Search Results (1,781)

Reliable classification of brain sequence cases is a challenging problem due to signal ambiguity and noise. Personal concentration is primarily determined by the base frequency of electroencephalogram (EEG) waves, i.e., the task rests on appropriate modelling and recognition of patterns in the corresponding human (in)activity (e.g., reading, relaxation, solving maths problems, etc.). Five underlying types of frequency (alpha, beta, gamma, delta, and theta) were considered as secondary input wave parameters in complex-valued node extensions to the prime amplitude in processing signals. Self-optimisable Artificial Intelligence (AI) methods can process, statistically analyse, and model the series-specific character and time behaviour to recognise untrained session assigned labels. This procedure involves signal pre-processing (transformation) and feature extraction to enhance the representation in time variability, eliminate uncertain cases, and reduce the unacceptable large raw format of data in detailed frequency band recording. This study focuses on improving AI modelling through brain-inspired doubled amplitude/frequency signal processing. This extended concept is based on an analogy with neural activity that generates dynamic frequency pulses as the main information holder in response to time excitations. The model is obtained in partial differential equation (PDE) solutions of evolutionary tree structure nodes—self-computational terms, using the optimal sine/cosine or rational expression. It enables the representation of periodic patterns in their intrinsic form related to primary wave characteristics. Two different machine learning methodologies were compared: the first evolutionary PDE transform and deep learning-based recurrent processing applied to all session records in bloc, assessing only one final class assessment, achieving predictive accuracy above 90% on untrained 1/3 data. The second group of regular modelling techniques evaluates each data row separately to compute its bound-label output in a time-lagged frame, reaching accuracy above 70%. An executable parametric software with a link to the public EEG data repository is available. Full article

Early exposure to scientific learning establishes a critical foundation for nurturing how natural phenomena are explored and our innate curiosities are pursued, as well as our potential to elevate the complexity of our understanding. Through the lens of Vygotsky’s social constructivist theory, this study investigates how 124 first- through fifth-grade students participated in an unstructured free play lab across two 12-week cohorts and how this influenced students’ exposure to the crosscutting concepts (CCCs) outlined in the Next Generation Science Standards (NGSS). An observational protocol was followed to gather images, which were subsequently analyzed to identify instances in which students interacted with the CCCs. The findings indicate that students engaged with all CCCs, most frequently through observations, modeling, and problem solving. The type of play influenced the extent to which students gained exposure to certain CCCs. Social interactions and moments when play caused cognitive disequilibrium further supported engagement. The results suggest that free play serves as a meaningful vehicle for increased exposure to scientific ideas in developmentally appropriate and socially mediated contexts. Leveraging play as a pedagogical tool offers opportunities to increase exposure to numerous foundational scientific skills and ideas in formal and informal settings. Full article

The Least Square Projection Twin Support Vector Machine (LSPTSVM) is an effective machine learning tool for solving classification problems. However, LSPTSVM does not account for the contribution of each sample during training, making it susceptible to outliers and noise. This susceptibility diminishes its generalization capability. To remedy this shortcoming, this paper introduces an Intuitionistic Fuzzy LSPTSVM (IFLSPTSVM). This model combines the LSPTSVM with the concept of Intuitionistic Fuzzy Numbers (IFN). In the training process of IFLSPTSVM, the importance of each training sample is gauged using an IFN-based score function that considers its geometric position and surrounding environment. Moreover, the weighted class mean, as opposed to the standard mean used in LSPTSVM, is employed in the calculation of intra-class scatter based on the intuitionistic fuzzy score of the sample. This approach effectively mitigates the impact of noise and outliers and more accurately captures the global information of the class samples. Experimental results on several real-world UCI benchmark datasets and the Case Western Reserve University rolling bearing datasets exhibit the efficacy of the proposed method. Full article

