Self-healing coatings are composed of anti-corrosive polymers that can recover from any damage caused to the coating film and regain their original performance. One of the methods for preparing self-healing coatings is by incorporating a healing agent stored in nanocontainers. When there is damage to the coating film, the nanocontainer ruptures due to the mechanical impact. It releases the healing agents, which form a protective film via polymerization over the damaged part, thereby protecting against corrosion. The second method for preparing self-healing coatings involves the embedment of corrosion inhibitors (healing agent) into a nanocontainer. Upon damage, the inhibitor is released into the exposed part of the film and retards corrosion reactions occurring at the defective part of the metal surface. The two components responsible for the self-healing functions are the nanocontainers and the corrosion inhibitors (polymerizable material). This article provides a detailed report on the development of several types of corrosion inhibitors and nanocontainers, their properties, and applications as self-healing coating materials, including their advantages and limitations.
Sustainable fisheries development is increasingly critical amid rising global demand for marine resources. In this context, the Indian Marine Fishery Advisories, particularly Potential Fishing Zone (PFZ) and Ocean State Forecast (OSF) Advisories, have emerged as key tools to enhance fishery practices while reducing uncertainty and risks. The Earth System Science Organization-Indian National Centre for Ocean Information Services, under the Ministry of Earth Sciences, has been providing satellite-based PFZ and OSF Advisories since 1999 and 2009, respectively. PFZ Advisories guide fishers to areas of high fish aggregation, whereas OSF services enhance safety through accurate ocean weather forecasts. These advisories are disseminated daily to the coastal fishing community across India through multiple channels. Despite demonstrable improvements in catch per unit effort and fisher incomes in many regions, significant disparities remain in access and utilization of these services. Public-private partnerships, particularly those involving non-profit organizations, have the potential to bridge these gaps by improving outreach and community capacity-building at the grassroots level. In addition, international experience shows that co-management practices can support long-term sustainability in fisheries. This study reviews the dissemination and utilization of PFZ and OSF Advisories globally, with a focus on India, and evaluates their socioeconomic and environmental impacts. It identifies barriers to access, highlights successful models, and explores future needs for inclusive and sustainable fishery development. The findings aim to inform policy frameworks that align with the Sustainable Development Goals, particularly those related to poverty reduction, food security, and marine resource sustainability.
A sediment grain on a riverside is surrounded by similar grains and is subjected to both cohesive and viscous forces. The present study considers the orientation of sediment grains based on the established truncated pyramid model and proposes an expression for the grain’s escape velocity. The escape velocity depends strongly on the inter-grain separation gap and temperature for a given water volume entrapped between two neighboring grains. This serves as a key measure of the volumetric erosion rate. A thorough comparative study was conducted, linking the escape velocity values reported in published work—where only cohesive forces were considered—with results obtained when both viscous and cohesive forces were accounted for under varying thermal conditions. Both scenarios were evaluated at a fixed liquid bridge volume and at different separation gaps, while all other parameters were kept constant. The findings revealed that the escape velocity increased relative to that reported in earlier research. In this study, the combined effect of viscous and cohesive forces results in a significant increase in the escape velocity required for a grain, indicating enhanced stability of the riverside compared to cases where only cohesive forces are considered at lower separation times. For the 1st time, temperature dependency is incorporated in the truncated pyramid model. In addition, a one-second threshold was identified, after which viscous forces and temperature no longer significantly affect grain binding.
Urban lakes in Dhaka are increasingly subjected to environmental degradation due to rapid urbanization and inadequate wastewater management. This study aims to assess and compare the water quality of three major urban lakes—Gulshan, Dhanmondi, and Hatirjheel—across four seasons—winter, pre-monsoon, monsoon, and post-monsoon—by analyzing key physicochemical parameters. In this study, data on various water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), turbidity, suspended solids, electrical conductivity (EC), chloride, and alkalinity, measured using standard methods, were referenced from the Bangladesh government’s national report on water quality. The results revealed seasonal and spatial variations in water quality, with monsoon seasons showing dilution effects while pre-monsoon values indicate peak pollution. Among the three lakes, Dhanmondi Lake demonstrated the best water quality, with average BOD and COD levels remaining within environmental quality standards and DO concentrations that support aquatic life. Conversely, Gulshan Lake was found to be the most polluted, with BOD levels reaching up to 48 mg/L, COD up to 202 mg/L, turbidity as high as 208 NTU, and DO dropping to 0.12 mg/L. Hatirjheel Lake exhibited moderate pollution levels, with elevated TDS and EC values, particularly in the post-monsoon season. The study concludes that the urban lake system of Dhaka, particularly Gulshan and Hatirjheel, is subject to immense water quality degradation; therefore, compulsory lake management and pollution prevention actions should be adopted to enhance the ecological and recreational significance of the lakes.
