Heavy metal contamination is a serious issue that poses a significant threat to soil environments and human health worldwide. The rapid population growth in developing countries, together with challenging economic conditions, has led to uncontrolled urbanization. These activities have become major sources of environmental pollution, affecting soil, water, and air quality. The objective of this study was to analyze the concentration of heavy metals in the soil of Setif City. To achieve this objective, 16 soil samples were collected using a regular 3 $\times$ 3 km grid across the region. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was used to analyze these samples, and their pH , organic matter (OM), and limestone (CaCO$_3$) levels were also determined to assess their physical and chemical properties. Zinc (Zn), Copper (Cu), Nickel (Ni), and Cadmium (Cd) were selected as representative heavy metals for the study. The sixteen diffractograms obtained from powder X-ray diffraction (XRD) analysis showed the presence of calcite and quartz, along with elements such as Cd, Zn, Cu, and Ni . The results indicate that the soils in Setif City are alkaline, with pH values ranging from 8.00 to 8.47 . The average concentrations of Zn, Cu, Ni, and Cd were 407.06, 55.85, 32.21, and 0.16 mg kg$^{-1}$, respectively, in the sixteen soil samples collected from Setif City. When compared with international standards (e.g., AFNOR NF X31-101 and CEPA), Zn concentrations in several samples exceeded acceptable thresholds, indicating moderate to high levels of contamination in specific zones. This finding is supported by the geoaccumulation index (Igeo) and contamination factor (Cf), both of which identified Zn as the main pollutant of concern. Contrary to the initial assumption of no contamination, the study reveals that localized Zn accumulation may pose potential environmental risks, highlighting the need for continuous monitoring and site-specific remediation strategies

Recognition of traffic signs by drivers is essential for ensuring road safety. Recently, with the growing demand for driver assistance systems and autonomous vehicles, traffic sign recognition has become increasingly important. In this study, Spatial Transformer Networks (STN) integrated with Convolutional Neural Networks (CNN) were used to classify traffic signs. STNs minimize the effects of geometric distortions by applying affine transformations to images, thereby improving classification performance. This study focuses on adapting and optimizing an STN-based CNN model specifically for the Russian Traffic Signs Dataset (RTSD) to achieve higher classification accuracy. The proposed model was trained and tested on the RTSD. First, the proposed CNN model was trained on the RTSD-R1 and RTSD-R3 datasets, achieving accuracy rates of 89.15% and 94.3%, respectively. Then, by integrating STN into the CNN model, the proposed model was trained on the RTSD-R1 and RTSD-R3 datasets, achieving accuracy rates of 93% and 95%, respectively. These results demonstrate that incorporating STNs into the CNNs is effective in improving traffic sign classification performance.

Microplastics are widely present in aquatic environments and, due to their high hydrophobicity, can carry organic pollutants while affecting their accumulation and toxicity in organisms at different trophic levels. The ingestion of microplastics by organisms can be divided into direct and indirect ingestion. Direct ingestion refers to organisms directly consuming microplastics present in the environmental medium, while indirect ingestion can be understood as microplastics being ingested by organisms along the transfer of trophic levels. This study aimed to determine the distribution of microplastics in the surface water of the Yangtze River Estuary and the accumulation characteristics of microplastics in organisms at different trophic levels. In 2021, the study selected typical sampling stations in the Yangtze River Estuary and its adjacent waters for observation, analyzing the concentration and characteristics of microplastics in surface water and samples from nine different trophic level organisms (two types of gastropods and seven types of fish). The abundance of microplastics in the surface water samples collected from the Yangtze River Estuary was 661.2 ± 220.5 items/m$^3$. The average abundance of microplastics in the gills of organisms was 1.1 ± 0.4 items/g w.w., and in the gastrointestinal tract, it was 0.3 ± 0.1 items/g w.w. Based on the calculation of the bioconcentration factor of microplastics, we found that the bioconcentration factor of higher trophic level organisms (fish) (2.6 ± 0.5 m$^3$/kg w.w.) was significantly greater than that of gastropod organisms (0.87 ± 0.4 m$^3$/kg w.w.). In terms of feeding types, the bioconcentration factor of carnivorous fish organisms was significantly greater than that of omnivorous fish. This paper determined the trophic level of organisms through stable nitrogen isotopes ($\delta^{15}$N), and the biomagnification factor of microplastics was calculated to be 4.2 based on the linear regression equation of microplastic concentration and organism trophic level. Therefore, microplastic concentrations can be transferred along different trophic levels in the food chain, and the accumulation level of microplastics in organisms significantly increases with the rise of trophic levels, indicating the potential for biomagnification of microplastics in gastropods and fish organisms.