Laboratories play a critical role in engineering education by bridging the gap between theoretical knowledge and practical applications. Dedicated laboratory stands serve as essential platforms for experiential learning, enabling students to interact directly with real or simulated systems. These stands enable a deeper understanding of complex engineering concepts and, at the same time, support the creation of essential skills such as problem-solving, critical thinking, and system integration. While physical laboratory stands provide a rich learning experience, their large-scale implementation can be both technically and financially challenging. In this context, virtual laboratory environments based on virtual prototypes offer a valuable complementary approach by improving access to practical learning while providing greater flexibility and reducing implementation costs. This paper presents the development of a virtual laboratory stand implemented using Simscape and designed for use in the teaching process in mechatronics engineering. The developed virtual prototype materializes a Segway-type self-balancing mobile robot and is used as a teaching tool in the lab activity of the control systems course. The educational effectiveness of the proposed virtual laboratory stand was evaluated by comparing students’ perceptions with those of a conventional analytical-model-based laboratory activity. The collected questionnaire data were analyzed using descriptive statistics and the Mann–Whitney U test. Students using the virtual laboratory reported consistently higher ratings than those using the analytical model, with statistically significant differences observed in three questionnaire items. These findings suggest that the proposed virtual laboratory stand enhances students’ learning experience and provides a valuable complementary approach for teaching control system design in mechatronics. Full article

The development of 4D-printing technology enables the fabrication of smart structures capable of transforming in response to external stimuli, offering potential for future minimally invasive biomedical applications. This study explores a direct 4D-printing approach for producing thermally actuated polylactic acid (PLA) components, focusing on self-expanding vascular stent prototypes. Unlike conventional 4D printing, which typically requires a separate post-printing programming step (e.g., heating, mechanical loading, cooling, and unloading), direct 4D printing embeds the programming phase directly into the layer-wise printing process by controlling the adjustment of process parameters, so that components are ready for shape transformation immediately after fabrication. A systematic experimental workflow was applied by combining a problem-solving methodology with action research and structured experimentation to identify and optimize the key parameters governing shape-shifting behavior. Bilayer plate structures were fabricated via fused filament fabrication (FFF) using multiple PLA material combinations and were thermally actuated under controlled conditions to characterize the influence of printing speed, printing temperature, and infill pattern on the magnitude and directionality of deformation. Building on these results, three functional demonstrators were developed, culminating in a grid-based cylindrical prototype that achieved approximately 5.8% radial expansion upon actuation at temperatures above 80 °C. Overall, the results suggest the fundamental technological feasibility of direct 4D printing as a manufacturing route for thermally actuated components, serving as a foundational proof-of-concept for future patient-specific, self-deploying medical devices. Full article

Source identification in confined underground ventilation systems is essential for hazardous-gas monitoring, and uranium-mine radon provides a representative case in which release locations and strengths must be inferred from limited concentration measurements. This presents an underdetermined, ill-posed inverse problem whose solvability under different sparse-regression strategies and roadway configurations remains poorly understood. In this study, a computational fluid dynamics (CFD) forward model is coupled with sparse regression. The ventilation flow field and radon advection–diffusion process are solved in OpenFOAM to construct a source–sensor contribution matrix, and source recovery is formulated as a sparse linear inverse problem. Four methods—LASSO, LASSO with non-negative least-squares (NNLS) refitting, Elastic Net, and Elastic Net with NNLS refitting—are compared, and the contribution matrix is characterized by its mutual coherence, condition number, and singular-value spectrum. Numerical tests were conducted for single- and multiple-source scenarios in single-main and main–branch roadway models. The results indicate that inversion performance depends on the spatial information and local identifiability provided by the sensor configuration rather than on sensor number alone. LASSO and Elastic Net exhibited varying degrees of source-strength shrinkage or dispersion, whereas NNLS refitting reduced these effects when the first-stage support contained the dominant source candidates. In the prescribed three-source case, denser sensor coverage improved dominant-source localization and reduced the post hoc condition number of the prescribed-source submatrix, although the full-matrix condition number increased. This finding indicates improved local identifiability for the tested source combination rather than a general sensor-count effect. Because the synthetic observations and the inversion operator were derived from the same CFD response matrix, the results represent a controlled model-consistent proof of concept rather than an estimate of field-level performance. Full article