Artificial intelligence (AI) and the concept of Tourism 4.0 have transformative potential in advancing sustainable tourism, particularly in addressing environmental and governance challenges. This systematic literature review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines, compiles peer-reviewed studies from 2015 to 2023 to evaluate AI’s role within Tourism 4.0 in promoting sustainability. The analysis focuses on two domains: Environmental impacts, such as energy efficiency, resource conservation, and low-carbon tourism strategies, and governance impacts, including transparent certification, accountable digital platforms, and data-driven policy innovation. While AI and related smart systems demonstrate positive outcomes, critical issues, such as cost barriers, data privacy concerns, and limited long-term evaluation, remain underexplored. The review also highlights research gaps, including the scarcity of integrated frameworks and longitudinal studies. Building on these findings, recommendations for Malaysia emphasize strengthening smart infrastructure, piloting blockchain-based transparency systems, enhancing institutional coordination, and embedding energy-efficient AI solutions into governance frameworks. By situating Malaysia at a mid-evolution stage of digital tourism, the study offers practical insights into how AI and Tourism 4.0 can support environmentally sustainable and ethically governed tourism futures.
The rising issue of biomedical discharges underscores the need for sustainable alternatives, leading to emerging applications of biodegradable conducting polymers (CPs). This review aims to emphasize current progress in this research area, focusing on applications for “green” biomedical uses. Novel biomaterials are distinct from others by virtue of their electrical conductivity, while being biocompatible and biodegradable such as conventional biomaterials, making them very suitable for biomedical applications that require safe, controlled degradation within the human body. In addition, the paper draws on current progress in the synthesis of conducting and novel biomaterials, as well as in the processes for controlled degradation, and also addresses their utilization in biomedical applications within biodegradable systems. Essential details on CPs synthesis, with a focus on their emerging applications ranging from temporary biomedical implants to tissue engineering and bioresorbable biosensors, are also discussed. Finally, this review aims to address essential issues and future research for prompt clinical applications and continuous innovations in emerging applications of conducting, biodegradable biomaterials.
Particulate matter (PM) of fine size (≤2.5 μm) remains one of the most significant global environmental risk factors for early mortality and morbidity, and more than 90% of the global population currently lives in areas exceeding the World Health Organization 2021 guideline value of 5 μg/m3. This study introduces a temporally constrained transformer-based forecasting model to anticipate annual population-weighted PM2.5 exposure across 204 countries and territories between 1990 and 2020, aimed at supporting evidence-based air quality and climate policy development. The framework is based on a filtered dataset from the State of Global Air, comprising 6,323 country-year observations with harmonized exposure estimates and uncertainty bounds, allowing the model to capture long-range temporal variations and enduring heterogeneity among countries in exposure trends. A time-aware expanding-window cross-validation approach was strictly implemented to prevent information leakage and ensure realistic predictive conditions. Five-fold temporal validation demonstrates strong performance across geographical locations, with mean squared error ranging from 0.00043 to 0.00115, root mean squared error from 0.0207 to 0.0339 μg/m3, and mean absolute error from 0.0094 to 0.0193 μg/m3, with Nash-Sutcliffe efficiencies exceeding 0.95 on average. Continental-scale evaluation shows similar high accuracy in Europe and Oceania (root mean squared error <0.01 μg/m3; R2 > 0.98), while systematically higher errors are observed in Asia and Africa, which bear a higher pollution burden. The attention-weight inspection offers clear decompositions of temporal trends and country-specific patterns that drive predictions. The proposed framework is, therefore, a methodological and practical addition to transformer-based environmental forecasting and policy-relevant global health-risk assessment.
Accurate and explainable forecasting of particulate matter (PM10) is increasingly essential for managing urban air quality and protecting public health. This study proposed and evaluated a hybrid stacked deep learning architecture designed to enhance PM10 and urban air quality forecasting accuracy and to provide transparent explanations for its predictions. Using a self-designed neural network and Ridge regression (the meta-learner), PM10 prediction was accomplished based on LightGBM integration. Analysis was performed on the World Air Quality Index dataset, consisting of 1.8 million observations from 380 cities globally. To demonstrate its effectiveness, the hybrid model was benchmarked against traditional time series models (Autoregressive Integrated Moving Average [ARIMA] and Seasonal ARIMA) and machine learning models, including decision tree, extreme gradient boosting, random forest, and neural network, using the mean squared error (MSE), root MSE (RMSE), mean absolute error (MAE), and R2 metrics as evaluation metrics. Model explainability was accomplished using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations analyses. The hybrid model achieved an R2 of 0.9916, MSE of 4.90, RMSE of 2.21, and MAE of 0.992, surpassing the other models’ performances and demonstrating strong reliability. The analysis determined the seven-day PM10 lag as the most important influential predictor, while other spatial parameters contributed minimally. The model’s ability to run efficiently on general-purpose computers further ensures accessibility for resource-constrained agencies. Overall, this study demonstrates the high predictive accuracy and interpretability of the proposed hybrid framework, offering a practical and informative tool for policymakers to improve air quality and public health outcomes.