Search Results (40)

The teaching of natural sciences in complex school contexts, such as border and peripheral zones, faces challenges linked to curricular relevance, teacher preparation, and structural conditions. This study explored the professional demands of elementary teachers in northern Chile through four focus groups with 21 in-service teachers from rural and urban schools in the tri-border region. Thematic analysis revealed challenges including limited disciplinary training, scarce resources, reduced instructional time, and pressure from standardized tests. Enabling factors included the natural environment, students’ experiential knowledge, and interdisciplinary integration. Findings stress the need for situated teacher education policies responsive to territorial realities. Full article

The local rules of elementary cellular automata (ECA) with one-dimensional three-cell neighborhoods are represented by eight-bit binary numbers that encode deterministic update rules. This class of systems is also commonly referred to as the Wolfram cellular automata. These automata are widely utilized to investigate self-organization phenomena and the dynamics of complex systems. In this work, we employ numerical simulations and computational methods to investigate the asymptotic density and dynamical evolution mechanisms in Wolfram automata. We explore alternative initial conditions under which certain Wolfram rules generate similar fractal patterns over time, even when starting from a single active site. Our results reveal the relationship between the asymptotic density and the initial density of selected rules. Furthermore, we apply both supervised and unsupervised learning methods to identify the configurations associated with different Wolfram rules. The supervised learning methods effectively identify the configurations of various Wolfram rules, while unsupervised methods like principal component analysis and autoencoders can approximately cluster configurations of different Wolfram rules into distinct groups, yielding results that align well with simulated density outputs. Machine learning methods offer significant advantages in identifying different Wolfram rules, as they can effectively distinguish highly similar configurations that are challenging to differentiate manually. Full article

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

The rapid urbanization of contemporary cities represents one of the most complex challenges of the 21st century, with profound implications for the environmental, social, and economic sustainability of territories. In this context, urban regeneration emerges as a strategic approach to territorial transformation. The complexity of urban dynamics requires the adoption of innovative paradigms and systemic approaches capable of guiding decision-making processes toward eco-sustainable and resilient solutions. This research develops advanced decision support tools for urban regeneration, using the city of Potenza (Italy) as a case study. The main objective is to identify key indicators to evaluate the effectiveness of urban regeneration interventions in advance (ex-ante). The methodology develops a composite economic-financial risk index capable of providing an accurate picture of existing conditions while adapting to the territorial specificities of the analyzed area. This index, which uses the Analytic Hierarchy Process (AHP) technique to integrate elementary economic-financial indicators in order to assess the sustainability level of urban redevelopment projects, is able to synthesize complex economic variables into a single parameter of immediate comprehension, strategically guiding investments toward a sustainable urban development model. The analysis of results highlights a peculiar territorial configuration: semi-central areas present the greatest criticalities, while there is a progressive decrease in risk both toward the central core and toward peripheral and extra-urban areas. The study represents a significant methodological contribution to future urban regeneration initiatives at the local level, promoting an integrated vision of sustainable urban development for the benefit of current and future generations. Full article

As an adsorbent, biomass activated carbon is effective at the removal of a wide range of organic and inorganic pollutants; however, its synthesis remains complex. Although spent coffee grounds (SCG) could be an effective material for the production of activated carbon, achieving a sufficient surface area has proven to be difficult. Here, this study presents a preliminary investigation into the optimal manufacturing conditions of activated-carbon adsorbents derived from SCG. SCG samples were characterized according to proximate analysis, elementary analysis, surface area, and pore volumes, then subjected to various processes (i.e., drying, carbonization, and chemical activation) with different operating parameters (temperature and time). The samples were optimized as follows: (1) Stable drying of SCG with a high moisture content of approximately 65% required consumption energy of 49 kWh/kg and drying at 105 °C for 20 h. (2) By comparing changes in the consumption energy and product yield with an increasing amount of carbon fraction, it was found that drying carbonization was more suitable than hydrothermal carbonization for SCG. The optimum drying carbonization temperature for achieving attractive biochar was 500 °C for 1 h. (3) Activated carbon with the optimum surface area (3687 m²/g) and mesopore volume fraction (approximately 70%) was achieved with a chemical activator agent ratio of approximately 3 and heating at 850 °C for 1 h. Furthermore, the butane working capacity of the activated carbon was related to the mesopore volume/surface area and reached 74.5% at a mesopore volume/surface area of 0.0004, indicating its suitability for activated carbon canisters. These findings can be used to optimize the synthesis of industrial-grade activated carbon from SCG. Full article

Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain. Full article

This paper presents an overview of research results on the physical and technological features of crystal formation with an ordered distribution of vacancies. It is noted that the composition and properties of complex chalcogenide phases are not always described by the traditional concepts behind Kroeger’s theory. Model concepts are considered in which the carriers of properties in the crystalline state are not molecules, but an elementary crystalline element with a given arrangement of nodes with atoms and vacancies. It is established that the introduction of the term “quasi-element atom” of the zero group for a vacancy allows us to predict a number of compounds with an ordered distribution of vacancies. Examples of the analysis of peritectic multicomponent compounds and solid solutions based on them are given. Quasi-crystalline concepts are applicable to perovskite materials used in solar cells. It is shown that the photoluminescence of perovskite lead-cesium halides is determined by crystalline structural subunits i.e., the anionic octets. This is the reason for the improvement in the luminescent properties of colloidal quantum CsPbBr₃ dots under radiation exposure conditions. Full article