This paper examines the Talmud as a rich, enduring source of pedagogical theory and practice. Beyond its role as a canonical legal corpus, the Talmud embeds a sophisticated pedagogical model grounded on situated learning. The paper proceeds in three steps. First, it draws on chosen vocabulary to describe the teaching and learning process, identifying seven key Hebrew concepts—שִׁנּוּן (shinun, repetition), שְׁמִיעָה (shmia, attentive listening), הֲבָנָה (habana, understanding), דְרָשׁ (drash, interpretation), חִדּוּשׁ (chidush, innovation), חִנּוּךְ (chinuch, dedication), and מָסַר (masar, to transmit)—and examining their alignment with modern pedagogical theories. Second, through close readings of sugyot (thematic Talmudic units), it reconstructs core practices—chavruta (paired learning in which two learners sharpen one another through dialectical inquiry), sheilot u teshuvot (a structured problem solving technique of posing questions and answers in which meaning is co-created through sustained questioning) and chonchut (mentorship understood not as the transfer of information but as the formation of character through sustained relational encounter)—and shows similarities with contemporary learning environments such as coding playgrounds. Third, the paper organizes these concepts into a learning spiral with three intertwined goals: instruction, education, and formation. The paper argues that Talmudic pedagogy prefigures many insights of modern educational theory—including the situated learning of Lave and Wenger, the constructionism of Papert, and the dialogic pedagogy of Freire and Vygotsky—while maintaining an ethical orientation sometimes absent from secular frameworks. The Talmud, read as a living pedagogy, offers contemporary educators a model in which to learn is to become and to teach is to transform. Full article

Time-of-flight magnetic resonance angiography (TOF-MRA) is widely used for noninvasive visualization of arterial anatomy, but extracting hemodynamics like blood velocity typically requires supplementary phase-contrast scans, tagging or multi-TE images. This study proposes a novel, physics-informed computational framework to extract variable fluid velocity directly from standard TOF-MRA signal profiles. We analytically expand the approach-to-steady-state Bloch equations to include convective flow, establishing a mathematical relationship between the spatial decay of longitudinal magnetization and fluid velocity. The velocity derivation was further extended to pointwise estimation over a 1-D centerline, overcoming the limitations of constant-velocity assumptions. To validate and solve this problem, a MATLAB (R2025b) simulation framework was developed to model fluid flow in two variable-geometry flowing tube cases, i.e., continuous narrowing and focal stenosis, under synthetic scanner noise. A global inverse optimization approach utilizing Dual-Tikhonov regularization was applied to stably invert the ill-posed transit time integral, actively penalizing high-frequency numerical ringing while preserving structural curves. The computational simulations successfully recovered ground-truth point-wise velocities, tracking gradual hemodynamic accelerations and sharp stenotic jets. This theoretical framework and the example centerline TOF-MRA signal intensity provide a robust mathematical proof-of-concept that quantitative, localized functional hemodynamic metrics can be extracted from standard structural MRA imaging, establishing a foundation for advanced flow quantification without requiring additional scan time. Full article

This work presents a numerical framework for the electromagnetic modeling and inverse characterization of laser-hardened steels using 3MA (Micromagnetic Multiparameter Microstructure and Stress Analysis) non-destructive testing. The proposed methodology combines a simplified two-layer eddy current model, representing the hardened case and the softer core, with the Jiles–Atherton hysteresis model. The associated inverse problem is solved by means of a genetic algorithm, enabling the identification of depth-dependent local hysteresis parameters from measured 3MA incremental permeability signals. The Jiles–Atherton hysteresis parameters are first calibrated using bulk B-H loops. Subsequently, the coupled forward model is used to establish the relationship between these parameters and the measured incremental permeability response for different hardening depths. As a proof of concept, the framework is applied to laser-hardened specimens. The identified local hysteresis and permeability characteristics show clear correlations with both case depth and excitation conditions, demonstrating the potential of the proposed approach for physics-based, non-destructive characterization of laser-hardened layers. Full article

