Inha University in Tashkent

Oil spills represent a severe environmental hazard, threatening marine and coastal ecosystems, biodiversity, and socio-economic stability. Timely and accurate detection of such incidents is critical for mitigating their ecological and economic consequences. Conventional detection techniques, including manual inspection and satellite-based observation, remain limited by high operational costs, temporal delays, and restricted spatial coverage. To overcome these limitations, this study introduces a comprehensive computer vision framework that addresses two core challenges: (i) the construction of a large-scale, high-quality synthetic oil spill dataset through mask extraction and seamless blending of oil spill regions with diverse oceanic backgrounds, and (ii) the development of a fine-tuned YOLOv11m-seg detection model trained on this enriched dataset. To further enhance the realism and spatial distinctiveness of oil spill textures, the Line Integral Convolution (LIC) is applied to estimate and visualize ocean surface flow patterns, generating coherent streamline textures that simulate the natural diffusion and transport of oil in water. The model exhibited strong generalization and precision, achieving a training accuracy exceeding IoU@0.50-0.95 to 85% over 50 epochs. Evaluation metrics confirmed its reliability, with an F1 score of 94%, precision of 94%, and recall (mAP@0.50) of 94%. These results demonstrate that the developed approach not only enhances dataset diversity but also substantially improves the accuracy and representativeness of real-time oil spill detection in marine environments.

ABSTRACT The luxury fashion and lifestyle sector is at a critical juncture, facing the dual challenge of achieving sustainability while maintaining differentiation from lower‐fashion segments. This research compiles the body of knowledge on luxury fashion, sustainability, and business models in emerging economies; pinpoints systematic and recurrent changes in the luxury fashion value chain; and creates a sustainable luxury business model that effectively incorporates emerging economies as consumer markets and value producers. Using a systematic meta‐narrative literature review approach, the study examines 644 peer‐reviewed publications on luxury, sustainability, business models, and developing economies that were published between 2000 and 2025. The luxury fashion industry's major trends and sustainability issues are identified through a descriptive study. The authors incorporate empirical insights from academic research and business interaction in growing Asian economies, guided by Schön's (1983, 1990) theory of reflective practice. The two main analytical frameworks used for business model building are value chain analysis (VCA) and stakeholder theory. The findings reveal a critical gap in existing luxury fashion literature, which predominantly positions emerging economies as low‐cost production hubs rather than strategic markets and stakeholders. The proposed business model demonstrates how stakeholder‐centric governance combined with value chain optimization can support sustainable value creation, balancing economic performance with social and environmental responsibilities. This research contributes theoretically by integrating stakeholder theory and value chain analysis into a unified framework for sustainable luxury fashion in emerging economies. Managerially, it offers strategic guidance for luxury firms seeking to innovate their business models by embedding sustainability into sourcing, production, and market development. Practically, the study emphasizes the importance of engaging emerging economies as co‐creators of value and as conscious consumers, thereby supporting long‐term resilience, legitimacy, and profitability in the global luxury fashion industry.

The eutonic points of the magnesium chlorate–water and thiourea–water binary systems were determined. Based on these data and on six internal polythermal sections of the Mg(ClO3)2–CS(NH2)2–H2O system, a polythermal phase diagram was constructed for the temperature range from –52.8 to 10.0°C. The diagram reveals the crystallization fields of ice, thiourea, and the magnesium chlorate crystal hydrates Mg(ClO3)2·6H2O, Mg(ClO3)2·12H22O, and Mg(ClO3)2·16H2O. Furthermore, for the internal section [45%[36%Mg(ClO3)2 + 9%MgCl2] + 55%H2O]–CS(NH2)2, the solution crystallization temperature, viscosity, density, pH, and refractive index were studied as functions of composition. A “composition–property” diagram was plotted based on the results.

The growth performance and feed utilization efficiency of tilapias mark aquaculture sustainability. The Oreochromis niloticus in this research was cultured under regulated aquaculture conditions, and its growth was modeled using the von Bertalanffy Growth Function (VBGF) to estimate feed conversion. The VBGF parameters were also adjusted to the actual value obtained in the 120-day rearing phase and the Weight and length of the fish were recorded at uniform intervals. The model was further scaled repeatedly to simulate empirical data with a tilapia growth curve that is a sigmoid, or S-shaped curve. Also, the dietary information regarding the intake was computed to obtain the value of Feed Conversion Ratio (FCR) which permitted the establishment of a correlation between growth and protein energy consumption. The findings of the research indicated that there is a high correlation between model predictions and growth, thereby demonstrating the effectiveness of VBGF in predicting the osmotic pressure of biomass and effective feeding plans. The model could be used to improve feed application for biomass production and profitability in aquaculture, which is generally constrained by resource limitations. As demonstrated in the model estimates, the model is quite precise in estimating the optimal harvest age and evaluating the impact of environmental/nutritional changes. In short, the model broadens the scope of application of VBGF by incorporating an effective, easy, and passive approach to simulating tilapia development, enabling more effective management of fish farming enterprises.

Timeliness evaluation of transportation is a key element of smart cities transit, as it drives operational efficiency, passenger level of satisfaction, and urban mobility management. Timeliness evaluation must be supported by effectively predicting travel times to ensure better scheduling and enhance reliability of day-to-day public transportation. However, the conventional techniques used to predict travel time accuracy often suffer from poor flexibility to model real time travel time data, incapable of capturing complex non-linear ways movements occur and limited ability when faced with irregularly spaced and sparse data. To this end, this research developed a new framework called Travel Time Predictions using Gaussian Process Regression (TTP-GPR). In combination with the probabilistic and non-parametric nature of Gaussian Process Regression (GPR), it is possible to effectively model uncertainties and complex spatiotemporal structures found in travel data. The TTP-GPR framework incorporates relevant traffic features and considers temporal changing dynamics, effectively predicting all travel times across various urban contexts. Experimental results on real transit datasets show that TTP-GPR consistently outperforms conventional transport models across four critical parameters with top scores of 5 each. It delivers superior travel time accuracy under various conditions, enhances punctuality through precise real-time predictions, provides detailed and reliable delay distribution analysis, and offers robust modeling of dwell time variability all with explicit uncertainty quantification for every travel time estimate.

The aim of this study is to examine the relationship between renewable energy consumption and CO2 emissions in a sample of 12 upper middle income transitioning countries using data from 1995 to 2022. This study relies on a number of panel data estimation methods such as OLS, fixed effects regression and cointegration techniques. Across all estimations with find that renewable energy decreases CO2 emissions even after controlling for economic growth and trade openness. In addition, the results from Dumitrescu and Hurlin (2012) suggest that there is unidirectional causality from renewable energy to CO2 emissions in our sample. This study offer a number of policy implications and avenues for future research.