The shear band is a prominent feature within the Banbiyan hazardous rock mass located in the Wushan section of the Three Gorges Reservoir area. This band constitutes a latent risk, as the potential for the rock mass to slide along the region threatens the safety of lives and property. Presently, the understanding of the shear mechanisms and the impact of shear band size on the band–bedrock interface is incomplete. In this study, based on band–bedrock shear laboratory tests, DEM simulation is used to investigate the shear-induced coalescence mechanism, stress evolution, and crack-type characteristics of the band–bedrock interface. In addition, the shear mechanical properties of samples considering specimen size, rock step height, and step width are further studied. The results show that the crack initiation and failure crack types observed in the first rock step are predominantly tensile. In contrast, the failure cracks in the remaining rock slabs and steps are primarily characterised by shear mode in addition to other mixed modes. The stress condition experienced by the first step is very near to the position of the applied point load, whereas the stress distribution across the remaining steps shows a more complex state of compressive–tensile stress. The relationship between shear parameters and sample size is best described by a negative exponential function. The representative elementary volume (REV) for shear parameters is suggested to be a sample with a geometric size of 350 mm. Notably, the peak shear strength and shear elastic modulus demonstrate a progressive increase with the rise in rock step height, with the amplifications reaching 91.37% and 115.83%, respectively. However, the residual strength exhibits an initial decline followed by a gradual ascent with increasing rock step height, with the amplitude of reduction and subsequent amplification being 23.73% and 116.94%, respectively. Additionally, a narrower rock step width is found to diminish the shear parameter values, which then tend to stabilise within a certain range as the step width increases. Full article

Synthetic aperture interferometry (SAI) is a signal processing technique that mixes the signals collected by pairs of elementary antennas to obtain high-resolution images with the aid of a computer. This note aims at studying the effects of the distance between the synthetic aperture interferometer and an observed scene with respect to the size of the antenna array onto the imaging capabilities of the instrument. Far-field conditions and near-field ones are compared from an algebraic perspective with the aid of simulations conducted at microwave frequencies with the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) mission. Although in both cases the signals kept by pairs of elementary antennas are cross-correlated to obtain complex visibilities, there are several differences that deserve attention at the modeling level, as well as at the imaging one. These particularities are clearly identified, and they are all taken into account in this study: near-field imaging is investigated with spherical waves, without neglecting any terms, whereas far-field imaging approximation is considered with plane waves according to the Van–Citter Zernike theorem. From an algebraic point of view, although the corresponding modeling matrices are both rank-deficient, we explain why the singular value distributions of these matrices are different. It is also shown how the angular synthesized point-spread function of the antenna array, whose shape varies with the distance to the instrument, can be helpful for estimating the boundary between the Fresnel region and the Fraunhofer one. Finally, whatever the region concerned by the aperture synthesis operation, it is shown that the imaging capabilities and the performances in the near-field and far-field regions are almost the same, provided the appropriate modeling matrix is taken into account. Full article

Most rule induction algorithms generate rules with simple logical conditions based on equality or inequality relations. This feature limits their ability to discover complex dependencies that may exist in data. This article presents an extension to the sequential covering rule induction algorithm that allows it to generate complex and M-of-N conditions within the premises of rules. The proposed methodology uncovers complex patterns in data that are not adequately expressed by rules with simple conditions. The novel two-phase approach efficiently generates M-of-N conditions by analysing frequent sets in previously induced simple and complex rule conditions. The presented method allows rule induction for classification, regression and survival problems. Extensive experiments on various public datasets show that the proposed method often leads to more concise rulesets compared to those using only simple conditions. Importantly, the inclusion of complex conditions and M-of-N conditions has no statistically significant negative impact on the predictive ability of the ruleset. Experimental results and a ready-to-use implementation are available in the GitHub repository. The proposed algorithm can potentially serve as a valuable tool for knowledge discovery and facilitate the interpretation of rule-based models by making them more concise. Full article

Contemporary mind and brain sciences provide theories and data that seem to confirm a hypothesis about human nature that we might formulate as follows. Human life is conditioned by a need that is no less important than elementary biological needs (such as survival and reproduction) or universal forms of social competition: the need to build and, indeed, defend a subjective identity whose solidity and clarity are the foundation of our intra- and inter-personal equilibrium and therefore of psychological well-being and mental health. In this article, distancing ourselves from a neo-Cartesian position still prevalent in the philosophy of mind and approaching instead the outcomes of contemporary cognitive sciences, we sketch the complex interweaving of the cognitive, emotional, and affective elements that are constitutive of subjective identity, with a focus on the role played in self-identity construction by Theory-of-Mind abilities. We will suggest that, at every stage of self-construction, individuals engage in processes of understanding others that have a largely innate basis. In this perspective, a mature self-awareness is somewhat secondary to the knowledge of others, an evolutionarily refined acquisition primarily serving as a defense mechanism. Full article