Teachers’ educational beliefs act as psychological frameworks that shape how they interpret educational constructs such as teaching, learning, and classroom practice. Need for cognition (NFC) is associated with motivation in learning, problem-solving, and decision-making processes. This quasi-experimental study used a non-equivalent pretest–posttest control group design to examine the associations of a philosophical inquiry-based professional development programme (P4TEduP) with in-service teachers’ NFC and educational belief subscales. The study included 28 teachers from a single private school; 13 participated in the experimental group and 15 in the control group. The P4TEduP was delivered over five days in 10 sessions and used philosophical inquiry stimuli focused on educational concepts. Data were collected using the Need for Cognition Scale and Educational Belief Scale and analysed using paired-samples t-tests, Wilcoxon signed-rank tests, and a two-stage rank-based residualised ANCOVA with HC3 heteroscedasticity-consistent standard errors. After controlling for pretest scores, no statistically significant between-group differences were found in NFC or educational belief subscales. However, NFC and progressivism increased significantly only within the experimental group. Given the small, non-randomised sample and non-significant baseline-adjusted between-group differences, the findings should be interpreted as preliminary empirical and practical evidence, not as support for programme effectiveness or a new theoretical model. Full article

Representations of engineering work in the curriculum influence students’ perceptions of valued skills and knowledge in the field. Industrial engineering attracts a diverse group of students and is well positioned to incorporate longstanding calls for comprehensive skills because of its systems-level approach to problem-solving. Examining students’ perceptions of the curriculum of industrial engineering can elucidate how the curriculum shapes diverse students’ understandings of engineering practice and their decisions to persist or leave. Using Holland’s framework of figured worlds, we examined the role of the core industrial engineering curriculum at a Midwestern university (MU) in the United States (U.S.) in shaping undergraduate students’ perceptions of valued skills and knowledge and sense of fit in engineering. Findings from a series of two interviews with 15 upper and lower division industrial engineering students indicate that students perceived the core curriculum at MU to minimally emphasize comprehensive skills, reinforcing a purely technical view of engineering. Further, students expressed a desire for course content that emphasized application of theoretical concepts and social and cultural context and impact. Findings indicate a continuing need for curricular reform incorporating comprehensive skills to engage students, align coursework with real-world engineering practice, and shape the perception of engineering work as inherently sociotechnical. Full article

Interorganizational trust constitutes a foundational pillar of relational governance that facilitates coordination and enhances organizational performance. However, recent scholarly debate suggests that an overreliance on trusted interorganizational relationships may engender unintended counterproductive consequences. Integrating Social Exchange Theory (SET) and the Knowledge-Based View (KBV) through a capability-based perspective, this study examines a capability-based mechanism through which interorganizational trust influences firm performance via the mediating role of problem-solving capability (PSC). Based on a census dataset of all 48 licensed futures brokerage firms operating under the Jakarta Futures Exchange (JFX), primary survey data gathered from Chief Executive Officers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical evidence indicates that interorganizational trust is negatively associated with both problem-solving capability and firm performance. In contrast, problem-solving capability is positively related to firm performance and likely to significantly mediate the relationship between interorganizational trust and firm performance. These findings suggest that the organizational implications of relational trust are better understood by examining the internal adaptive capabilities that transform relational resources into firm performance. From this capability-based standpoint, problem-solving capability serves as a critical internal mechanism linking external relational conditions to organizational performance. Meanwhile, the concept of capability erosion offers a plausible theoretical explanation for how excessive reliance on relational support may reduce organizations’ incentives to sustain internal problem-solving capability. Overall, this paper contributes to the relational governance literature by providing a capability-based perspective on the dark side of interorganizational trust, offering practical insights for industry executives and market regulators on balancing relational governance with internal capability development. Full article

