Emerson (Sweden)

Introduction This study examines the integration of digital tools in early childhood education and explores how parents and educators manage children’s screen time in urban Lahore, Pakistan. Methods A mixed-methods design was employed. Quantitative data from 30 educators and 30 parents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Qualitative data from 20 semi-structured interviews were analyzed using thematic analysis. Results Socio-economic status (SES) (β = 0.42, p = 0.002), perceived benefits of digital tools (PBDT) (β = 0.55, p < 0.001), and frequency of digital tool usage (FDTU) (β = 0.35, p = 0.007) significantly predicted screen time management (STM), explaining 52% of its variance (R 2 = 0.52). Qualitative findings revealed themes of enhanced engagement, improved learning outcomes, socio-economic disparities, limited teacher training, and structured management strategies such as co-viewing and time limits. Discussion Digital tools are perceived as valuable educational resources but require structured, developmentally appropriate management. Socio-economic disparities and limited institutional support remain key challenges, underscoring the need for equitable access and targeted professional development.

ABSTRACT In this study, the performance of BaZr(S 0.5 Se 0.5 ) 3 ‐based thin film solar cell with the configuration Au/BaZr(S 0.5 Se 0.5 ) 3 /ZnS/ZnO/ZnO:Al was numerically investigated using SCAPS‐1D simulation. The effects of BaZr(S 0.5 Se 0.5 ) 3 layer thickness, ZnS layer thickness, acceptor density, defect density, back contact work function, and operating temperature on the PV parameters were thoroughly analyzed. Increasing the BaZr(S 0.5 Se 0.5 ) 3 absorber thickness from 100 nm to 3 µm enhanced device performance, while saturation beyond 3 µm indicated the practical optimum. The ZnS layer exhibited the best performance at 20 nm, with thicker layers slightly reducing J sc due to increased transport resistance. Variation in the acceptor density from 10 12 to 10 18 cm −3 enhanced the parameters, while slightly decreasing J sc , reflecting stronger built‐in fields and enhanced charge separation. The cell performance remained stable for defect densities up to 10 14 cm −3 , but higher densities significantly degraded efficiency due to increased recombination. Optimizing the back contact work function revealed selenium (5.9 eV) as the most effective contact, yielding power conversion efficiency (PCE) = 24.49%, V oc = 1.2617 V, J sc = 21.81 mA/cm 2 , and FF = 88.98%. Temperature analysis indicated that 300 K provides the maximum performance, with higher temperatures leading to reduced V oc , J sc , and PCE. Comparison of initial and optimized JV and quantum efficiency curves demonstrated substantial improvements in both electrical and optical response. The results highlight that careful engineering of absorber and buffer layers, doping, defect control, and contact selection can substantially enhance the performance of chalcogenide–perovskite solar cells, offering a promising route for stable, lead‐free photovoltaics.

Selecting the proper solar inverter architecture is critical for microgrid performance, reliability, and cost. Traditional design processes are linear and inflexible, making late-stage changes expensive and risky, especially as site loads and renewable technologies evolve rapidly. This paper presents a digital twin-based methodology—implemented in MATLAB/Simulink—wherein the behavioral aspects of existing inverter assets are modelled and simulated using real site load profiles. A custom MATLAB App enables scenario-based technoeconomic analysis, including fuel savings and ROI. Simulations for a rural agricultural microgrid show that the digital twin approach can recommend the optimal number of MPPTs, battery configuration, and generator sizing, achieving a surplus of 60% reduction in annual diesel consumption and a payback period under three years. This workflow accelerates innovation, reduces risk, and supports the deployment of affordable, sustainable energy systems.

Multilayer metals, metal oxides, and polymers are widely used for various applications in industry, including optics, gas/vapor sensors, and automotives. For these applications, ultrashort pulse lasers can be customized to optimize the selective coupling and processing of a specific layer. This study investigated and optimized ultraviolet nanosecond and picosecond laser-material interactions for the precision cutting of multilayer electrodes. Cutting quality and process efficiency were evaluated against industrial manufacturing requirements. A range of laser parameters were studied using a 7 ns and a 10 ps, 355 nm laser system.

This study addresses the application of Instructional Design (ID) in mathematics teaching, highlighting its relevance for the construction of more organized, interactive, and meaningful pedagogical practices. Based on an analysis of the target audience's needs and the use of the ADDIE model, ID allows for the systematic planning, development, implementation, and evaluation of educational strategies, promoting student learning. The study discusses the advantages of this model, such as the organization of the pedagogical process, adaptation to different student demands, the integration of active methodologies, and the possibility of continuous teaching evaluation. At the same time, limitations are presented, including the need for time and resources for planning, the risk of excessive systematization, and dependence on student engagement. The bibliographic research carried out provided the theoretical basis for the study, supporting the understanding of the importance of ID in the school context. It is concluded that the application of Instructional Design contributes significantly to making mathematics teaching more efficient, dynamic, and connected to the reality of students, promoting the construction of critical knowledge and the improvement of pedagogical practices.