In the context of the digital transformation of cultural institutions, there exists an imperative for interactive AR/VR systems that leverage the capabilities of artificial intelligence for the personalized presentation of cultural content and the management of cognitive interaction scenarios. In light of the above, the purpose of this study was to develop and empirically evaluate the efficacy of an interactive AI model (CurioMind) for AR/VR experiences, which tailors information input to align with user requests and behaviors. Within the framework of the study, an experiment was conducted with two groups of participants (n = 60), which involved comparing the measures of attraction, memorization and subjective evaluation of the experience. Furthermore, NASA-TLX cognitive load assessments were conducted alongside semi-structured interviews to qualitatively evaluate interface perception and content. Participants engaging with the CurioMind model demonstrated significantly higher levels of information retention (mean score of 9.1 compared to 7.2 in the control group) and longer exposure time. Moreover, their ratings on the parameters of emotional engagement, personalization, and interface attractiveness were markedly higher. The findings substantiate the study’s hypothesis: the integration of an adaptive AI agent within AR/VR experiences augments the efficacy of informal cultural learning and elevates the quality of the user experience. The CurioMind model introduces a novel approach to digital museum storytelling based on behavioral personalization. Future research may encompass the scaling of the system to authentic museum environments, adding multimodal input (gestures, gaze).

A three-dimensional seismic response analysis of an asphalt concrete face rockfill dam constructed on a thick overburden layer at the upper reservoir of a pumped-storage power station was conducted using the nonlinear finite element method. The study focused on evaluating the seismic safety of the dam body and the seepage control system. The results indicated that, under the design seismic load, the peak dynamic displacements of the dam body in the horizontal, vertical, and axial directions were 23.87 cm, 10.44 cm, and 26.13 cm, respectively, and the peak accelerations were 2.98 m/s$^2$, 2.01 m/s$^2$, and 2.98 m/s$^2$, respectively. The maximum permanent deformations in the same directions were 18.42 cm, -61.60 cm, and -5.61 cm/18.69 cm, with a settlement ratio of 0.37%. For the asphalt concrete face slab, the peak dynamic displacements in the horizontal, vertical, and axial directions were 23.87 cm, 9.42 cm, and 24.86 cm, respectively. The maximum and minimum principal strains of the face slab after the earthquake were 1.29% and -0.74%. The maximum principal tensile strains of the geomembrane at the reservoir bottom during and after the earthquake were -1.43% and -1.50%. Under the seismic check conditions, the dynamic responses of the dam body, face slab, and geomembrane increased. Comprehensive analysis of the results shows that the seismic response patterns of the dam are consistent with the general characteristics of rockfill dams on thick overburden layers. The dynamic response of the asphalt concrete face slab around the reservoir and the geomembrane at the reservoir bottom did not exceed their respective safety thresholds, indicating that the dam exhibits high seismic safety under seismic loading.

The shift to a green economy presents substantial barriers to sustainable economic growth. A key component of promoting a sustainable and green growth trajectory is educating the public about environmental sustainability, climate change, and the green economy. The main aim of this study is to evaluate students' understanding of green economy, and environmental concerns while highlighting the contribution of education to the green transition. The study was carried out in Kosovo using a quantitative method approach in a sample from 425 students including undergraduate, master and PhD. The analysis examined in this study are Multiple Regression Analysis, Pearson Correlations, Cronbach's Alpha, Inter item correlations, and descriptive statistics using IBM SPSS program Multiple Regression results showed that students' intention to continue their education in the green economy was significantly predicted by factors like comprehension of environmental policies, confidence in comprehending global economic challenges, and belief in the significance of government policies for the green transition. Significantly, students' willingness to participate in green economy studies was inversely connected with their comprehension of the role of law in environmental issues, indicating a possible discrepancy between perceived readiness and actual participation. The findings indicate that students possess a limited understanding of the green economy and environmental concerns in Kosovo; however, they are quite enthusiastic about expanding their knowledge, particularly via bachelor’s and master’s programs focused on these topics. Considering these results, the research proposes that to enhance awareness and preparedness for the green transition, new educational programs and vocational training efforts should be established alongside targeted conferences. These initiatives are crucial for providing professionals and students with the resources necessary for a sustainable future. Proper education can have a fundamental impact on environmental protection and sustainable economic development thus policymakers and universities must collaborate to create applicable study programs and curricula that promote a stronger focus on green and sustainable education.

This study develops and validates an integrated model for evaluating passenger service quality (SQ) in Thailand’s rural railway system by embedding environmental and engineering perspectives within the RAILQUAL framework. Drawing upon SERVQUAL, Grönroos's model, and Servicescape theory, it introduces the Eco-Rail Atmosphere Quality (Eco-RAQ) construct, which incorporates sustainability attributes-greenhouse-gas reduction, waste management, traction-energy efficiency, and renewable energy efficiently-into the Rail Atmosphere Quality (RAQ) dimension. Survey data from 1,013 passengers were analyzed using covariance-based structural equation modeling (CBSEM). The final model exhibits excellent fit ($\chi^2$/df = 1.096, CFI = 1.000, RMSEA = 0.010) and explains 91.5% of variance in rail efficiency quality (REQ. RAQ demonstrates the strongest total effect on REQ ($\beta$ = 0.848, $p$ $<$ 0.001), while Eco-RAQ shows a meaningful but more modest total effect ($\beta$ = 0.257, $p$ $<$ 0.001), influencing REQ both directly and indirectly through rail perceived quality (RPQ). Validity diagnostics confirm discriminant validity (HTMT $<$ 0.85) and no substantive common-method bias. The findings advance service-quality theory by integrating sustainability cognition into the Stimulus-Organism-Response paradigm and by proposing Eco-RAQ as a socio-technical mechanism linking passenger perception with operational performance. The model offers actionable insights for achieving Sustainable Development Goals (SDGs) 9, 11, and 13 in rural rail contexts.