Although radon measurements have been carried out in most countries for several decades, a rigorous methodology for assessing the reliability (as well as the effectiveness) of indoor radon testing between short-term and long-term measurements has not yet been established at either the national or international level. National regulators, lacking scientific justification, arbitrarily set the duration of indoor radon testing (from a few minutes to several months) preferring long-term measurements simply because it “seems more reliable”. To solve this fundamental regulation problem, an original (innovative) methodology is proposed based on the algorithms of the previously published Rational Method for indoor radon testing, which ensures 95% decision-making reliability within modern metrological concepts. A comparison of the reliability (and effectiveness) between short-term and long-term measurements can be made by taking into account the radon situation at both national and global levels. The obtained dependence of the mean (among 26 countries) percentage of definite (reliable) decisions vs. the duration of measurements indicates: (i) the greater effectiveness of using short-term measurements compared to long-term ones, as well as (ii) the excessive focus of national and international regulators only on instrumental uncertainty within QA/QC to the detriment of temporal (key) uncertainty in decision-making. Key conclusion: The established practice of radon regulation on both global and national levels contains a number of underlying myths, and therefore clearly needs to be rethought and updated within a rational (scientific) strategy. A vital necessity are specific experimental (field) studies to verify (clarify) the national values of the temporal (key) uncertainty of indoor radon (and thoron EEC), as well as the creation of simple and effective (inexpensive but reliable) IoT tools for large-scale measurements of indoor radon through the involvement (and at the expense) of the population. Full article

The paper addresses the problem of maritime navigation in coastal areas without access to satellite positioning data. The proposed approach relies on visual information from a monocular camera supported by map-derived coastal information. Although the operational concept assumes the use of publicly available cartographic data, the method’s preparation and evaluation require additional processing steps, including high-resolution aerial imagery, GIS-based extraction of coastal features, semantic segmentation, and trained convolutional neural network models. The problem discussed in the paper concerns, for example, Autonomous Underwater Vehicles that seek to reduce underwater dead-reckoning navigation error by surfacing and using information about what is visible around them, in a way similar to how a human would. To solve the above problem, a system was proposed that compares the camera’s representation of the observed coastline with the map representation of the area where the vehicle is most likely located. The system was validated using real-world data. The tests revealed that the information contained in a flat map is insufficient for accurate position estimation. Accuracy is also significantly affected by errors in the semantic segmentation used to extract land features from camera images, as well as by potential errors in the camera viewing angle. The achieved accuracies are sufficient for navigation away from land, but when operating close to land, the proposed system appears significantly insufficient. The paper specifies the system and reports the results. Full article

Since decades, surgery of pancreatic adenocarcinoma is confronted with two major challenges. First, the prognosis of pancreatic adenocarcinoma is the worst of all gastrointestinal malignancies, characterized by late diagnosis and aggressive tumor biology. Second, postoperative mortality and morbidity have been exceptionally high, giving pancreatic resection a reputation of being one of the most dangerous procedures. This narrative review concentrates on the progress in decreasing postoperative mortality by centralization and standardization, the exertion of extended resections for local tumor clearance and multimodal strategies to improve oncologic survival. Research regarding surgical techniques, especially robotic assistance, indicated further progress in minimizing the perioperative trauma. Multimodal treatment concepts have been implemented with adjuvant therapy in resectable and neoadjuvant regimen in borderline and locally advanced cancer. However, significant problems have to be solved. Postoperative morbidity remains high, hindering the administration of adjuvant therapy, and toxicity is an essential factor in neoadjuvant strategies. Despite neoadjuvant therapies, resection rates of locally advanced carcinoma are modest and tumor progression hinders resection of borderline and resectable carcinomas. Obviously, deficits in understanding tumor biology are a central obstacle in improving resection rates and overall survival. Hence, improvement in selection of patients for surgical resection apart from using preoperative anatomical findings is mandatory. The progression of current standards, future developments and the status of surgery in multimodal concepts to treat pancreatic adenocarcinoma are delineated using historical and recent reports, registry data, meta-analysis and randomized controlled trials. Full article