Semnan University

The rapid growth of artificial intelligence (AI) in mathematics education places new demands on teachers’ digital-pedagogical competence and their ability to use AI critically. Although research highlights both the potential and risks of AI tools, there is limited evidence on how structured instructional models help future teachers move beyond uncritical reliance on AI-generated explanations. This study examined a two-rotation AI-integrated blended learning model implemented with 94 pre-service mathematics teachers in Kazakhstan. A quasi-experimental pre–post design with a control group was utilised. The experimental group (n = 47) completed an eight-week intervention consisting of guided AI-supported construction (visualisation, modelling, micro-teaching) followed by a systematic analysis of AI outputs (error identification, human–AI comparison, modelling with real data). Quantitative analyses (t-tests, ANCOVA) and thematic analysis of reflections and artefacts revealed significant improvements in the experimental group. Digital pedagogy showed a large effect (d = 0.93), while professional skills and AI readiness demonstrated medium effects (d = 0.56). Qualitative findings indicate a shift from initial acceptance of AI outputs towards systematic verification and more informed pedagogical decision-making. Overall, the findings suggest that combining guided AI use with structured analytical reflection supports responsible and reflective AI integration in mathematics teacher education. The findings also suggest that teacher education programmes should systematically incorporate structured AI-integrated models that combine guided application with critical analysis to foster reflective, responsible, and pedagogically sound AI use.

ABSTRACT Industry 5.0 has emerged to address the limitations of Industry 4.0 by emphasizing inclusive sustainability through environmentalism, human‐centricity, and resilience. However, its development faces barriers across technological, organizational, and social dimensions. This study proposes a structured decision‐making framework to identify critical barriers, design targeted strategies, and prioritize them for implementation. Strategies are evaluated using seven criteria: impact, cost, timeframe, feasibility, risk, complexity, and acceptance. A hybrid fuzzy multi‐criteria decision‐making method, informed by systematic literature review and expert consultation, ensures reliable and balanced prioritization. The analysis identified 54 barriers and 35 strategies, highlighting the highest importance of “public–private partnership programs in driving digital transformation,” “resilience‐focused business continuity,” and “localized and regional digital infrastructure development,” respectively. Considering all criteria, the strategies “clarity on long‐term return on investment,” “resilience‐focused business continuity programs,” and “ethical AI adoption programs for smaller enterprises” received the highest implementation priority, respectively. This study makes three key contributions to the literature on Industry 5.0. First, it provides a comprehensive and structured summary of Industry 5.0 barriers, identifying 54 barriers across seven distinct dimensions. Second, it develops a systematic QFD‐based framework that directly links these barriers to strategies for overcoming them and facilitating the development of Industry 5.0. Third, it introduces a novel MCDM method called F‐WASKOR. The study provides a phased roadmap for advancing Industry 5.0 transformation and realizing inclusive sustainability goals.

Urbanization has significantly altered natural hydrological processes by increasing impervious surfaces, leading to higher surface runoff volumes and elevated flood risk. Permeable pavement systems have therefore attracted increasing attention as a sustainable solution for mitigating urban runoff. This study aims to investigate and compare the performance of sand pavement without plastic geocells and with one, two, and three layers of plastic geocells in reducing runoff volume, time to runoff, runoff coefficient, time to end runoff, peak flow rate, time to peak, and the time interval between runoff initiation and runoff termination under six different rainfall intensities and two slopes using a rainfall simulator. To achieve this goal, a total of 48 laboratory tests were conducted. The results were analysed using factorial analysis and Duncan’s multiple range test in SPSS software. In addition, empirical relationships for time to runoff, runoff coefficient, time to end runoff, and peak flow rate were developed based on rainfall intensity, slope, and the number of test treatments. Finally, performance criteria including the coefficient of determination, root mean square error, and mean absolute error were evaluated. The results indicated statistically significant differences at the 5% significance level for the dependent variables time to runoff, time to end runoff, and runoff coefficient in all cases. Moreover, variations in rainfall intensity and the combined effects of rainfall intensity, slope, and treatment configuration resulted in significant differences in peak flow rate. The cumulative runoff volume obtained from the three layer geocell treatment was consistently lower than that of the other treatments under all experimental conditions, indicating that sand pavement reinforced with three geocell layers is more effective in reducing runoff than the two-layer, one-layer, and unreinforced configurations. Increasing the number of geocell layers delayed runoff initiation and reduced both the runoff coefficient and peak flow rate. However, no definitive conclusion could be drawn regarding the influence of geocell layers on the time to end runoff.

