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.

The feasibility of utilizing solar panels for electricity generation in desert environments has been critically assessed, with a particular focus on Rutba, a city located in Anbar Governorate, western Iraq. Rutba presents a promising opportunity for solar energy investment due to its desert location and the substantial amount of solar radiation it receives, estimated at approximately 3000 hours of sunshine annually. The average daily solar radiation in Rutba is approximately 9.3 hours. The city also faces a significant deficit in electrical power, with an annual demand of 209.5 GWh, while current diesel generators provide only 52.56 GWh annually, resulting in a shortfall of approximately 156.94 GWh. In light of these challenges, the implementation of solar panels is proposed as a viable solution to mitigate the electricity shortage. The potential for solar energy to meet the city’s growing energy demands has been demonstrated, positioning Rutba as an ideal candidate for solar energy development. The study concludes that, based on the area’s solar irradiance, energy needs, and geographical conditions, Rutba is a highly suitable site for large-scale solar energy projects, offering both economic and environmental benefits.

The organic rice supply chain in Indonesia, particularly in Banten Province, is characterized by high complexity and the involvement of multiple actors, which creates challenges related to transparency, traceability, and product authenticity. These issues reduce consumer trust and complicate regulatory supervision in organic farming systems. This study aims to design and evaluate a blockchain-based traceability model to enhance transparency, ensure product authenticity, and support food safety compliance in the organic rice supply chain. This research employs the Design Science Research Methodology (DSRM), encompassing problem identification, objective definition, artifact design and development, demonstration, and evaluation. Data were collected through interviews, field observations, and Focus Group Discussions (FGDs) involving organic rice supply chain actors, government regulators, and experts. The proposed model was empirically evaluated using Partial Least Squares–Structural Equation Modeling (PLS-SEM) based on responses from 220 participants. The resulting Organic Rice-Supply Chain Traceability (Organic Rice-SCT) model integrates farmers, farmer cooperatives, business actors, retailers, consumers, and government agencies within a blockchain-based system supported by quick response (QR) code technology. The findings indicate that operational excellence, cultural suitability, environmental conditions, quality assurance, and organizational resources significantly influence blockchain adoption. Conversely, data management, supply chain integration, technology maturity, and knowledge management show no significant effect. The model demonstrates its capability to improve supply chain visibility, reduce information asymmetry, strengthen regulatory oversight, and support compliance with Fresh Plant-Based Food (Pangan Segar Asal Tumbuhan, PSAT) certification. In conclusion, this study provides a validated blockchain-based traceability model that enhances transparency and trust in organic rice supply chains. Practically, the model supports stakeholders and regulators in ensuring food safety and product authenticity, while theoretically contributing to the literature on blockchain adoption in sustainable agricultural systems.