This study investigated a journalistic learning approach in a high school English class within a civic media literacies framework toward civic intentionality. Informed by social-cognitive theory, this study focused on learning if and how the student experience developed (a) motivational factors for student writing, including self-efficacy, belonging, writing anxiety, agentic engagement, metacognitive self-regulation, flow in writing, and intrinsic writing motivation, and (b) cognitive factors for writing, including critical thinking and digital media literacy. The sequential mixed method study used correlations, pre- to post-program change analysis, and the triangulation of qualitative thematic analyses of student focus group and teacher interview data. Results supported most of the expected relationships, suggesting the experience likely contributed to a positive shift in motivational and cognitive factors for writing. Qualitative data highlighted the most impactful parts of the journalistic learning experience, such as interviewing real-world experts for their stories, that increased students’ interest and motivation in writing about real-world issues. The mixed method results build an integrated narrative of students’ agentic, cognitive, and metacognitive development in journalistic skills, reinforcing the importance of interest-driven learning opportunities in writing and autonomy-support in skill development, such as in writing, that brings anxiety to many students.

Federated Learning (FL) has become one of the paradigms shifting towards machine learning, allowing a number of edge devices to jointly train models without exchanging raw data. This can be used to guarantee privacy of the data, less data communication overhead, and real-time decision-making under edge computing conditions. The paper discusses federated learning to secure edge computing, focusing on architecture, communication models, aggregation, and security. The experimental and theoretical studies show that FL is capable of reaching model accuracy that is similar to the centralized learning and reducing privacy threats and adversarial risks. The problems, including the heterogeneity of data, and the lack of computing power and communication efficiency, are discussed critically. The research paper ends with the suggestions on how to improve FL implementation in the actual edge networks, focusing on secure aggregation protocols, differential privacy, and client selection adaption strategies.

Neuro-Symbolic Artificial Intelligence (AI) is indeed a fascinating domain, merging the structured reasoning of symbolic methods with the learning capabilities of neural networks. Its long-standing history reflects its significance in advancing AI towards achieving more robust and interpretable solutions. Neuro-symbolic AI is such an exciting and transformative field, as it combines the structured reasoning of symbolic AI with the adaptability and learning capabilities of neural networks. Your summary elegantly captures the breadth and depth of this growing discipline. The focus on representation, learning, reasoning, and decision-making is particularly critical, as these features define the capabilities of neuro-symbolic systems; Combines structured knowledge with data-driven insights. Merges symbolic frameworks with neural networks for adaptive systems. Implements robust symbolic logic for explainable outcomes. Guides systems in making informed and ethical choices. Neural- symbolic computation stands out as a compelling framework for bridging symbolic reasoning with the adaptive strengths of neural networks. Its foundation in cognitive models of reasoning, learning, and language offers a computational lens to explore and replicate human-like intelligence. Establishing a robust basis by combining logic-based symbolic systems and neural-based connectionist models to capture both structured reasoning and pattern recognition. Highlighting practical systems like cognitive computational tools that integrate machine learning and reasoning. These systems' impact ranges from biomedical applications (e.g., computational biology) to problem-solving in fault diagnosis and software verification. The need to address interpretability, scalability, and adaptability while ensuring the systems align with human cognitive processes.

Ice build-up on wind turbine blades is a significant issue, leading to operational risks and reduced efficiency. Traditional detection methods, such as visual inspection, power curve analysis or specialised sensors, are often slow, inefficient, or costly. This paper proposes an approach using 0-dimensional persistence homology from topological data analysis (TDA) on tower and blade vibration spectra to extract key features representing the lifespan of the sub-level sets to formulate a clearer supervised learning problem. Persistence diagrams are embedded in persistence images and rank functions, enabling ice detection through convolutional neural networks (CNN), and functional principal component analysis (FPCA) with support vector machines (SVM). This approach shows promise for reducing equipment and maintenance costs, leading to more efficient blade monitoring and maintenance processes.

A research investigation uses Chinese provincial data from 2000 until 2024 to assess the environmental resource productivity (ERP) effects involving economic growth and trade openness alongside foreign direct investment (FDI) and urbanization. A composite measure for environmental resource productivity emerges through application of principal component analysis (PCA) among three key factors which are energy efficiency and material productivity combined with carbon emissions. Analysis using PARDL models demonstrates that investments from FDI together with GDP increases show a negative effect on ERP thus indicating that economic growth and foreign capital do not necessarily support better environmental outcomes. Both urban development patterns and increased trading links show positive relationships with ERP because they promote sustainable resource utilization methods. The co-integration test shows Shanghai along with Beijing Jiangsu Zhejiang Sichuan Henan Shandong follow a shape towards lasting balance however Guangdong moves apart demonstrating poor integration of its environmental and economic structures. FDI requires specific policy interventions which will help sustainability and support economic growth results that incorporate efficient resource usage. Policy makers need to establish tight environmental risk evaluation tools for FDI projects while adopting environmentally friendly trade policies and smart city development methods to create more effective ERP. Special policies must be designed to achieve economic realignment through industrial restructuring and regulatory improvements because Guangdong Province demonstrates distinctive patterns. This research delivers quantitative data to assist authorities in making choices about economic development versus environmental preservation so they can create sustainable development plans for all Chinese provinces.