In this study, alumina/CQD (carbon quantum dot) nanostructures are synthesized using varying concentrations of CQD solution to systematically investigate their structural, morphological, and optical characteristics. X-ray diffraction (XRD) analysis shows a gradual transition from a crystalline to an amorphous structure with increasing CQD content used through the synthesis process. For the samples with lower values of CQD content (AQD-1 and AQD-7), the calculated crystallite sizes by the Scherrer equation are 2.93 and 2.77 nm. In comparison, they cannot be calculated for the samples synthesized using higher values of CQDs (AQD-9 and AQD-13). The results indicate that the volume of the CQD solution notably influenced the nanostructure morphology and the distribution of CQDs in the produced nanostructures. Also, a notable dependence of the samples' optical properties on CQD concentration is observed. The indirect band gap energy of the nanostructures, in particular, demonstrates a systematic increase by increasing the CQD content, suggesting the tunability of the nanostructure's optical properties by adjusting the carbon concentration used in the synthesis process. The nanocomposites' specific surface area (SSA) decreased with increasing CQD concentration from 247.2 to 97.7 m2/g, suggesting partial pore blockage or aggregation induced by CQD incorporation. The synthesized nanocomposites exhibited high efficiency in the water treatment even in water containing high concentrations of copper ions (184 ppm), underscoring their potential as effective adsorbents for heavy metal remediation. These findings suggest promising prospects for developing multifunctional nanomaterials suitable for optical and environmental applications.

In this work, WS2 thin films approximately 200 nm thick were successfully synthesized on glass substrates using RF sputtering, applying deposition rates of 1 and 2 Å/s. The impact of these rates on the films’ structure, surface morphology, optical properties, and electrical characteristics was thoroughly investigated. Field emission scanning electron microscope analysis revealed a morphology transition from nanoparticles to nanosheets as the rate increased. Atomic force microscope (AFM) analysis revealed that higher deposition rates increased grain size, nanosheet formation, and surface roughness in WS2 films. X-ray diffraction and Raman results showed enhanced crystallinity and the emergence of multiple growth orientations at higher deposition rates. Optical measurements indicated reduced transmittance and bandgap in the higher-rate film, attributed to morphological and structural improvements. Hall effect results showed significantly enhanced electrical conductivity in the nanosheet-based film due to increased carrier concentration and mobility. The Urbach energy values were also extracted, showing lower disorder in the high-rate film (79 meV) compared to the low-rate sample (106 meV). These findings underscore the key role of deposition rate in tailoring WS2 thin film properties for optoelectronic applications.

We consider an extension to the Standard Model (SM) with four new fields, including scalar( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi>S</a:mi> </a:math> ), spinor ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msup> <c:mi>ψ</c:mi> <c:mrow> <c:mn>1</c:mn> <c:mo>,</c:mo> <c:mn>2</c:mn> </c:mrow> </c:msup> </c:math> ), and vector( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:msub> <e:mi>V</e:mi> <e:mi>μ</e:mi> </e:msub> </e:math> ), under a new <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:mi>U</g:mi> <g:mo stretchy="false">(</g:mo> <g:mn>1</g:mn> <g:mo stretchy="false">)</g:mo> </g:math> gauge group in the hidden sector. The scalar particle interacts with the SM Higgs particle and is an intermediary between the dark and the SM parts. Our dark matter (DM) candidate is the spinor particle. We show that the model successfully explains the relic density of the DM in the universe and evades the strong bounds from direct detection experiments while respecting the theoretical constraints and the vacuum stability conditions. In addition, we study the hierarchy problem within the Veltman approach by solving the renormalization group equations at one-loop. We demonstrate that the addition of the new fields contributes to the Veltman parameters, which, in turn, results in satisfying the Veltman conditions much lower than the Planck scale. For our DM model, we find one representative point in the viable parameter space which satisfies also the Veltman conditions at <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"> <k:mi mathvariant="normal">Λ</k:mi> <k:mo>=</k:mo> <k:mn>1</k:mn> <k:mtext> </k:mtext> <k:mtext> </k:mtext> <k:mi>TeV</k:mi> </k:math> . Therefore, the presence of the extra particle solves the fine-tuning problem of the Higgs mass.