Plasma-driven science is defined as the artificial control of physical plasma-driven phenomena based on complex interactions between nonequilibrium open systems. Recently, peculiar phenomena related to physical plasma have been discovered in plasma boundary regions, either naturally or artificially. Because laboratory plasma can be produced under nominal conditions around atmospheric pressure and room temperature, phenomena related to the interaction of plasma with liquid solutions and living organisms at the plasma boundaries are emerging. Currently, the relationships between these complex interactions should be solved using science-based data-driven approaches; these approaches require a reliable and comprehensive database of dynamic changes in the chemical networks of elementary reactions. Consequently, the elucidation of the mechanisms governing plasma-driven phenomena and the discovery of the latent actions behind these plasma-driven phenomena will be realized through plasma-driven science. Full article

Investigation of the change rate for contaminant parameters is important to characterize dense non-aqueous phase liquid (DNAPL) transport and distribution in groundwater systems. In this study, four experiments of perchloroethylene (PCE) migration are conducted in two-dimensional (2D) sandboxes to characterize change rates of PCE saturation (S_o) and PCE–water interfacial area (A_OW) under different conditions of salinity, surface active agent, and heterogeneity. Associated representative elementary volume (REV) of the change rate of S_o (S_o rate) and change rate of A_OW (A_OW rate) is derived over the long-term transport process through light transmission techniques. REV of S_o rate (SR-REV) and REV of A_OW rate (AR-REV) are estimated based on the relative gradient error (${\epsilon }_g^i$). Regression analysis is applied to investigate the regularity, and a model based on a back-propagation (BP) neural network is built to simulate and predict the frequencies of SR-REV and AR-REV. Experimental results indicated the salinity, surface active agent, and heterogeneity are important factors that affect the S_o rate, A_OW rate, SR-REV, and AR-REV of the PCE plume in porous media. The first moment of the PCE plume along the vertical direction is decreased under conditions of high salinity, surface active agent, and heterogeneity, while these factors have different effects on the second moment of the PCE plume. Compared with the salinity and surface active agent, heterogeneity has the greatest effect on the GTP, the distributions of the S_o rate and A_OW rate along the depth, and d_M, d_I. For SR-REV, the standard deviation is increased by the salinity, surface active agent, and heterogeneity. Simultaneously, the salinity and heterogeneity lead to lower values of the mean value of SR-REV, while the surface active agent increases the mean value of SR-REV. However, the mean and standard deviation of AR-REV have no apparent difference under different experimental conditions. These findings reveal the complexity of PCE transport and scale effect in the groundwater system, which have important significance in improving our understanding of DNAPL transport regularity and promoting associated prediction. Full article

The purpose of this work is to provide applications of real, complex, and functional analysis to moment, interpolation, functional equations, and optimization problems. Firstly, the existence of the unique solution for a two-dimensional full Markov moment problem is characterized on the upper half-plane. The issue of the unknown form of nonnegative polynomials on $\mathbb{R}\times {\mathbb{R}}_{+}$ in terms of sums of squares is solved using polynomial approximation by special nonnegative polynomials, which are expressible in terms of sums of squares. The main new element is the proof of Theorem 1, based only on measure theory and on a previous approximation-type result. Secondly, the previous construction of a polynomial solution is completed for an interpolation problem with a finite number of moment conditions, pointing out a method of determining the coefficients of the solution in terms of the given moments. Here, one uses methods of symmetric matrix theory. Thirdly, a functional equation having nontrivial solution (defined implicitly) and a consequence are discussed. Inequalities, the implicit function theorem, and elements of holomorphic functions theory are applied. Fourthly, the constrained optimization of the modulus of some elementary functions of one complex variable is studied. The primary aim of this work is to point out the importance of symmetry in the areas mentioned above. Full article

The basic idea of the RNA world as an early step towards life relies on a molecular evolution process based on self-replicating RNA strands. It is probably the oldest and most convincing model for efficient prebiotic evolution. Obviously, the functionality of RNA sequences depends on order (i.e., the definition of their sequence) as well as on complexity (i.e., the length of their sequence). Order and complexity seem to be crucial parameters in the course of RNA evolution. In the following, an attempt is made to define these parameters and to identify characteristic mechanisms of their development. Using a general RNA world scenario including the free monomer units, the sequential order is defined based on statistical thermodynamics. The complexity, on the other hand, is determined by the size of a minimal algorithm fully describing the system. Under these conditions, a diagonal line in an order/complexity-diagram represents the progress of molecular evolution. Elementary steps such as repeated random polymerization and selection follow characteristic pathways and finally add up to a state of high system functionality. Furthermore, the model yields a thermodynamic perspective on molecular evolution, as the development of a defined polymer sequence has a distinct influence on the entropy of the overall system. Full article