribution.

3D Monte Carlo geometry inversion using gravity data

Diverse Monte Carlo methods have gained widespread use across a broad range of applications. However, the challenge of 3D Monte Carlo sampling remains due to the curse of dimensionality. To date, only a few works have been published regarding 3D Monte Carlo sampling. This study aims to develop an efficient 3D transdimensional Monte Carlo framework for reconstructing the spatial geometry of an anomalous body using gravity data. Our framework also can quantify the uncertainty of the shape of an anomalous body recovered from geophysical measurements. To improve the computational efficiency of 3D Monte Carlo sampling, we develop a sparse geometry parameterization strategy. This approach adequately approximates the shape of a complex 3D anomalous body using a set of simple geometries, such as ellipsoids. Each ellipsoid can be characterized by a few parameters, such as the centroid, axes, and orientations, significantly reducing the number of parameters to be sampled. During the sampling, we randomly perturb the number, locations, sizes, and orientations of the ellipsoids. To impose prior structural constraints from other geophysical methods, such as seismic imaging, we design a new method by placing a fixed layer oriented along the top boundary of the anomalous body. The fixed layer is then connected to the randomly sampled ellipsoids using an alpha shape, allowing us to estimate the geometry of the anomalous source body. The current work focuses on the reconstruction of salt bodies. We start with a synthetic spherical salt model and then conduct a more realistic study using a simplified 3D SEG/EAGE salt model, which has a much more complex geometry than the synthetic spherical model. Finally, we apply our method to the Galveston Island salt dome, offshore Texas. The numerical results demonstrate that our framework can effectively recover the shape of an anomalous body and quantify the uncertainty of the reconstructed geometry.

Understanding the Distributions of Aggregation Layers in Deep Neural Networks.

The process of aggregation is ubiquitous in almost all the deep nets' models. It functions as an important mechanism for consolidating deep features into a more compact representation while increasing the robustness to overfitting and providing spatial invariance in deep nets. In particular, the proximity of global aggregation layers to the output layers of DNNs means that aggregated features directly influence the performance of a deep net. A better understanding of this relationship can be obtained using information theoretic methods. However, this requires knowledge of the distributions of the activations of aggregation layers. To achieve this, we propose a novel mathematical formulation for analytically modeling the probability distributions of output values of layers involved with deep feature aggregation. An important outcome is our ability to analytically predict the Kullback-Leibler (KL)-divergence of output nodes in a DNN. We also experimentally verify our theoretical predictions against empirical observations across a broad range of different classification tasks and datasets.

Estradiol-mediated modulation of memory and of the underlying dendritic spine plasticity through the life span.

The morphophysiology of the nervous system changes and adapts in response to external environmental inputs and the experiences of individuals throughout their lives. Other changes in the organisms internal environment can also contribute to nervous system restructuring in the form of plastic changes that underlie its capacity to adapt to emerging psychophysiological conditions. These adaptive processes lead to subtle modifications of the organisms internal homeostasis which is closely related with the activity of chemical messengers, such as neurotransmitters and hormones. Hormones reach the brain through the bloodstream, where they activate specific receptors through which certain biochemical, physiological, and morphological changes take place in numerous regions. Fetal development, infancy, puberty, and adulthood are all periods of substantial hormone-mediated brain remodeling in both males and females. Adulthood, specifically, is associated with a broad range of life events, including reproductive cycles in both sexes, and pregnancy and menopause in women. Events of this kind occur concomitantly with eventual modifications in behavioral performance and, especially, in cognitive abilities like learning and memory that underlie, at least in part, plastic changes in the dendritic spines of the neuronal cells in cerebral areas involved in processing cognitive information. Estrogens form a family that consists of three molecules [17β-estradiol (E2), estrone, estriol] which are deeply involved in regulating numerous bodily functions in different stages of the life-cycle, including the modulation of cognitive performance. This review addresses the effects of E2 on the dendritic spine-mediated synaptic organization of cognitive performance throughout the life span.

Exploring the synergy between instructional design models and learning theories: A systematic literature review

To enhance education, we conducted a comprehensive investigation into integrating instructional design models (IDMs) and learning theories in this systematic literature review. We methodically selected and analyzed 25 publications from a pool of 1,102 documents using the preferred reportinfg items for systematic reviews and meta-analyses framework to guarantee a rigorous and systematic approach to literature selection. Our results demonstrate the worldwide span of study on this topic, including contributions from prestigious academic institutions and scholarly journals. This examination explores both the benefits and drawbacks of combining IDMs with learning theories. Noteworthy positives include increased student motivation, support for innovative teaching methods, and the development of complex and diverse learning environments. However, several shortcomings were observed. most notably relating to accessibility problems, evaluation difficulties, and questions about the adaptability of such integrated techniques. Our findings have implications for a broad range of stakeholders, including educators, instructional designers, and students functioning in a variety of educational contexts. The increase of learner motivation, the creation of novel pedagogical tools, the refining of teacher training programs, and the promotion of interdisciplinary learning methods are significant areas of focus. In addition, our evaluation uncovered a number of gaps in the current literature, indicating intriguing possibilities for future research. The examination of holistic learning environments, the untapped potential of integrated systems, the incorporation of educational robots into pedagogical tactics, and the refining of schema assessment approaches are notable research fields. By providing these insights, this systematic review not only adds to the current body of knowledge, but also has the potential to shape the future trajectory of educational practices, so acting as a significant resource for boosting learning outcomes in a variety of educational environments.